Content:
Presentation type:
AS – Atmospheric Sciences

EGU24-11717 | Orals | MAL11-AS | Vilhelm Bjerknes Medal Lecture

Discovering global-scale processes in the marine atmosphere 

Lucy Carpenter, Anna Callaghan, Rosie Chance, Mat Evans, James Lee, Katie Read, Matthew Rowlinson, Marvin Shaw, Tomas Sherwen, Simone Andersen, Liselotte Tinel, and John Plane

Measurements in the remote unpolluted atmosphere have tremendous power to reveal processes that are happening on a global scale.   In the marine atmosphere where nitrogen oxide (NOx) levels are very low,  the photochemical loss rate of tropospheric ozone dominates over production, allowing loss processes to be sensitively explored.   We showed that bromine and iodine emitted from open-ocean marine sources initiate important global-scale catalytic ozone-destroying cycles and found that the deposition of ozone and subsequent reactions at the sea surface are a substantial pathway for production of volatile iodine.   Production of ozone in the remote atmosphere is predominantly regulated by the abundance of NOx, which also exerts substantial control over the hydroxyl radical (OH), the most important oxidant in the atmosphere.  It is now emerging that NOx regeneration pathways, namely the photolysis of particulate nitrate, could provide the dominant source of NOx to the marine atmosphere.  This has significant implications for our understanding of the chemistry of the remote troposphere.  This presentation discusses advances made in understanding these important, predominantly natural, cycles and their impacts on the atmosphere.

How to cite: Carpenter, L., Callaghan, A., Chance, R., Evans, M., Lee, J., Read, K., Rowlinson, M., Shaw, M., Sherwen, T., Andersen, S., Tinel, L., and Plane, J.: Discovering global-scale processes in the marine atmosphere, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11717, https://doi.org/10.5194/egusphere-egu24-11717, 2024.

EGU24-21254 | ECS | Orals | MAL11-AS | Arne Richter Award for Outstanding ECS Lecture

Weathering the STORM: Challenges and opportunities in tropical cyclone risk research  

Nadia Bloemendaal

Tropical cyclones (TCs), also referred to as hurricanes or typhoons, are amongst the deadliest and costliest natural hazards, affecting people, economies, and the environment in coastal areas around the globe when they make landfall. TCs are projected to become more intense in a warming climate, enhancing the risks associated with their wind speeds, precipitation and storm surges. It is therefore crucial to minimize future loss of life and by performing accurate TC risk assessments for coastal areas. Calculating TC risk at a global scale, however, has proven to be difficult, given the limited temporal and spatial information on landfalling TCs around much of the global coastline, and how this is going to change under climate change.

To overcome these limitations, we developed a novel approach to calculate TC risk under present and future climate conditions using the Synthetic Tropical cyclOne geneRation Model (STORM). STORM is a fully statistical model that can take any input dataset and statistically resamples this to an equivalent of 10,000 years of TC activity under the same climate condition. The resulting publicly available STORM dataset contains of enough TC activity in any coastal region of interest to adequately calculate TC probabilities and risk from. Furthermore, the STORM algorithm has been expanded with a future-climate module, enabling globally consistent local-scale assessments of (changes in) TC risk. This presentation will discuss the challenges and opportunities in using such synthetic datasets, particularly in the light of improving our understanding of TC risk. 

How to cite: Bloemendaal, N.: Weathering the STORM: Challenges and opportunities in tropical cyclone risk research , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21254, https://doi.org/10.5194/egusphere-egu24-21254, 2024.

AS1 – Meteorology

EGU24-857 | ECS | Posters on site | AS1.1

Improving extreme rainfall forecasting for a flood prone region: A hybrid modelling approach 

Athira Krishnankutty Nair and Sarmistha Singh

Numerical weather prediction models are utilized to forecast extreme rainfall events at fine resolutions; however, these models possess inherent errors due to the parameterization and discretization of differential equations, which diminishes simulation accuracy. Recent advancements in machine learning methods indicate their potential capability to significantly enhance forecast results. In this study, multiple extreme rainfall events for the Pamba river basin during the Indian Summer Monsoon Period spanning 2-4 days were forecasted using the WRF model. Pamba, one of the flood-prone basins in southern states of India (Kerala), experiences severe flood events annually. While numerous studies have been conducted to simulate the Kerala flood of 2018, those demonstrating the application of high-resolution rainfall data for the Pamba river basin remain unexplored. Therefore, in this study, we simulated multiple storm events during the period from 2015 to 2018 using the WRF model at a high resolution (1 km * 1 km) and a temporal resolution of a one-hour interval. The WRF-simulated rainfall dataset was further post-processed using various machine learning algorithms, including Random Forest, Support Vector Machine, and extreme gradient boost (XGBoost), to reduce bias in the hourly forecast of extreme rainfall events. Several cross-validation training and testing procedures were carried out using various algorithms, and the forecasted and predicted results were compared with ERA5 hourly data of 10*10 km resolution. Results indicated that XGBoost, with hyperparameter tunings, substantially reduced the Root Mean Square Error (RMSE); it was able to reduce the RMSE by up to 50% across the river basin. This hybrid model will provide a more accurate forecast of hourly extreme rainfall during the Indian Summer Monsoon Period for Pamba river basin, with high resolution essential for flood forecasting and warning.

How to cite: Krishnankutty Nair, A. and Singh, S.: Improving extreme rainfall forecasting for a flood prone region: A hybrid modelling approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-857, https://doi.org/10.5194/egusphere-egu24-857, 2024.

EGU24-1043 | ECS | Posters on site | AS1.1

Development of a mathematical model for the determination of the atmospheric boundary layer height using artificial intelligence 

Sebastián Estrada and Olga Lucia Quintero Montoya

The Atmospheric Boundary Layer Height (ABLH) is a fundamental parameter for many meteorological applications and climate change assessment and evaluation. A large number of methods for ABLH determination have been proposed; however, there is no sufficiently reliable and feasible method for this purpose. The rise of intelligence-based mathematical models for feature determination in data space has allowed their application to solve problems similar to ABLH determination. This article describes the development of a mathematical model based on artificial intelligence for ABLH determination, in which an introductory analysis of the data space from the point of view of machine learning, unsupervised, and supervised methods is presented. The methods explored are the mountain method, subtractive clustering, and the classic K-means and its soft counterpart, Fuzzy C-means. Furthermore, an analysis was conducted to determine which similarity function—whether Euclidean, Manhattan, Mahalanobis, or Cosine—best fits for ABLH estimation in each unsupervised method. For classification in a supervised fashion, the best suitable models, among others, are support vector machines and decision trees. Different internal metrics (Silhouette Index, Calinski-Harabasz score) and external metrics (root mean square error and adjusted Rand score), with a reference made by means of visual inspection by an expert, were used to evaluate the methods. The unsupervised mountain method with the Manhattan similarity function proved to be the most feasible, as it is a non-stochastic method, its computation time is reduced, and it does not require an ABLH reference. The data used was extracted from several sources: 83 days of quasi-continuous LIDAR data with 23,000 data points located at Brest, France, measured with a MiniMPL from the Meteo France LIDAR network, were used. An ABLH reference from a radiosonde adjacent to the site of the LIDAR system was used. The references range from October to December 2018. The root mean square error achieved for the whole dataset was 600 m for the mountain method. The presented method is shown to be effective for various atmospheric situations, regardless of their complexity.

How to cite: Estrada, S. and Quintero Montoya, O. L.: Development of a mathematical model for the determination of the atmospheric boundary layer height using artificial intelligence, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1043, https://doi.org/10.5194/egusphere-egu24-1043, 2024.

EGU24-2375 | Orals | AS1.1

Horizontally Explicit Vertically Implicit (HEVI) Time-Integrators for a Non-Hydrostatic Whole Atmosphere Models 

James F. Kelly, Francis X. Giraldo, P. Alex Reinecke, Felipe Alves, Cory A. Barton, and Stephen D. Eckermann

The U.S. Navy is building a coupled thermosphere-ionosphere prediction system.  As part of this project, we are developing a new dynamical core (DyCore) extending from the ground to the exobase (~500 km).  The DyCore must be able to handle large variations in both temperature and composition, which motivates a new Horizontally Explicit Vertically Implicit (HEVI) time integrator.  Unlike traditional linear Implicit-EXplicit (IMEX) methods commonly used in numerical weather prediction (NWP), HEVI does not require a fixed reference state.  Our DyCore combines HEVI with a Specific Internal Energy Equation (SIEE) and a Spectral Element Method (SEM) spatial discretization to form a robust, whole-atmosphere model for the neutral atmosphere.  We present results for two test cases using the proposed DyCore: an idealized heating/cooling test extending into the middle thermosphere and a perturbation experiment yielding nonhydrostatic baroclinic instability. The idealized heating/cooling test, which is compared to corresponding results from the hydrostatic Navy Global Environmental Model (NAVGEM), demonstrates that HEVI is more robust than traditional linear IMEX methods.  The baroclinic instability test shows that HEVI, when combined with a banded lower-upper (LU) direct solve, is efficient and allows a large timestep.  These numerical results suggest that our HEVI-enabled DyCore is a good candidate for the proposed thermosphere-ionosphere prediction system.

This work was funded by the Office of Naval Research Marine Meteorology and Space Weather program.

How to cite: Kelly, J. F., Giraldo, F. X., Reinecke, P. A., Alves, F., Barton, C. A., and Eckermann, S. D.: Horizontally Explicit Vertically Implicit (HEVI) Time-Integrators for a Non-Hydrostatic Whole Atmosphere Models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2375, https://doi.org/10.5194/egusphere-egu24-2375, 2024.

EGU24-2897 | Orals | AS1.1

Postprocessing East African rainfall forecasts using a generative machine learning model 

Bobby Antonio, Andrew McRae, Dave MacLeod, Fenwick Cooper, John Marsham, Laurence Aitchison, Tim Palmer, and Peter Watson

Existing weather models are known to have poor skill at forecasting rainfall over East Africa, where there are regular threats of drought and floods. Improved precipitation forecasts could reduce the effects of these extreme weather events and provide significant socioeconomic benefits to the region. We present a novel machine learning based method to improve precipitation forecasts in East Africa, using postprocessing based on a conditional generative adversarial network (cGAN). This addresses the challenge of realistically representing tropical rainfall in this region, where convection dominates and is poorly simulated in conventional global forecast models. We postprocess hourly forecasts made by the European Centre for Medium-Range Weather Forecasts Integrated Forecast System at 6-18h lead times, at 0.1o resolution. We combine the cGAN predictions with a novel neighbourhood version of quantile mapping, to integrate the strengths of both machine learning and conventional postprocessing. Our results indicate that the cGAN substantially improves the diurnal cycle of rainfall, and improves rainfall predictions up to the 99.9th percentile of rainfall. This improvement persists when evaluating against the 2018 March-May season, which had extremely high rainfall, indicating that the approach has some ability to generalise to more extreme conditions. We explore the potential for the cGAN to produce probabilistic forecasts and find that the spread of this ensemble broadly reflects the predictability of the observations, but is also characterised by a mixture of under- and over-dispersion. Overall our results demonstrate how the strengths of machine learning and conventional postprocessing methods can be combined, and illuminate what benefits machine learning approaches can bring to this region.

How to cite: Antonio, B., McRae, A., MacLeod, D., Cooper, F., Marsham, J., Aitchison, L., Palmer, T., and Watson, P.: Postprocessing East African rainfall forecasts using a generative machine learning model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2897, https://doi.org/10.5194/egusphere-egu24-2897, 2024.

The UFS-R2O Project, which began in July 2020 as a five-year plan with deliverables for the first three years funded, has made significant progress in developing the medium-range and sub-seasonal to seasonal (MRW/S2S) predictions, a regional, high-resolution hourly-updating and convection-allowing ensemble system for prediction of short range severe weather (CAM/SRW), and a Hurricane Application developing a very high-resolution Hurricane Analysis and Forecast System (HAFS) with storm following moving nests.  The Project is organized with Application Teams and Development Teams interacting with each other to reflect the cross-cutting nature of the UFS components and infrastructure. It  fostered successful collaborations between the National Centers for Environmental Prediction (NCEP) Environmental Modeling Center, several NOAA research labs, the National Center for Atmospheric Research (NCAR), the Naval Research Lab (NRL), and multiple universities and cooperative institutes.  Most sIgnificant outcomes of the project thus far are the implementation of the HAFSv1 ahead of the schedule, and the development of a six-way global coupled (atmosphere/ ocean/ land/ sea-ice/ wave/ aerosol) modeling system, both within the UFS framework, major accomplishments from the community modeling perspective.  

The UFS-R2O Project has entered into its second phase (2023-2024), albeit with reduced funding, to continue the momentum built during the first phase.  While the first three years of the project were focused on engineering and infrastructure, Phase II is primarily targeting systematic testing and evaluation of the prototype UFS configurations for selecting the candidates for potential transition to operations in the next few years.  In addition, Phase II of the project includes a new Seasonal Forecast System (SFS) Application Tean established to develop SFS v1 that will replace the legacy Climate Forecast System (CFSv2) currently in operations since 2011.

This presentation describes the outcomes of the UFS R2O Project for the first three years, and highlights the progress and plans for the Phase II.

How to cite: Tallapragada, V., Whitaker, J., and Kinter, J.: NOAA's Unified Forecast System Research to Operations (UFS R2O) Project Phase II - Accomplishments, Progress and Future Plans, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2930, https://doi.org/10.5194/egusphere-egu24-2930, 2024.

EGU24-3462 | ECS | Posters on site | AS1.1

Assimilation of HY-2D scatterometer wind field data in CMA-GFS 

Chuanwen Wei, Wei Han, Yan Liu, Hao Hu, Huijuan Lu, Hongyi Xiao, and Dan Wang

Satellite sea surface wind fields can compensate for the shortcomings of conventional observation data, thereby improving the forecasting skills of global medium-range numerical weather models. China successfully launched the HY2D satellite carrying a Ku band microwave scatterometer (HSCAT) on May 19, 2021. It can provide a large amount of high-quality sea surface wind field data for numerical forecasting models. In order to test the potential application of HY2D sea surface wind field data in the Global Assimilation Forecasting System of the China Meteorological Administration (CMA-GFS). A three-step study was conducted, with the first step being the timeliness evaluation of HY2D wind, followed by the quality evaluation of HY2D wind using ERA5 and buoy data, and finally assessment of impacts of the HY2D wind assimilation on the analyses and forecasts. Two sets of assimilation experiments were conducted: the control experiment without HY2D wind (CTRL) and sensitivity experiment with HY2D wind based on a new quality control scheme (HY2D-FlagQC). The experimental period is from March 1, 2023 to April 1, 2023. The results show that the timeliness of HY2D wind field obtained through National Satellite Ocean Application Service (NSOAS) has been improved by about 20% compared to Koninklijk Nederlands Meteorologisch Instituut (KNMI). But the timeliness fluctuation is relatively large in terms of time and space. The root mean square error of HY2D wind field is less than 2m/s. After assimilating the HY2D wind, the analysis errors of the wind fields in the lower-middle troposphere of the tropics and the southern hemisphere (SH) are significantly reduced. Furthermore, assimilating the HY2D wind data can improve the forecast skill of wind, geopotential height, and temperature in the troposphere of the tropics and SH. 

How to cite: Wei, C., Han, W., Liu, Y., Hu, H., Lu, H., Xiao, H., and Wang, D.: Assimilation of HY-2D scatterometer wind field data in CMA-GFS, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3462, https://doi.org/10.5194/egusphere-egu24-3462, 2024.

EGU24-3686 | Orals | AS1.1

Improving Visibility Forecasting during Haze-fog Processes in Shanghai and Eastern China: the Significance of Aerosol and Hydrometeor 

Ying Xie, Xiaofeng Wang, Yanqing Gao, Baode Chen, Ronald van der A, Jieying Ding, Wen Gu, Min Zhou, and Hongli Wang

Aerosols and droplets are the main factors of visibility reduction by scattering and absorbing light. For visibility predictions in operational NWP models, hydrometeors are often considered to be the dominant factor in the total extinction, whereas aerosol effects are usually simplified or omitted in models developed for relatively clean regions. In China, also many NWP studies related to visibility forecasting during haze-fog processes have been conducted, primarily focusing on severely polluted periods before 2018. These studies often employed visibility parameterizations that considered either aerosol extinction alone or hydrometeor extinction alone. Therefore, the significance of incorporating both aerosol and hydrometeor extinction into visibility forecasting during haze-fog processes remains uncertain, particularly under recent rapid changes in aerosol concentration, composition, and hygroscopicity in China.

In this study, we first use the 3-D meteorology fields from the Shanghai Meteorological Service WRF-ADAS Real-Time Modeling System (WARMS) to drive the Community Multiscale Air Quality (CMAQ) model. In this version, CMAQ is used in an off-line mode and visibility is diagnosed by combining extinctions due to hydrometeors and aerosols. Satellited derived NOx emissions using the Daily Emissions Constrained by Satellite Observations (DECSO) algorithm have been incorporated to give more up-to-date emissions. We analyze the results of a one-month forecasting period during the winter of 2021-2022 to assess the model's performance and understand the impact of hydrometeor and aerosol extinction on operational visibility forecasting. We find that for the city of Shanghai, aerosol extinction has a minor impact on the model’s performance when forecasting visibility below 1 km but becomes crucial for predictions spanning 1-10 km. Comparison against observations shows that the model well captures the general contributions from various chemical constituents with nitrate as the most important factor in aerosol extinction (~60%). Furthermore, our assessment of the North China Plain (NCP) highlights that in highly polluted areas aerosols could be significant for visibility below 1 km. Finally, we conduct case studies with the fully coupled WRF-Chem model and compare results with the offline WARMS-CMAQ system. Aerosol effects on fog and visibility forecasting due to feedbacks between aerosols, radiation, and cloud physics will be discussed.

How to cite: Xie, Y., Wang, X., Gao, Y., Chen, B., van der A, R., Ding, J., Gu, W., Zhou, M., and Wang, H.: Improving Visibility Forecasting during Haze-fog Processes in Shanghai and Eastern China: the Significance of Aerosol and Hydrometeor, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3686, https://doi.org/10.5194/egusphere-egu24-3686, 2024.

This study explores the potential impact of global navigation satellite system radio occultation (RO) data assimilation on the tropical cyclone (TC) intensity forecast over the western North Pacific. The forecast experiments are performed through a regional model for six TCs occurring in 2020. RO data are mainly obtained from the Constellation Observing System for Meteorology, Ionosphere, and Climate Mission II. The forecasts with and without assimilation of RO data are compared, and their difference is regarded as the impact of RO data on TC forecasts. Overall, the forecasts tend to underestimate the TC intensity relative to the best track data. Compared to the forecasts assimilating without RO data, forecasts assimilating with RO data improve the initial conditions and reduce the underestimation of TC intensity forecast by 13 kt and 16 hPa in subsequent forecasts. This intensity improvement is more significant for TCs developing in drier environments than those in moister environments. The main period of intensity increase is 48-24 h prior to TCs developing to the maximum intensity. The assimilation of RO data increases the moisture around the TC centers, especially at mid-levels (700-300 hPa). It also increases the low-level vorticity but reduces the mid-level vorticity around the TC centers. These characteristics favor TCs with stronger surface wind speed and lower sea surface pressure. In summary, this study highlights the positive contribution of RO data to TC intensity forecast and explores the potential mechanisms.

How to cite: Teng, H.-F.: Impact of radio occultation data assimilation on tropical cyclone intensity forecast over the western North Pacific, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4760, https://doi.org/10.5194/egusphere-egu24-4760, 2024.

Data assimilation is a widely used method for estimating the state and associated uncertainties in numerical models. While ensemble-based approaches are common, their computational expense arises from necessary ensemble integrations. This study improves the Weather Research and Forecasting–Advanced Research WRF (WRF-ARW) model by integrating it with the Parallel Data Assimilation Framework (PDAF) in a fully online mode. Through minimal modifications to the WRF-ARW code, an efficient data assimilation system is developed, leveraging parallelization and in-memory data transfers to minimize file I/O and model restarts during assimilation. The clear separation of concerns between method development and model application, facilitated by PDAF's model-agnostic structure, is an advantage. Evaluating the assimilation system through a twin experiment simulating a tropical cyclone reveals improved accuracy in temperature, U and V fields. The assimilation process incurs minimal overhead in run time compared to the model without data assimilation, demonstrating excellent parallel performance. Consequently, the online WRF-PDAF system proves to be an efficient framework for high-resolution mesoscale forecasting and reanalysis.

How to cite: Shao, C.: Augmenting WRF with PDAF for an Online Localized Ensemble Data Assimilation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4851, https://doi.org/10.5194/egusphere-egu24-4851, 2024.

EGU24-5395 | ECS | Orals | AS1.1

Uncertainty quantification for data-driven weather models 

Nina Horat, Christopher Bülte, Julian Quinting, and Sebastian Lerch

Data-driven machine learning methods for weather forecasting have experienced a steep progress over the last years, with recent studies demonstrating substantial improvements over physics-based numerical weather prediction models. Beyond improved forecasts, the major advantages of purely data-driven models are their substantially lower computational costs and faster generation of forecasts, once a model has been trained. However, in contrast to ensemble forecasts from physical weather models, most efforts in data-driven weather forecasting have been limited to deterministic, point-valued predictions only, making it impossible to quantify forecast uncertainties which is crucial for optimal decision making in applications.

Our overarching aim is to evaluate and compare methods for creating probabilistic forecasts from data-driven weather models. The uncertainty quantification (UQ) approaches we compare are either based on generating ensemble forecasts from data-driven weather models via perturbations to the initial conditions, or based on statistical post-hoc UQ methods. The perturbation-based methods either leverage initial conditions from the ECMWF IFS ensemble, add random Gaussian noise to the deterministic initial conditions, or add random field perturbations based on past observations (Magnusson et al., 2009). The post-hoc approaches operate on deterministic forecasts and quantify forecast uncertainty using established post-processing methods, namely distributional regression networks (Rasp and Lerch, 2018) and isotonic distributional regression (Walz et al., 2022; Henzi et al., 2021).

Using forecasts from Pangu-Weather (Bi et al., 2023), we evaluate these UQ methods over Europe for selected user-relevant weather variables, such as wind speed at 10 m, temperature at 2 m, and geopotential height at 500 hPa. We focus on daily initialised Pangu-Weather forecasts for 2022 with a forecast horizon of up to 7 days and compare their performance against ECMWF IFS ensemble forecasts. Our results suggest that Pangu-Weather predictions combined with UQ approaches yield improvements over the ECMWF ensemble forecasts for lead times of up to 5 days in terms of the Continuous Ranked Probability Score. However, it strongly depends on the variable of interest which of the UQ methods performs best, none of the different UQ methods performs best over all variables and lead times. Post-hoc UQ methods tend to perform better for shorter lead times, while initial condition perturbations are superior for longer lead times, with in particular the random field method showing promising results.

 

References:

  • Bi, K., Xie, L., Zhang, H., Chen, X., Gu, X. and Tian, Q. (2023). Accurate medium-range global weather forecasting with 3D neural networks. Nature, 619, 533–538.
  • Henzi, A., Ziegel, J. F. and Gneiting, T. (2021). Isotonic distributional regression. Journal of the Royal Statistical Society Series B: Statistical Methodology, 83, 963–993.
  • Magnusson, L., Nycander, J. and Källén, E. (2009). Flow-dependent versus flow-independent initial perturbations for ensemble prediction. Tellus A: Dynamic Meteorology and Oceanography, 61, 194.
  • Rasp, S. and Lerch, S. (2018). Neural networks for postprocessing ensemble weather forecasts. Monthly Weather Review, 146, 3885–3900.
  • Walz, E.-M., Henzi, A., Ziegel, J. and Gneiting, T. (2022). Easy Uncertainty Quantification (EasyUQ): Generating Predictive Distributions from Single-valued Model Output. Preprint, available at https://arxiv.org/abs/2212.08376.

How to cite: Horat, N., Bülte, C., Quinting, J., and Lerch, S.: Uncertainty quantification for data-driven weather models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5395, https://doi.org/10.5194/egusphere-egu24-5395, 2024.

EGU24-5656 | Orals | AS1.1

Impact of GNSS tropospheric gradient assimilation and sensitivity analysis 

Rohith Thundathil, Florian Zus, Galina Dick, and Jens Wickert

The Global Navigation Satellite System (GNSS) ground-based network in Europe is a comparatively dense network that provides valuable humidity information through Zenith Total Delays (ZTDs) and tropospheric gradients. ZTDs include information on column water vapor, while tropospheric gradients provide information on moisture distribution. Recently, we developed the tropospheric gradient operator (Zus et al., 2023) and implemented it in the Weather Research and Forecasting (WRF) model (Thundathil et al., 2023, under review).

We have conducted ZTD and tropospheric gradient assimilation experiments over a couple of periods, which lasted for two months. We will present our latest test period, the Benchmark Campaign organized within the European COST Action, in May and June 2013. Data from more than 250 GNSS stations in central Europe covering Germany, the Czech Republic, and part of Poland and Austria were assimilated during this period. The data assimilation (DA) system used a rapid update cycle of 3-dimensional variational DA with 6-hourly cycles for two months.

Our research methodology involved configuring a 0.1 x 0.1-degree mesh in the WRF model with 50 vertical levels up to 50 hPa for Europe. Model forcing was done with the European Centre for Medium-Range Weather Forecasts (ECMWF) operational analysis. We conducted three runs, which included the assimilation of conventional datasets from ECMWF (or control run), ZTD added on top of the control run, and ZTD and gradients on top of the control run. We observed a significant reduction of the root mean square errors; we observed a 42 % and 16 % reduction for ZTDs and gradients in the ZTD assimilation run, which further reduced to 43 % and 21 % for ZTDs and gradients in the ZTD and gradient assimilation. Validation with the atmospheric reanalysis ERA5 and radiosondes revealed improvements in the lower troposphere.

We conducted an additional sensitivity experiment using a sparsely distributed GNSS network. This process involved reducing the station density from roughly 0.5 degrees to 1 degree by replacing the original network with one consisting of 100 stations. We found that the improvement in the humidity field with the assimilation of ZTD and gradients from the sparse station network (1-degree resolution) is roughly the same as in the humidity field with the assimilation of ZTD only from the dense station network (0.5-degree resolution). Therefore, the assimilation of gradients in addition to ZTDs is particularly interesting in regions with a few GNSS stations. It may also be considered a cost-effective way to increase the density of networks.

After preliminary testing of the GNSS ZTD plus gradient assimilation with WRF, we are ready to move to convective-scale assimilation using an ensemble-based approach over different regions and seasons. We will be presenting initial results from our high-resolution simulations.

References

Zus, F., Thundathil R., Dick G., and Wickert J. "Fast Observation Operator for Global Navigation Satellite System Tropospheric Gradients." Remote Sensing 15, no. 21 (2023): 5114.

Thundathil, R. M., Zus, F., Dick, G., and Wickert, J. "Assimilation of GNSS Tropospheric Gradients into the Weather Research and Forecasting Model Version 4.4.1", Geoscientific Model Development Discussion [preprint], in review, 2023.

How to cite: Thundathil, R., Zus, F., Dick, G., and Wickert, J.: Impact of GNSS tropospheric gradient assimilation and sensitivity analysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5656, https://doi.org/10.5194/egusphere-egu24-5656, 2024.

Ensemble forecasts play a pivotal role in weather prediction, providing valuable insights into the inherent uncertainty of atmospheric processes. Strategies in ensemble construction involve generating multiple simulations by perturbing initial conditions, model parameters, or both. This diverse set of forecasts allows meteorologists to capture a range of possible future scenarios, acknowledging the inherent complexity of the atmosphere. Model resolution is a critical factor, influencing the representation of small-scale features and improving the overall accuracy of ensemble predictions. Additionally, forecast range-related issues address the challenge of extending predictions beyond a few days, where uncertainties tend to grow. Combining advanced statistical techniques with cutting-edge modeling technologies helps refine ensemble forecasts, enhancing our ability to anticipate and mitigate the impacts of weather-related events on society and the environment.

The investigation based operational global ensemble forecast system from NCEP, CMC, ECMWF and CMA to focus on the analyses of ensemble design that combined to the data assimilation for initial condition perturbation and various stochastic physical perturbations. The impact of model resolutions (both horizontal and vertical) will be addressed to the different atmospheric characteristics, such as forecast uncertainty, reliability and resolution. The forecast capability and predictability to the extreme events will be discussed from single model ensemble and multi-model ensemble. Finally, the 1st-moment and 2nd-moment ensemble forecast calibration will be demonstrated from traditional statistical method and machine learning based ensemble reforecasts

How to cite: Zhu, Y.: An assessment of prediction and predictability through the state-of-the-art global ensemble forecast systems, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6802, https://doi.org/10.5194/egusphere-egu24-6802, 2024.

EGU24-6813 | ECS | Posters on site | AS1.1

Numerical Investigation of High Impact Foehn storm in February 1925 using WRF and PALM models. 

Renuka Prakash Shastri, Stefan Brönnimann, and Peter Stucki

One of the most hazardous windstorms was observed in Switzerland on February 15, 1925. The storm is categorized as a 'High-impact Foehn Storm' that affected all Foehn regions of Switzerland. All communities, stables, and houses were wholly or partially damaged in the canton of Glarus. In previous work, the Weather Research and Forecasting Model (WRF) was used for downscaling the storm from the Twentieth Century Reanalysis (20CRv2) down to a grid width of 3 km. While many storm features were realistically simulated, wind speeds in the Glarus Valley, where most damage occurred, remained well below the expected values. Here, we go one step further by using a Large-Eddy Simulation model (LES) to analyze whether high gust peaks would occur at the bottom of the valley. For this, the PArallelized Large-eddy simulation Model (PALMv6.0) is coupled to WRFv4.1.2. In the first stage, WRFv4.1.2 was downscaled to a resolution of 1x1 km2 by using the "Twentieth Century Reanalysis" (20CRv3) as a boundary condition. Three nested domains with resolutions 25km, 5 km, and 1 km were set up for the simulation experiment. The second stage involves downscaling PALMv6.0 to a resolution of 20 m by using the output of WRFv4.1.2 as a boundary condition. The simulation shows strong winds between Netstal and Näfels on Earth's surface. Peak gusts of 40 m/s and more hit the valley floor south of Näfels. Strong turbulence fields reaching the ground at high velocities are observed in the central valley in the south-north direction. The simulation shows good agreement with the damage described, and the simulated peak gusts easily reach the measured maxima of extreme storms. Being able to realistically simulate the local characteristics of a Foehn storm that occurred a century back opens a new window to quantitative analyses of past extremes and their impacts.

How to cite: Shastri, R. P., Brönnimann, S., and Stucki, P.: Numerical Investigation of High Impact Foehn storm in February 1925 using WRF and PALM models., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6813, https://doi.org/10.5194/egusphere-egu24-6813, 2024.

The Cross-track Infrared Sounder (CrIS) observations (O) contributed greatly to numerical weather prediction. Further contribution depends on the success of all-sky data assimilation, which requires a method to produce realistic cloud/rain band structures from background fields (i. e., 6-h forecasts), and to remove large biases of all-sky simulation of brightness temperature in the presence of clouds. In this study, CrIS all-sky simulations of brightness temperatures at an arbitrarily selected window channel within Typhoon Hinnamnor (2022) are investigated. The 3-km Weather Research and Forecasting model with three microphysics schemes were used to produce 6-h background forecasts (B). The O−B statistic deviate greatly from Gaussian distribution with large biases in either water clouds, or thin ice clouds, or thick ice clouds within Typhoon Hinnamnor. By developing a linear regression function of three all-sky simulations of brightness temperature from 6-h forecasts with three microphysics schemes, the O−B statistics approximate a Gaussian normal distribution in water clouds, thin ice clouds and thick ice clouds. Taking the regression function that is established by a training dataset to combine 6-h background forecasts at later times, the cloud/rain band structures compared much more favorably with CrIS observations than those from an individual microphysics, and the O−B biases are significantly reduced. The work in this study to quantify and remove biases in background fields of brightness temperature and generating realistic typhoon cloud/rain band structures in background fields will allow a better description of center position, intensity and size to improve typhoon forecasts.

How to cite: Niu, Z.: Improving All-sky Simulations of Typhoon Cloud/Rain Band Structures of NOAA-20 CrIS Window Channel Observations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6917, https://doi.org/10.5194/egusphere-egu24-6917, 2024.

EGU24-6940 | Orals | AS1.1

Impacts of Direct Assimilation of the FY-4A/GIIRS Long-Wave Temperature Sounding Channel Data on Forecasting Typhoon In-Fa (2021) 

Lei Zhang, Zeyi Niu, Fuzhong Weng, Peiming Dong, Wei Huang, and Jia Zhu

The Advanced Weather Research Forecast model (WRF-ARW) is used to investigate the potential impacts of assimilating the FengYun-4A (FY-4A) Geostationary Interferometric Infrared Sounder (GIIRS) long-wave temperature sounding channel data on prediction of Typhoon In-Fa (2021). In addition, a series of data assimilation experiments are conducted to demonstrate the added value of the FY-4A/GIIRS data assimilation for typhoon forecasts. It is shown that the higher spectral resolution and broader coverage of GIIRS radiance data can positively impact the model analysis and forecasts with larger temperature and moisture increments at the initial time of simulations, thus producing the better simulation for typhoon warm core aloft, vortex wind structure and spiral rainfall band. Moreover, the assimilation of the GIIRS data can also lead to better storm steering flows and consequently better typhoon track forecasts. Overall, the assimilation of FY-4A/GIIRS temperature sounding channel data shows some added values to improve the track and storm structure forecasts of Typhoon In-Fa.

How to cite: Zhang, L., Niu, Z., Weng, F., Dong, P., Huang, W., and Zhu, J.: Impacts of Direct Assimilation of the FY-4A/GIIRS Long-Wave Temperature Sounding Channel Data on Forecasting Typhoon In-Fa (2021), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6940, https://doi.org/10.5194/egusphere-egu24-6940, 2024.

MPAS-JEDI, a relatively-new data assimilation (DA) system for the Model for Prediction Across Scales – Atmosphere (MPAS-A) based upon the Joint Effort for Data assimilation Integration (JEDI), allows to assimilate cloud-/precipitation-affected satellite microwave and infrared radiance data to analysis microphysical parameters, e.g., mixing ratios of hydrometeors. Global cycling DA experiments were conducted in the context of MPAS-JEDI’s hybrid-3DEnVar configured at 30km resolution with 80-member ensemble input at 60km that is produced using MPAS-JEDI's ensemble of 3DEnVar. The benchmark experiment assimilates conventional observations plus clear-sky radiances from AMSU-A and MHS. All-sky experiments add the assimilation of all-sky microwave (MW) radiances from AMSU-A’s and/or ATMS’s window channels over water as well as infrared (IR) channels of two geostationary sensors GOES-ABI and Himawari-AHI. In addition to the impact assessment on dynamic and thermodynamic variables, we investigated more the impact on cloud forecasts in terms of fitting to ABI/AHI radiance data at different wavelengths. The community radiative transfer model (CRTM) is used as the observation operator in both all-sky radiance DA and evaluation. The substantial positive impact on cloud forecasts was obtained with all-sky microwave DA (individually or collectively from AMSU-A and ATMS) in terms of a better forecast fitting to observed ABI/AHI channel 13's radiances up to 7 days, especially over tropical regions, where the day-1 forecast root-mean-square error is reduced up to 10%. Cloud forecast impact from assimilating all-sky ABI/AHI 3 water vapor channels' radiances is more limited although a clear benefit is seen for middle/upper troposphere moisture field, which is consistent with ABI/AHI water vapor channels' sensitivity height. Future research direction for all-sky MW and IR radiance DA with MPAS-JEDI will also be discussed.

How to cite: Liu, Z., Ban, J., and Banos, I.: Improving cloud forecasts with assimilation of cloud-/precipitation-affected microwave and infrared radiances using MPAS-JEDI, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7021, https://doi.org/10.5194/egusphere-egu24-7021, 2024.

We developed a rice paddy model based on the Noah LSM considering the standing water layer during the irrigation period. In the model, we adopted a consistent subcanopy process from thin to thick canopy conditions and considered small scalar roughness length of water surface in rice paddy field. We evaluated the model’s performance against observations from three rice paddy sites with different leaf area index (LAI) and water depths during the growing season. Two simulations were performed in an offline mode: the fixed irrigation simulation of Noah LSM with saturation moisture in the top two soil layers during the irrigation period (IRRI) and the developed model simulation (RICE). The evaluation results showed that RICE outperformed IRRI in the simulating ground, sensible (H) and latent heat (LH) fluxes and topsoil temperature (Tsoil) on hourly and diurnal time scales. Two sensitivity tests of RICE were performed in relation to the subcanopy resistance and standing water layer: RICE without consideration of small roughness length of water surface during the irritation period (BARE) and RICE with a constant standing water depth (FIX). The sensitivity tests showed that BARE calculated very low subcanopy resistance values when the sum of LAI and stem area index was less than 2 m2 m-2, which resulted in cold biases in the daily mean Tg and Tsoil and also led to overestimation of daily mean LH. There was no significant difference in RICE and FIX with hourly and seasonal time scale statistics, suggesting that H, LH,  Tg and Tsoil of the developed model are not sensitive to changes in water depth. The structure of the developed model was also discussed.

How to cite: Lim, H.-J. and Lee, Y.-H.: Development of Rice Paddy Model Based on Noah LSM: Consistent Parameterization of Subcanopy Resistance from the Ponded Water to Dense Rice Canopy , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7968, https://doi.org/10.5194/egusphere-egu24-7968, 2024.

EGU24-8324 | Posters on site | AS1.1

Eta features, additional to the vertical coordinate, deserving attention 

Fedor Mesinger, Katarina Veljovic, Sin Chan Chou, Jorge L. Gomes, André A. Lyra, and Dusan Jovic

An experiment reported in Mesinger and Veljovic (JMSJ 2020) and at the preceding EGU General Assembly, showed an advantage of the Eta over its driver ECMWF ensemble members in placing 250 hPa jet stream winds east of the Rockies.  Verifications subsequent to 2020 confirmed this advantage.  A byproduct of that experiment was that of the Eta ensemble switched to use sigma, Eta/sigma, also achieving 250 hPa wind speed scores better than their driver members, although to a lesser extent.  It follows that the Eta must include feature or features additional to the eta coordinate responsible for this advantage over the ECMWF.

An experiment we have done strongly suggests that the van Leer type finite-volume vertical advection of the Eta, implemented in 2007, may be a significant contributor to this advantage.  In that experiment, having replaced a centered finite-difference Lorenz-Arakawa scheme, this finite-volume scheme enabled a successful simulation of an intense downslope windstorm in the lee of the Andes.

Another likely and perhaps unique feature of the Eta contributing to that advantage is its sophisticated representation of topography, designed to arrive at the most realistic grid-cell values with no smoothing (Mesinger and Veljovic, MAAP 2017).

While apparently a widespread opinion is that it is a disadvantage of terrain intersecting coordinates that “vertical resolution in the boundary layer becomes reduced at mountain tops as model grids are typically vertically stretched at higher altitudes (Thuburn, 10.1007/978-3-642-11640-7 2011),” a comprehensive 2006 NCEP parallel test gave the opposite result.  With seemingly equal PBL schemes, the Eta showed a higher surface layer accuracy over high topography than the NMM, using a hybrid terrain-following system (Mesinger, BLM 2023).

Hundreds of thousands of the Eta forecasts and experiments performed demonstrate that the relaxation lateral boundary condition, almost universally used in regional climate models (RCMs), in addition to conflicting with the properties of the basic equations used, is unnecessary.  Similarly, so-called large scale or spectral nudging, frequently applied in RCMs, based on an ill-founded belief, should only be detrimental if possible numerical issues of the limited area model used are addressed.  Note that this is confirmed by the Eta vs ECMWF results we refer to above.

Even so, to have large scales of a nested model ensemble members most times more accurate than those of their driver members, surely requires not only the absence of detrimental techniques, but also the use of a lateral boundary condition (LBC) scheme that is not inducing major errors.  The scheme of the Eta is at the outflow points of the boundary prescribing one less condition than at the inflow points (e.g., Mesinger and Veljovic, MAAP 2013), and has for that reason been referred to by McDonald (MWR 2003) as one of “fairly well-posed” schemes.

How to cite: Mesinger, F., Veljovic, K., Chou, S. C., Gomes, J. L., Lyra, A. A., and Jovic, D.: Eta features, additional to the vertical coordinate, deserving attention, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8324, https://doi.org/10.5194/egusphere-egu24-8324, 2024.

Convective-scale ensembles are routinely used in operational centres around the world to produce probabilistic precipitation forecasts, but a lack of spread between members is providing forecasts that are frequently overconfident. This deficiency can be corrected by increasing spread, increasing forecast accuracy or both. A recent development in the Met Office forecasting system is the inclusion of Large-Scale Blending (LSB) in the convective-scale data assimilation scheme. This method aims to reduce the synoptic-scale forecast error in the analysis by reducing the influence of the convective-scale data assimilation at scales that are too large to be constrained by the limited domain. These scales are instead initialised using output from the global data assimilation scheme, which we expect to reduce the forecast error and, thus, improve the spread-skill relationship. In this study, we have quantified the impact of LSB on the spread-skill relationship of hourly precipitation accumulations by comparing forecast ensembles with and without LSB over a 17-day summer trial period. This trial found modest but significant improvements to the spread-skill relationship as calculated using metrics based on the Fractions Skill Score. Skill is improved for a lower precipitation centile by an average of 0.56% at the largest scales, but a corresponding degradation of spread limits the overall correction. The spread-skill disparity is reduced the most in the higher centiles due to a more muted spread response, with significant reductions of up to 0.40% obtained at larger scales. Case study analysis using a novel extension of the Localised Fractions Skill Score demonstrates how spread-skill improvements transfer to smaller-scale features, not just the scales that have been blended. There are promising signs that further spread-skill improvements can be made by implementing LSB more fully within the ensemble.

How to cite: Gainford, A.: Improvements in the spread-skill relationship of precipitation in a convective-scale ensemble through blending, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9034, https://doi.org/10.5194/egusphere-egu24-9034, 2024.

EGU24-9797 | Posters on site | AS1.1

Arctic temperature persistence in winter and spring and seasonal forecasting 

Haraldur Ólafsson and Negar Ekrami

Persistence is a natural first approximation or a baseline to seasonal temperature forecasting.  In the present study, winter and spring persistence in mean montly temperatures in the circumpolar Arctic is explored in long time-series of monthly mean data for the winter and spring seasons.

Locally, very high temporal correlations, as well as significant negative correlations are detected

Physically, the persistence may be traced to snow cover and sea-ice extent.  The variability in these factors may contribute directly to seasonal variability in the radiation budget as well as in surface fluxes, but there are also indirect, but detectable impacts upon regional circulation patterns.

How to cite: Ólafsson, H. and Ekrami, N.: Arctic temperature persistence in winter and spring and seasonal forecasting, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9797, https://doi.org/10.5194/egusphere-egu24-9797, 2024.

In modern forecasting it is now a common technique to use an ensemble of forecasts generated by Numerical Weather Prediction (NWP) models. This necessitates a statistical approach be taken when using these weather predictions to inform decision-making and leveraging probabilities in the production of forecasts. It is often required to take the spread of predictions made by NWPs in the ensemble and reduce these to a single value, a pseudo-deterministic forecast, analogous to a forecast made be a traditional deterministic NWP, in order to allow end users to make binary decisions often defined at a definite threshold. These values may be representative of a single physical parameter modelled (e.g. road surface temperature) or may combine multiple parameters in a physically consistent manner (e.g. the road surface temperature coupled to the depth of water on the road for calculating road state), and are used by stakeholders in a number of sectors often to inform safety critical decision making. Therefore, it is important to ensure that the methodology used to reduce the ensemble of predictions to a pseudo-deterministic forecast is as accurate as possible and can retain information related to the ensemble spread , whilst ensuring consistency in parameters through the spatial and temporal domain.

The Surface Transport Forecast (STF) system produces forecasts for different transport surfaces in response to NWP outputs. The STF system is architected such that it runs simultaneously for each member of the NWP forecast ensemble, producing a corresponding ensemble of STF predictions. This enables the computation of a pseudo-deterministic forecast, which retains the maximum amount of information provided by the NWP ensemble.

To reduce the STF ensemble to a pseudo-deterministic forecast a Kernel Density Estimation (KDE) is utilised to build Probability Density Functions (PDFs), which can be readily interrogated using standard statistical techniques. It is found that pseudo-deterministic forecasts, which are consistent across a combination of physical modelled parameters, can be determined using covariant techniques, ensuring the ensemble is reduced as late as possible in the forecast production keeping the maximum benefit provided by the forecast spread. We will present the numerical and computational implementation of the described method in our STF system. Further, we will analyse the pseudo-deterministic forecasts produced and verify the validity of results at specific locations using multiple years of road observations.

How to cite: Wiggs, J., Eyles, J., and Lake, A.: Creating a Pseudo-Deterministic Forecast for Surface Transport from an NWP Ensemble with Consistency Across Multiple Variables using KDE, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11209, https://doi.org/10.5194/egusphere-egu24-11209, 2024.

EGU24-11708 | ECS | Orals | AS1.1

Medium-Range Excessive Rainfall Prediction with Machine Learning 

Aaron Hill and Russ Schumacher

The prediction of excessive rainfall using numerical weather prediction (NWP) models is unequivocally difficult owing to the myriad of complexities that must be resolved (e.g., parent storm dynamics, microphysics) in order to forecast the placement and intensity of rainfall correctly. However, machine learning (ML) has provided a new avenue by which we can generate predictions of excessive rainfall with sufficient lead time to inform decision makers and planners to the threat of inclement weather. ML techniques are able to decode known long-standing relationships between environmental predictors and convective hazards from long historical records, and they have demonstrated tremendous value in predicting weather hazards at longer lead times (e.g., Hill et al. 2023). Further, continued effort by the meteorological community to explain ML models and their forecasts is building trust between developers and end users. As a result, their use in meteorological hazard forecasting is expanding, particularly into the medium range (e.g., 4-8 days) when forecasters are reliant on relatively coarse NWP models to create forecasts.

 

In this work, we are using Random Forests (RFs) to generate daily probabilistic forecasts of excessive rainfall at 1-8 day lead times. The RFs are trained using output from the Global Ensemble Forecast System and historical observations of excessive rainfall. Environmental parameters like precipitable water and CAPE, as well as modeled precipitation, are spatiotemporally arranged so the RFs can learn spatial and diurnal patterns that associate with excessive rainfall. The RF models are evaluated against a spatio-temporally varying climatology and show skill out to 7 days, and routinely outperform human-based forecasts past a 1-day lead time. In this presentation, we will highlight performance characteristics of the RFs into the medium-range (e.g., out to 8 days) and discuss the implications of excessive rainfall definitions in RF model training. Additionally, we will present an ensemble prediction framework that provides estimates of uncertainty and ranges of forecast solutions that operational forecasters desire at extended lead times.

How to cite: Hill, A. and Schumacher, R.: Medium-Range Excessive Rainfall Prediction with Machine Learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11708, https://doi.org/10.5194/egusphere-egu24-11708, 2024.

EGU24-11953 | Posters on site | AS1.1

A Unified Representation of Subgrid Convection in NOAA’s Unified Forecast System 

Jian-Wen Bao, Sara Michelson, Haiqin Li, and Sungsu Park

It remains challenging to represent subgrid convection in weather and climate models at horizontal grid resolution across the gray zone, in which convective clouds are only partially resolved by the model dynamics and it is required for the representation of subgrid convection to have a generalized transitional behavior as the model’s horizontal resolution varies.  A practical approach for such a representation is to scale the eddy transport of physical properties from a conventional convection parameterization scheme by a quadradic function of the fractional area covered by convective updrafts in the grid cell (Arakawa and Wu, 2013).  Despite this approach’s popularity, its generalization is limited theoretically by the fact that the coarse-graining statistical analysis that gave rise to the approach involved only an idealized scenario of deep convection in quasi-equilibrium.  Additionally, when applying this approach, there is a theoretical ambiguity associated with the validity of conventional convection parameterizations for a fractional area covered by convective updrafts in the grid cell that is not close to zero.

An alternative approach for subgrid convection representation across the gray zone is to apply a unified plume scheme that treats subgrid convection as nonlocal asymmetric eddies due to unresolved convection relative to the grid-mean vertical flow (Park, 2014).  This unified plume scheme represents unresolved convection relative to the grid-mean vertical motion without relying on quasi-equilibrium assumptions in conventional convection parameterizations.  Its generalized transitional behavior across the gray zone is naturally controlled by the size of the plumes representing unresolved convection that varies with the model’s horizontal resolution.  It simulates all unresolved convective transport of atmospheric properties within a single steady framework, allowing multiple convective plumes.  It also includes the prognosis of unresolved cold pool and convection organization within the planetary boundary layer.  The unified plume scheme circumvents the theoretical limitation and ambiguity of the above approach based on conventional convection parameterization.  It also rectifies the lack of plume memory across the time step in conventional convection parameterizations.

This presentation will focus on an ongoing effort to experiment with the alternative unified approach for representing subgrid convection across the gray zone in NOAA’s Unified Forecast System.  Results from 1-D and 3-D case studies will be shown to highlight the advantage of the unified plume scheme.

References:

Arakawa, A., and C.-M. Wu, 2013: A unified representation of deep moist convection in numerical modeling of the atmosphere. Part I. J. Atmos. Sci., 70, 1977–1992.

Park, S., 2014: A unified convection scheme (UNICON). Part I: Formulation. J. Atmos. Sci., 71, 3902–3930.

How to cite: Bao, J.-W., Michelson, S., Li, H., and Park, S.: A Unified Representation of Subgrid Convection in NOAA’s Unified Forecast System, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11953, https://doi.org/10.5194/egusphere-egu24-11953, 2024.

EGU24-12029 | Orals | AS1.1

NOAA’s Environmental Modeling Center Update: Transitioning to Unified Forecast System Applications for Operations 

Ivanka Stajner, Brian Gross, Vijay Tallapragada, Jason Levit, Raffaele Montuoro, Avichal Mehra, Daryl Kleist, and Fanglin Yang

National Oceanic and Atmospheric Administration’s (NOAA’s) Environmental Modeling Center (EMC) is a lead developer of operational Numerical Weather Prediction (NWP) systems at the National Weather Service (NWS), which are used for the protection of life and property and the enhancement of the economy. EMC transitions to operations and maintains more than 20 numerical prediction systems that are used by NWS, NOAA, other United States (U.S.) federal agencies, and various other stakeholders. These systems are developed through a close collaboration with academic, federal and commercial sector partners. EMC maintains, enhances and transitions-to-operations numerical forecast systems for weather, ocean, climate, land surface and hydrology, hurricanes, and air quality for the U.S. and global domains.

 

NOAA’s operational predictions are transitioning to the Unified Forecast System (UFS) framework in order to simplify the operational prediction suite of modeling systems. The UFS is being designed as a community-based, comprehensive atmosphere-ocean-sea-ice-wave-aerosol-land coupled Earth modeling system with coupled data assimilation and ensemble capabilities, organized around applications spanning from local to global domains and predictive time scales ranging from sub-hourly analyses to seasonal predictions.  Disparate legacy operational applications that have been developed and maintained by EMC in support of various stakeholder requirements are being transitioned to the UFS framework. The transition started several years ago and is planned to continue over the next few years. Fewer resulting applications will consolidate NCEP’s Production Suite that shares a set of common scientific components and technical infrastructure.  This streamlined suite is expected to accelerate the transition of research into operations and simplify maintenance of operational systems.

 

This talk describes major development and operational implementation projects at EMC over the last couple of years including for example a new UFS-based hurricane application, recent advances in the use of satellite data and a new verification system. We will present EMC plans for the next few years, within the overall NOAA strategy, and how planned efforts link with other modeling efforts within NOAA, in the broader U.S. and international community.

How to cite: Stajner, I., Gross, B., Tallapragada, V., Levit, J., Montuoro, R., Mehra, A., Kleist, D., and Yang, F.: NOAA’s Environmental Modeling Center Update: Transitioning to Unified Forecast System Applications for Operations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12029, https://doi.org/10.5194/egusphere-egu24-12029, 2024.

EGU24-12345 | Orals | AS1.1

Impact of a new land surface package in Canadian  numerical weather prediction system on the medium range weather forecast in the lower and upper atmosphere 

Nasim Alavi, Stephane Belair, Marco Carrera, Maria Abrahamowicz, Bernard Bilodeau, Dragan Simjanovski, Dorothee Charpentier, Bakr Badawy, and Sylvie Leroyer

A new land surface package developed at Environment and Climate Change Canada (ECCC) has been evaluated in the context of the medium-range global deterministic numerical weather prediction (NWP) system. The evaluation is performed by comparison of NWP forecasts against near-surface and

atmospheric analyses. The new land surface package includes i) new databases to specify soils and vegetation characteristics, ii) improved initialization of land surface variables by the assimilation of space-based remote sensing observations, and iii) a more sophisticated land surface scheme.

Evaluation for the screen-level air temperature and humidity indicates that the new land surface package resulted in smaller STDEs and larger temporal correlation between forecasts and analyses comparing to the current operational configuration. The improvement is greater for humidity than for air temperature.

Upper-air evaluation indicates that the impact of the new land surface package on the Planetary boundary layer (PBL) is substantial but more mixed, with large spatial variability in terms of its effect.

This study also investigated the physical and statistical links between near-surface and upper-air forecast errors at the medium range.

How to cite: Alavi, N., Belair, S., Carrera, M., Abrahamowicz, M., Bilodeau, B., Simjanovski, D., Charpentier, D., Badawy, B., and Leroyer, S.: Impact of a new land surface package in Canadian  numerical weather prediction system on the medium range weather forecast in the lower and upper atmosphere, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12345, https://doi.org/10.5194/egusphere-egu24-12345, 2024.

EGU24-12794 | Posters on site | AS1.1

Sensitivity Experiments of a Mountain-Induced Gravity Wave Drag Parameterizations for Global Weather Forecasting 

Songyou Hong, Jian-Wen Bao, Sara Michelson, Evelyn Grell, Mike Toy, Joe Olson, and Fanglin Yang

The lower tropospheric enhanced gravity wave drag (GWD) parameterization has been operational in Global Forecast System (GFS) since late 1990s. The scheme is based on Kim and Arakawa and further revised with the addition of flow blocking (Kim and Doyle). For UFSR2O project, there have been collaborative efforts to improve the GWD parameterization by revising the mountain induced GWD. Revisions include the updates in GWD and flow blocking (Choi and Hong), and turbulent orography form drag of Beljaars et al. Sensitivity experiments are performed to investigate the importance of partitioning GWD and flow blocking in the skill of medium-range forecasts. Alternative approach for TOFD (Richter et al.) is tested. Importance of the representation of sub-grid orography statistics is also examined. 

How to cite: Hong, S., Bao, J.-W., Michelson, S., Grell, E., Toy, M., Olson, J., and Yang, F.: Sensitivity Experiments of a Mountain-Induced Gravity Wave Drag Parameterizations for Global Weather Forecasting, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12794, https://doi.org/10.5194/egusphere-egu24-12794, 2024.

EGU24-13187 | Orals | AS1.1

Improved Weather Predictions Through Data Assimilation for GFDL SHiELD 

Mingjing Tong, Lucas Harris, Linjiong Zhou, Kun Gao, Alex Kaltenbaugh, and Baoqiang Xiang

The Geophysical Fluid Dynamics Laboratory (GFDL)’s System for High‐resolution prediction on Earth‐to‐Local Domains (SHiELD) model typically uses the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) analyses to initialize its medium-range global forecasts. Both initial condition (IC) and forecast model have an impact on model prediction skills. The quality of the IC is partially determined by the model short-range forecast used as first guess in data assimilation. 

A data assimilation (DA) system has been developed for the global SHiELD to demonstrate the prediction skills of the model initialized from its own analysis. The DA system largely leverages the advanced DA techniques used in GFS and assimilates all the observations assimilated in GFS. Compared to the SHiELD forecasts initialized from GFS analysis, SHiELD forecast skill is significantly improved by using its own analysis. Tremendous improvement was found in the Southern Hemisphere with positive impact lasting up to 10 days. The DA system is also useful in identifying and understanding model errors. The most noticeable model error detected by the DA system originates from the TKE-EDMF boundary layer scheme. The model error leads to insufficient ensemble spread, which could not be fully addressed by the multiplicative inflation and stochastic physics schemes used in the system. Including two versions of the TKE EDMF scheme in the ensemble can alleviate the systematic model error, which further improves forecast skills. The use of the interchannel correlated observation errors for Infrared Atmospheric Sounding Interferometer (IASI) and Cross-track Infrared Sounder (CrIS) was also investigated, which improves the forecast skill up to day 5 and further reduces the impact of the model error in the marine stratocumulus region. Further understanding of the model error associated with the TKE-EDMF scheme will be presented. 

How to cite: Tong, M., Harris, L., Zhou, L., Gao, K., Kaltenbaugh, A., and Xiang, B.: Improved Weather Predictions Through Data Assimilation for GFDL SHiELD, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13187, https://doi.org/10.5194/egusphere-egu24-13187, 2024.

EGU24-13504 | Posters on site | AS1.1

Second Year Progress of PREVENIR: Japan-Argentina Cooperation Project for Heavy Rain and Urban Flood Disaster Prevention 

Takemasa Miyoshi, Yanina G. Skabar, Shigenori Otsuka, Arata Amemiya, Juan Ruiz, Tomoo Ushio, Hirofumi Tomita, Tomoki Ushiyama, and Masaya Konishi

This presentation provides recent research highlights of the project PREVENIR, including radar quantitative precipitation estimates (QPE), ensemble nowcasting, data assimilation, numerical weather prediction (NWP), and hydrological model prediction. PREVENIR is an international cooperation project between Argentina and Japan since 2022 for five years under the Science and Technology Research Partnership for Sustainable Development (SATREPS) program jointly funded by the Japan International Cooperation Agency (JICA) and the Japan Science and Technology Agency (JST). The main goal is to develop an impact-based early warning system for heavy rains and urban floods in Argentina. PREVENIR takes advantage of leading research on Big Data Assimilation (BDA) with the Japan’s flagship supercomputer “Fugaku” and its predecessor “K” and develops a total package for disaster prevention, namely, monitoring, QPE, nowcasting, BDA and NWP, hydrological model prediction, warning communications, public education, and capacity building. The total package for disaster prevention will be the first of its kind in Argentina and will provide useful tools and recommendations for the implementation of similar systems in other parts of the world.

How to cite: Miyoshi, T., Skabar, Y. G., Otsuka, S., Amemiya, A., Ruiz, J., Ushio, T., Tomita, H., Ushiyama, T., and Konishi, M.: Second Year Progress of PREVENIR: Japan-Argentina Cooperation Project for Heavy Rain and Urban Flood Disaster Prevention, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13504, https://doi.org/10.5194/egusphere-egu24-13504, 2024.

EGU24-13557 | ECS | Orals | AS1.1

What determines the predictability of a Mediterranean cyclone?   

Benjamin Doiteau, Florian Pantillon, Matthieu Plu, Laurent Descamps, and Thomas Rieutord

Cyclones provides the majority of water supplies in the Mediterranean and are essential elements of the climate of the region. The most intense of them lead to natural disasters because of their violent winds and extreme rainfall. Identifying systematic errors in the predictability of Mediterranean cyclones is therefore essential to better anticipate and prevent their impact. The aim of this work is to understand what processes determine their predictability. 

We investigate the predictability of Mediterranean cyclones in a systematic framework using an ensemble prediction system. First, a reference dataset of 2853 cyclones is obtained by tracking lows in the ERA5 reanalysis, using an algorithm developed for the North Atlantic and adapted for the Mediterranean region. Then we investigate their predictability using IFS ensemble reforecasts in a homogeneous configuration over 22 years (2000-2021). The predictability in the reforecasts is quantified using probabilistic scores on cyclones trajectories and on intensity (mean sea level pressure) and then crossed with explanatory variables such as geographic area, cyclone velocity, season and intensity.

The evolution of location error with lead time shows a two phases growth, until and beyond 72 h, which will be discussed. When crossing the location and intensity errors with the explanatory variables, we can identify the conditions leading to a poorer (respectively better) predictability. In particular the velocity of cyclones appears to play an important role in the predictability of the location, the slower the cyclone the better the predictability, while the season is shown to play a greater role on the predictability of the intensity. These characteristics are also dependant on the sub-region considered and on the intensity of the low itself, the deeper the cyclone, the poorer the predictability in both the location and the intensity.

How to cite: Doiteau, B., Pantillon, F., Plu, M., Descamps, L., and Rieutord, T.: What determines the predictability of a Mediterranean cyclone?  , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13557, https://doi.org/10.5194/egusphere-egu24-13557, 2024.

EGU24-13655 | Posters on site | AS1.1

An overview of Japan’s Moonshot Goal 8 R&D program for controlling and modifying the weather by 2050 

Tetsuo Nakazawa, Takemasa Miyoshi, Takashi Sakajo, and Kohei Takatama

Forecast and control are the two sides of a coin. Recent improvements in numerical weather prediction have led to the point where we can start discussing the control of complex, chaotic weather systems. The Japan’s Moonshot Goal 8 research and development (R&D) program or simply MS8 was launched in 2022 to control extreme weather events such as typhoons and torrential rains and to reduce damage from extreme winds and rains, so that we can realize a society safe from such disasters by 2050. As the important first step toward the next 3-decade R&D, MS8 prioritizes numerical simulation experiments to investigate the feasibility of weather control under the constraints of energy and technology within human’s capability in a foreseeable future. Thus far, MS8 achieved promising results to reduce a peak rainfall of heavy downpours, and more results are expected by ongoing efforts. MS8 also accelerates developing basic science and technologies for realizing weather control, such as advanced weather models, computational models of flood damage, and mathematical approaches to intervention optimization techniques for large dimensional systems. In addition, addressing ethical, legal, and social issues (ELSI) is essential and a priority in MS8. This presentation will provide an overview of MS8 with highlighting scientific results.

 

How to cite: Nakazawa, T., Miyoshi, T., Sakajo, T., and Takatama, K.: An overview of Japan’s Moonshot Goal 8 R&D program for controlling and modifying the weather by 2050, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13655, https://doi.org/10.5194/egusphere-egu24-13655, 2024.

EGU24-13807 | ECS | Posters on site | AS1.1

Implementation of the Generalized Double-Moment Normalization Method in the Cloud Microphysics Scheme 

JoongHyun Jo, Sun-Young Park, Kyo-Sun Sunny Lim, Wonbea Bang, and Gyuwon Lee

Cloud microphysics parameterizations are generally divided into two categories: bin models that explicitly calculate the evolution of the drop size distribution (DSD) and bulk models that represent the DSD with a specific function. The Weather Research and Forecasting (WRF) Double-Moment 6-class (WDM6) scheme is one of the bulk microphysics options in the WRF model and is widely utilized for both research and operational purposes. In WDM6 scheme, the gamma form with a single static shape parameter is applied for the DSD of rain. This study adopts a generalized double-moment normalization method for the rain DSD in WDM6 scheme. Previous study mentions that the advantage of the generalized double-moment normalization method lies in its ability to singnificantly reduce the observed DSD scatter. Therefore, it can concisely represent the DSD with appropriate shape parameters, c and μ. The modified WDM6 is evaluated through simulations of an idealized 2D squall line and a summer precipitation case over the Korean peninsula. Based on similar experimental results from the original WDM6 and the modified WDM6 schemes, we can confirm that the generalized double-moment normalization method in the WDM6 scheme is properly implemented. We further collected the observed shape parameters suitable for the generalized double-moment DSD of rain over a two-year summer period (2018, 2019). The modified WDM6, with the observed shape parameters, simulates a more comparable spatial distribution of acummulated precipitation that occurred on 6 August 2013 with the observation, compared to the original WDM6. More detailed simulation results will be presented at the conference.

 

* This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT). (grant no.RS-2023-00208394).

How to cite: Jo, J., Park, S.-Y., Lim, K.-S. S., Bang, W., and Lee, G.: Implementation of the Generalized Double-Moment Normalization Method in the Cloud Microphysics Scheme, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13807, https://doi.org/10.5194/egusphere-egu24-13807, 2024.

EGU24-13978 | Posters on site | AS1.1

A Positive-Definite Moist EDMF Parameterization Scheme for Turbulent Mixing in the PBL 

Evelyn Grell and Jian-Wen Bao

Planetary boundary layer (PBL) parameterizations using the eddy diffusivity - mass flux (EDMF) technique for turbulent mixing in the convective PBL have been popularly used in weather and climate models.  When including moist adjustment processes, some numerical implementations of the EDMF parameterization may result in unphysical solutions of cloud condensate, for example, negative cloud water quantities.  To solve this problem, a procedure to obtain a positive definite solution is proposed to solve the moist EDMF equations.  In this presentation, we will demonstrate the formulation of the solution procedure and show examples of its impact on the PBL mixing simulation using a single-column model.

How to cite: Grell, E. and Bao, J.-W.: A Positive-Definite Moist EDMF Parameterization Scheme for Turbulent Mixing in the PBL, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13978, https://doi.org/10.5194/egusphere-egu24-13978, 2024.

Korea Institute of Atmospheric Prediction Systems (KIAPS) has developed a global forecasting system, Korean Integrated Model (KIM) and the model now operates with 12-km horizontal resolution. With plans to develop the numerical model in horizontally and vertically higher resolution, smoothed hybrid sigma-pressure (SMH) coordinate has applied to KIM to cover the influence of the terrain structure. The SMH is proposed to alleviate artificial circulations that horizontal pressure gradients and advection can be appeared along complex surfaces by reducing small-scale components more rapidly with height (Choi and Klemp, 2021). 
In this research, we focus on the prediction with higher-resolution topography in the SMH coordinate and it is revealed that more realistic data can be utilized than the previous topography adapted in hybrid sigma coordinate. The SMH coordinate could well reflect the steepness and roughness of complex region such as terrains near mountains without stability issue. To investigate the sensitivity to the detailed topographic data, case studies such as heatwave, cold surge and rainfall are dealt with especially in the Korean peninsula consisted of complex terrain. By considering more complex topography, the SMH coordinate performs better in capturing precipitation peak and temperature bias. In addition, it will be discussed that vertical propagation to the upper atmosphere is appropriately controlled due to the SMH coordinate. This study can contribute to the future work on adjusting diffusion coefficient by optimizing the SMH coordinate in much higher resolution.

How to cite: Kong, H.-J., Park, J.-R., and Nam, H.: Response of the SMoothed Hybrid sigma-pressure (SMH) coordinate to higher-resolution topographic data in Korean Integrated Model (KIM), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14761, https://doi.org/10.5194/egusphere-egu24-14761, 2024.

EGU24-15364 | Posters on site | AS1.1

Development of extended medium-range reforecasting system based on the Korean Integrated Model (KIM) 

Shin-Woo Kim, Taehyoun Shim, Ja-Young Hong, and Hye-Jin Park

The Korean Integrated Model (KIM) is a global numerical weather prediction (NWP) system developed by the first phase project of the Korea Institute of Atmospheric Prediction Systems (KIAPS) and has been used as the operational NWP system at the Korea Meteorological Administration (KMA) since April 2020. The second phase project of KIAPS aims at developing a next-generation NWP system to seamlessly predict from very short-range to extended medium-range. To improve the extended medium-range forecast, one of the main goals of KIAPS is to develop the ensemble prediction system with coupling to land, ocean, and sea ice. The production of extended medium-range reforecast data is necessary to understand the climatological characteristics and model biases of KIM. KIAPS developed an initial version of reforecasting system based on the KIM atmopheric model. The system has a spatial resolution of 50 km (NE090NP3) and consists of 91 vertical layers. We produce reforecast of the cold season cases for 20 years (from 2001 to 2020) and perform the diagnosis and verification of reforecast data. A suite of sensitivity experiments are also performed to investigate the impact of initial perturbations on the ensemble prediction system.

How to cite: Kim, S.-W., Shim, T., Hong, J.-Y., and Park, H.-J.: Development of extended medium-range reforecasting system based on the Korean Integrated Model (KIM), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15364, https://doi.org/10.5194/egusphere-egu24-15364, 2024.

EGU24-15467 | ECS | Posters on site | AS1.1

Impact of Nesting Techniques Over Short-Term WRF Forecast Accuracy 

A. Cem Çatal, Aysu Arık, M. Tuğrul Yılmaz, and İsmail Yücel

Weather Research and Forecasting (WRF) plays a crucial role in studying atmospheric dynamics and investigating the mesoscale weather prediction phenomena. However, WRF model offers lots of different configurations for physics, dynamics, and domain options that need to be investigated. From these configurations, domain options offer nesting techniques which may affect the fundamental structure and the performance of the simulations. Nesting options may impact the representation of fine-scale processes by increasing the resolution for the desired domain, compared to single-domain simulations. Existing studies on comparison of different nesting configurations in mesoscale domains are limited. This study presents a comparative analysis of three different nesting configurations in the WRF model over Türkiye. Accuracy of WRF-based short-term (24 to 48 hourly) temperature, wind, and precipitation forecasts over a 30-day period in November 2021 is investigated utilizing ground-station based observations. Three different model configurations are investigated: single domain, one-way feedback nested, and two-way feedback nested runs for the same time period and region. Root mean square error (RMSE), error standard deviation, and correlation coefficient were calculated for all three configurations. This study contributes to the optimization of nesting configurations in WRF mesoscale weather predictions, aiding decision-making processes reliant on accurate short-term forecasts in Türkiye.

How to cite: Çatal, A. C., Arık, A., Yılmaz, M. T., and Yücel, İ.: Impact of Nesting Techniques Over Short-Term WRF Forecast Accuracy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15467, https://doi.org/10.5194/egusphere-egu24-15467, 2024.

EGU24-15614 | Orals | AS1.1

Running global Machine Learning weather models - challenges, observations and conclusions 

Karolina Stanisławska and Olafur Rognvaldsson

Machine Learning (ML) became pervasive in every domain of the research, providing opportunities of modeling phenomena that were difficult to capture using known equations. From small models running on student computers, to giant LLMs trained on the whole Internet, ML models come in all shapes and sizes. To the meteorological community, one branch of this research stands out as revolutionary - ML-based global weather models.

ML-based global weather models lie on the opposite end of the spectrum compared to numerical weather prediction (NWP) models. Instead of representing the physics in a form of equations and solving these equations on the model grid, ML models are purely data-driven - even if they managed to represent physics internally, the inference of that physics would remain a black box.

Yet, these models underwent significant advancement in the past year - and three of them stand out - GraphCast (Google), ClimaX (Microsoft) and MetNet (Google). The former two, open-sourced for research purposes, are being tested currently at Belgingur. Having many years of experience with running and deploying NWP weather models, we notice how working with these models differs from working with the new class of ML-based (or data-driven) models.

This talk discusses essential differences between working with NWP and ML-based weather models. What we can, and what we cannot control? What does the process of working with such an ML model look like? What is the main advantage of an ML model run in production? What are the main obstacles in deploying an ML model and running it operationally?

With the current pace of the growth of Machine Learning models, we will be encountering them in our everyday work sooner or later. Knowing the challenges and opportunities of them will help us understand how to use them to our advantage.

How to cite: Stanisławska, K. and Rognvaldsson, O.: Running global Machine Learning weather models - challenges, observations and conclusions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15614, https://doi.org/10.5194/egusphere-egu24-15614, 2024.

EGU24-16714 | Orals | AS1.1

The Weather On Demand weather forecast framework - Recent developments and outlook 

Olafur Rognvaldsson and Karolina Stanislawska

Belgingur Ltd. has created a novel weather forecasting framework, called Weather On Demand – WOD, that is deployable in the cloud and on in-house hardware and which can be customised for any location world-wide at a very short notice.

The WOD framework is a distributed system for:

  • Running the WRF weather model for data-assimilation and forecasts by either triggering scheduled or on-demand jobs.
  • Gathering upstream weather forecasts and observations from a wide variety of sources.
  • Processing data for long to medium-term storage.
  • Making results available through APIs.
  • Making data files available to custom post-processors.

Much effort is put into starting processing as soon as the required data becomes available and in parallel when possible.

Recent additions to the WOD system include the potential of:

  • Optional use of the hybrid data assimilation techniques of the WRF Data Assimilation system [1, 2].
  • Set up a multi-domain dispersion forecast of volcanic ash and gases.
  • Use of the Verif [3] verification package to compare forecasts, both upstream and WOD, to observations.
  • Using different sources of initial data to that of the boundary forcing data.

On-going developments focuses on the use of in-situ UAV profiles and radar data as input to the WOD data assimilation system.

We have further started experimenting with using global models, both conventional NWP models as well as novel ML models (cf. abstract no. EGU24-15614).

References:

[1] Xuguang Wang, Dale M. Barker, Chris Snyder, and Thomas M. Hamill, 2008: A hybrid ETKF–3DVAR data assimilation scheme for the WRF model. Part I: Observing system simulation experiment. Mon. Wea. Rev., 136, 5116–5131.

[2] Xuguang Wang, Dale M. Barker, Chris Snyder, and Thomas M. Hamill, 2008: A Hybrid ETKF–3DVAR Data Assimilation Scheme for the WRF Model. Part II: Real Observation Experiments. Mon. Wea. Rev., 136, 5132–5147.

[3] Nipen, T. N., R. B. Stull, C. Lussana, and I. A. Seierstad, 2023. Verif: A Weather-Prediction Verification Tool for Effective Product Development. Bulletin of the American Meteorological Society 104, 9; 10.1175/BAMS-D-22-0253.1.

How to cite: Rognvaldsson, O. and Stanislawska, K.: The Weather On Demand weather forecast framework - Recent developments and outlook, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16714, https://doi.org/10.5194/egusphere-egu24-16714, 2024.

EGU24-17438 | ECS | Orals | AS1.1

A feature-based framework to investigate atmospheric predictability. 

Sören Schmidt, Michael Riemer, and Tobias Selz

Atmospheric predictability is intrinsically limited by the upscale growth of initial small-scale, small-amplitude errors. For practical predictability, model error and initial-condition uncertainty also contribute significantly. The accurate representation and interactions of these factors within numerical weather prediction systems determine the extent to which forecast uncertainty is correctly modeled. An improved understanding of upscale error-growth mechanisms and their flow dependence in numerical weather prediction models has several implications: it enables more focused model verification and development, aids in recognizing limitations in emerging forecasts systems like machine-learning-based approaches, and may indicate when the intrinsic limit of predictability has been reached.

Studying the flow dependence of error growth requires a local perspective, which is not provided by the traditional spectral perspective on upscale error growth. We here take a complementary approach and apply a feature-based perspective. We have developed an automated algorithm to identify error features in gridded data and track their spatial and temporal evolution. Errors are considered in terms of potential vorticity (PV) and near the tropopause, where they maximize. A previously derived PV-error tendency equation is evaluated to quantify the different contributions to error-growth experiments with the global prediction Model ICON from the German Weather Service. Errors in these experiments grow from differences in the seeding of a stochastic convection scheme. In a suite of experiments, this source of uncertainty competes with initial-condition uncertainty of varying magnitude. Evaluation of the process-specific error-growth rates allow the detailed quantification of the upscale-growth mechanisms. For this purpose, we integrate the growth rates over the respective area associated with an error feature. Examination of the combined growth rates of all features in an upscale-error-growth experiment reproduces a previously found three-stage multi-scale upscale-growth paradigm. Illustration the importance of flow dependence, the growth rates from a single feature can substantially differ from the overall average. Further highlighting this importance, intrinsic limits of predictivity can be identified for some features even in the presence of substantial initial-condition uncertainty. The presentation will conclude with a comparison of error evolution in conventional numerical weather prediction systems to a data-driven, machine-learned model.

How to cite: Schmidt, S., Riemer, M., and Selz, T.: A feature-based framework to investigate atmospheric predictability., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17438, https://doi.org/10.5194/egusphere-egu24-17438, 2024.

EGU24-18054 | Posters on site | AS1.1

Enhanced coupled land-atmosphere data assimilation for reanalysis 

Peter Weston, Patricia de Rosnay, Christoph Herbert, and Ewan Pinnington

The CERISE (CopERnIcus climate change Service Evolution) project aims to develop land and coupled land-atmosphere data assimilation systems for the next generation of coupled reanalysis. This encompasses technical enhancements to the system architecture as well as scientific changes to improve the quality of the reanalyses.

Recent work has focussed on developing ensemble perturbation methods for the land-surface. The existing ensemble spread in model variables at and near the land-surface is known to be insufficient which can cause problems when assimilating interface observations in a coupled system. This is because the existing ensemble perturbations are mainly applied to upper air atmospheric variables. One way to increase the spread at the surface is to directly perturb land-surface parameters such as vegetation cover and leaf area index. Results from this approach are encouraging in offline and coupled experiments.

Another part of the project is to enhance the assimilation of passive microwave radiances over land. Currently the use of surface-sensitive passive microwave channels are largely limited to the ocean due to challenges in forward modelling of complex and heterogenous land surfaces. In CERISE, machine learning approaches are being explored to develop an observation operator to enable the use of these observations over land and snow surfaces.

Finally, developing quasi-strongly coupled land-atmosphere assimilation is a key objective of the project. Developments so far have focussed on technical changes to build a framework to allow stronger coupling than the current weakly coupled assimilation strategy. A summary of recent progress in the CERISE project will be presented.

How to cite: Weston, P., de Rosnay, P., Herbert, C., and Pinnington, E.: Enhanced coupled land-atmosphere data assimilation for reanalysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18054, https://doi.org/10.5194/egusphere-egu24-18054, 2024.

EGU24-18151 | ECS | Posters on site | AS1.1

Evaluating multi-task learning strategies for tropical cyclones itnensity forecasting from satellite images 

Clément Dauvilliers, Anastase Charantonis, and Claire Monteleoni

Skillfully forecasting the evolution of tropical cyclones (TC) is crucial for
the human populations in areas at risk, and an essential indicator of a storm’s
potential impact is the Maximum Sustained Wind Speed, often referred to as
the cyclone’s intensity. Predicting the future intensity of ongoing storms is
traditionally done using statistical-dynamical methods such as (D)SHIPS and
LGEM, or as a byproduct of fully dynamical models such as the HWRF model.
Previous works have shown that deep learning models based on convolutional
neural networks can achieve comparable performances using infrared and/or
passive microwave satellite imagery as input. Recently, multi-task models have
highlighted that jointly learning the future intensity and other indicators such
as the TC size with shared network weights can improve the performance in the
context of intensity estimation. This ongoing work aims to evaluate which tasks
and architectures can lead to the best improvement for intensity forecasting.

How to cite: Dauvilliers, C., Charantonis, A., and Monteleoni, C.: Evaluating multi-task learning strategies for tropical cyclones itnensity forecasting from satellite images, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18151, https://doi.org/10.5194/egusphere-egu24-18151, 2024.

EGU24-18966 | Posters on site | AS1.1

Effects of initialization of sea ice properties on medium-range forecasts in the Korean Integrated Model 

Hyun-Joo Choi, Seok Hwan Kim, Baek-Min Kim, Myung-Seo Koo, Eek-Hyun Cho, and Young Cheol Kwon

The Korean Integrated Model (KIM) has been in operation at Korea Meteorological Administration (KMA) since April 2020 and its forecasting performance has been improved by updating model physical processes and data assimilation system. The model performance is comparable to that of the Unified Model run in parallel with the KIM at KMA during Boreal summer, but is relatively poor during the winter. One of the major biases in 5-day temperature forecasts for Norther Hemisphere winter is the low atmospheric cold bias over the Arctic region, and thus this study modifies the initialization of sea ice properties (sea ice thickness and temperature) to reduce the bias. First, the initial sea ice thickness data prescribed by climatology data produced using reanalysis data from the past 10 years (2000~2009) is replaced using the latest (2019~2021) reanalysis data. Second, the initial temperatures of the 1st and 2nd sea ice layers are set to the sea water freezing temperature instead of the currently applied first guess (background) sea ice temperatures. The effects of initialization modification on the medium-range forecasts of KIM are analyzed by performing two sets of experiments: cold start and warm cycle experiments without and with a data assimilation system in January 2022. The latest sea ice thickness initial data shows that sea ice thickness has decreased by about a factor of two. And its adoption by KIM increases surface and lower atmospheric temperatures in the Arctic sea ice region, alleviating cold biases in the region for both analysis and forecasts. In addition to sea ice thickness, sea ice temperature initialization modifications enhance Arctic warming and lead to greater improvement of cold bias. The warming effect in the lower Arctic is consistent in both cold start and warm cycle experiments. However, secondary effects induced by the Arctic warming occur significantly only in the warm cycle experiment and significantly affect forecasts fields not only in the polar region but also in the Southern Hemisphere and mid-latitude regions. Skill scores for medium-range forecasts in January 2022 are mostly improved (degraded) for the 12 UTC (00 UTC) initial conditions in the warm cycle experiment.

How to cite: Choi, H.-J., Kim, S. H., Kim, B.-M., Koo, M.-S., Cho, E.-H., and Kwon, Y. C.: Effects of initialization of sea ice properties on medium-range forecasts in the Korean Integrated Model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18966, https://doi.org/10.5194/egusphere-egu24-18966, 2024.

EGU24-18982 | ECS | Posters on site | AS1.1

Benefits of initializing equatorial waves on extratropical forecasts 

Chen Wang, Nedjeljka Žagar, and Sergiy Vasylkevych
Large initial uncertainties in the tropics are believed to compromise medium- and extended-range extratropical forecasts. A more reliable analysis of tropical Rossby and non-Rossby waves requires more tropical observations and improved data assimilation schemes. Wind observations are known to be more valuable than mass observations in the tropics, but it is not well-understood how different types of observations affect the accuracy of equatorial wave analysis and influence extratropical predictability. 
We investigate these questions by assimilating only wind or mass observations within the tropics using a perfect-model framework and a global model based on shallow-water equations and 3D-Var data assimilation. The mass-wind relationships of equatorial waves are built into the background-error covariance matrix with Rossby and non-Rossby waves as control variables in 3D-Var and prognostic variables in the forecast model.  Results demonstrate that wind observations are more efficient at reducing both tropical and extratropical forecast errors than mass observations. Adding mass-wind coupling further improves extratropical forecasts and it is especially beneficial for mass observations.Forecast benefits are quantified along latitude circles in terms of scales. A more accurate analysis of the equatorial Rossby waves is found to be the key for the propagation of observation impact from the tropics to midlatitudes. 
 

How to cite: Wang, C., Žagar, N., and Vasylkevych, S.: Benefits of initializing equatorial waves on extratropical forecasts, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18982, https://doi.org/10.5194/egusphere-egu24-18982, 2024.

EGU24-19593 | Posters on site | AS1.1

Dynamical downscaling and data assimilation for a cold-air outbreak in the European Alps during the Year Without Summer 1816 

Peter Stucki, Lucas Pfister, Stefan Brönnimann, Yuri Brugnara, Chantal Hari, and Renate Varga

The “Year Without Summer” of 1816 was characterized by extraordinarily cold and wet periods in Central Europe, and it was associated with severe crop failures, famine, and socio-economic disruptions. From a modern perspective and beyond its tragic consequences, the summer of 1816 represents a rare occasion to analyze the adverse weather (and its impacts) after a major volcanic eruption. However, given the distant past, obtaining the high-resolution data needed for such studies is a challenge. In our approach, we use dynamical downscaling, in combination with 3D-variational data assimilation of early instrumental observations, for assessing a cold-air outbreak in early June 1816. 
Our downscaling simulations reproduce and explain meteorological processes well at regional to local scales, such as a foehn wind situation over the Alps with much lower temperatures on its northern side. Simulated weather variables, such as cloud cover or rainy days, are simulated in good agreement with (eye) observations and (independent) measurements, with small differences between the simulations with and without data assimilation. However, validations with partly independent station data show that simulations with assimilated pressure and temperature measurements are closer to the observations. In turn, data assimilation requires careful selection, preprocessing and bias-adjustment of the underlying observations. Our findings underline the great value of digitizing efforts of early instrumental data and provide novel opportunities to learn from extreme weather and climate events as far back as 200 years or more.

How to cite: Stucki, P., Pfister, L., Brönnimann, S., Brugnara, Y., Hari, C., and Varga, R.: Dynamical downscaling and data assimilation for a cold-air outbreak in the European Alps during the Year Without Summer 1816, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19593, https://doi.org/10.5194/egusphere-egu24-19593, 2024.

EGU24-20366 | ECS | Orals | AS1.1

Irrigation parameterization in the Operational Numerical Weather Prediction model ICON-nwp 

Jane Roque, Arianna Valmassoi, and Jan Keller

Irrigation is one agricultural practice that contributes to maintain an optimal soil water content for crop development. Currently, farmers find this practice as an essential method for adapting to climate change. The Earth science community identified some irrigation effects beyond soil moisture and plant growth impact, as multiple studies found an influence on atmospheric variables such as 2 m temperature, relative humidity and even precipitation. Moreover, the effect of irrigation on the Earth’s system has been studied on various temporal and geographical scales and with different climate and land surface models. However, there are few studies that simulated the effect of irrigation on higher resolutions on a regional scale. Therefore, the aim of this study is to include the representation of irrigation processes in the operational ICON-nwp in Limited Area Mode on the EURO-CORDEX domain. The implementation of the current irrigation parameterization in ICON-nwp coupled with TERRA is an adaptation of the CHANNEL scheme developed by Valmassoi et al. (2020). We found suitable to include this scheme in the land surface and atmosphere interface of the icon-nwp-2.6.6-nwp0 version. The present study consists of four sensitivity experiments with different irrigation water amounts, namely 2.6 mmd-1, 6.7 mmd-1 and two fixed soil water contents, field capacity and saturation. All experiments have the same irrigation frequency (1 day), length (24 hours), and simulation period (May to August). The model settings for the experiments are 3 km resolution, 75 vertical levels and ICON boundary and initial conditions. The results from the difference between experiments and the control run demonstrate that ICON captures the irrigation effect on land surface atmospheric variables. As expected, soil moisture content increased on different magnitudes in all experiments. Moreover, 2 m temperature values dropped on average -0.74 K in irrigated areas. Likewise, energy fluxes were sensible to the different irrigation amounts.

How to cite: Roque, J., Valmassoi, A., and Keller, J.: Irrigation parameterization in the Operational Numerical Weather Prediction model ICON-nwp, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20366, https://doi.org/10.5194/egusphere-egu24-20366, 2024.

EGU24-20553 | Orals | AS1.1

Crossing the Valley of Death : Transitioning Weather Research to Operations in NOAA 

Chandra Kondragunta, Aaron Pratt, Kevin Garrett, Nicole Kurkowski, Wendy Sellers, and Valbona Kunkel

In 2016, the U. S. Congress created the Joint Technology Transfer Initiative (JTTI) program in the Office of Oceanic and Atmospheric Research (OAR), the research wing of the National Oceanic Atmospheric Administration (NOAA).  Within OAR, the Weather Program Office (WPO) is responsible for managing the JTTI program.  The main mission of this program is to continuously develop and transition the mature weather technologies from the research community to the National Weather Service (NWS) operations.  

JTTI selects promising Research to Operations (R2O) transition projects through two types of competitions: one for the external community (non-NOAA) that includes private, academic sectors and non-profit organizations through Notices of Funding Opportunities; and the other for the NOAA scientific community.  Additionally, the JTTI program collaborates with the NWS Office of Science and Technology Integration (OSTI) and provides funding for the Unified Forecasting System - R2O project and testbed activities.  JTTI-funded R2O projects cover three main frameworks within the NWS forecasting operations: the observational, modeling, and products and services frameworks.  The topics covered include data assimilation; convective scale weather modeling; stochastic physics; ensemble model building; hydrologic modeling; post-processing of model output on time scales ranging from hourly to subseasonal; high impact weather forecasting tools; artificial intelligence/machine learning; and social behavioral and economic science. To date, the JTTI program has funded 155 R2O projects and transitioned 20 projects to the NWS operations.  In this paper, we present the JTTI implementation process in NOAA and share some of the successful R2O stories.

How to cite: Kondragunta, C., Pratt, A., Garrett, K., Kurkowski, N., Sellers, W., and Kunkel, V.: Crossing the Valley of Death : Transitioning Weather Research to Operations in NOAA, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20553, https://doi.org/10.5194/egusphere-egu24-20553, 2024.

EGU24-3548 | Posters on site | AS1.2

Improving the Completion of Weather Radar Missing Data with Deep Learning 

Aofan Gong, Haonan Chen, and Guangheng Ni

Weather radars commonly suffer from the data-missing problem that limits their data quality and applications. Traditional methods for the completion of weather radar missing data, which are based on radar physics and statistics, have shown defects in various aspects. Several deep learning (DL) models have been designed and applied to weather radar completion tasks but have been limited by low accuracy. This study proposes a dilated and self-attentional UNet (DSA-UNet) model to improve the completion of weather radar missing data. The model is trained and evaluated on a radar dataset built with random sector masking from the Yizhuang radar observations during the warm seasons from 2017 to 2019, which is further analyzed with two cases from the dataset. The performance of the DSA-UNet model is compared to two traditional statistical methods and a DL model. The evaluation methods consist of three quantitative metrics and three diagrams. The results show that the DL models can produce less biased and more accurate radar reflectivity values for data-missing areas than traditional statistical methods. Compared to the other DL model, the DSA-UNet model can not only produce a completion closer to the observation, especially for extreme values, but also improve the detection and reconstruction of local-scale radar echo patterns. Our study provides an effective solution for improving the completion of weather radar missing data, which is indispensable in radar quantitative applications.

How to cite: Gong, A., Chen, H., and Ni, G.: Improving the Completion of Weather Radar Missing Data with Deep Learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3548, https://doi.org/10.5194/egusphere-egu24-3548, 2024.

EGU24-5373 | ECS | Orals | AS1.2 | Highlight

Convective environments in AI-models - What have AI-models learned about atmospheric profiles? 

Monika Feldmann, Louis Poulain-Auzeau, Milton Gomez, Tom Beucler, and Olivia Martius
The recently released suite of AI-based medium-range forecast models can produce multi-day forecasts within seconds, with a skill on par with the IFS model of ECMWF. Traditional model evaluation predominantly targets global scores on single levels. Specific prediction tasks, such as severe convective environments, require much more precision on a local scale and with the correct vertical gradients in between levels. With a focus on the North American and European convective season of 2020, we assess the performance of Panguweather, Graphcast and Fourcastnet for convective available potential energy (CAPE) and storm relative helicity (SRH) at lead times of up to 7 days.
Looking at the example of a US tornado outbreak on April 12 and 13, 2020, all models predict elevated CAPE and SRH values multiple days in advance. The spatial structures in the AI-models are smoothed in comparison to IFS and the reanalysis ERA5. The models show differing biases in the prediction of CAPE values, with Graphcast capturing the value distribution the most accurately and Fourcastnet showing a consistent underestimation.
By advancing the assessment of large AI-models towards process-based evaluations we lay the foundation for hazard-driven applications of AI-weather-forecasts.

How to cite: Feldmann, M., Poulain-Auzeau, L., Gomez, M., Beucler, T., and Martius, O.: Convective environments in AI-models - What have AI-models learned about atmospheric profiles?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5373, https://doi.org/10.5194/egusphere-egu24-5373, 2024.

EGU24-5571 | ECS | Orals | AS1.2

SHADECast: Enhancing solar energy integration through probabilistic regional forecasts 

Alberto Carpentieri, Doris Folini, Jussi Leinonen, and Angela Meyer

Surface solar irradiance (SSI) is a pivotal component in addressing climate change. As an abundant and non-fossil energy source, it is harnessed through photovoltaic (PV) energy production. As the contribution of PV to total energy production grows, the stability of the power grid faces challenges due to the volatile nature of solar energy, predominantly influenced by stochastic cloud dynamics. To address this challenge, there is a need for accurate, uncertainty-aware, near real-time, and regional-scale SSI forecasts with forecast horizons ranging from minutes to a few hours.

Existing state-of-the-art SSI nowcasting methods only partially meet these requirements. In our study, we introduce SHADECast [1], a deep generative diffusion model designed for probabilistic nowcasting of cloudiness fields. SHADECast is uniquely structured, incorporating deterministic aspects of cloud evolution to guide the probabilistic ensemble forecast, relying only on previous satellite images. Our model showcases significant advancements in forecast quality, reliability, and accuracy across various weather scenarios.

Through comprehensive evaluations, SHADECast demonstrates superior performance, surpassing the state of the art by 15% in the continuous ranked probability score (CRPS) over diverse regions up to 512 km × 512 km, extending the state-of-the-art forecast horizon by 30 minutes. The conditioning of ensemble generation on deterministic forecasts further enhances reliability and performance by more than 7% on CRPS.

SHADECast forecasts equip grid operators and energy traders with essential insights for informed decision-making, thereby guaranteeing grid stability and facilitating the smooth integration of regionally distributed PV energy sources. Our research contributes to the advancement of sustainable energy practices and underscores the significance of accurate probabilistic nowcasting for effective solar power grid management.

 

References

[1] Carpentieri A. et al., 2023, Extending intraday solar forecast horizons with deep generative models. Preprint at ArXiv. https://arxiv.org/abs/2312.11966 

How to cite: Carpentieri, A., Folini, D., Leinonen, J., and Meyer, A.: SHADECast: Enhancing solar energy integration through probabilistic regional forecasts, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5571, https://doi.org/10.5194/egusphere-egu24-5571, 2024.

EGU24-5849 | ECS | Posters on site | AS1.2

Towards seamless rainfall and flood forecasting in the Netherlands: improvements to and validation of blending in pysteps 

Ruben Imhoff, Michiel Van Ginderachter, Klaas-Jan van Heeringen, Mees Radema, Simon De Kock, Ricardo Reinoso-Rondinel, and Lesley De Cruz

Flood early warning in fast responding catchments challenges our forecasting systems. It requires frequently updated, accurate and high-resolution rainfall forecasts to provide timely warning of rainfall amounts that will reach a catchment in the coming hours. The Netherlands is a typical example, with polder systems below sea level, a high level of urbanization and catchments with short response times. The need for better short-term rainfall forecasts is clearly present, but this is generally not feasible with numerical weather prediction (NWP) models alone. Hence, an alternative rainfall forecasting method is desirable for the first few hours into the future.

Rainfall nowcasting can provide this alternative but quickly loses skill after the first few hours. A promising way forward is a seamless forecasting system, which tries to optimally combine rainfall products from nowcasting and NWP. In this study, we applied the STEPS blending method to combine rainfall forecasts from ensemble radar nowcasts with those from the Harmonie-AROME configuration of the ACCORD NWP model in the Netherlands. This blending method is part of the open-source nowcasting initiative pysteps. To make blending possible in an operational setup, including the needs of involved water authorities, we made several adjustments to the blending implementation in pysteps, for instance:

  • We reduced the computational time by using a faster preprocessing and advection scheme.
  • We improved the noise initialization (needed for generating ensemble members) to allow for stable forecasts, also when one or both product(s) contain(s) no rain.
  • We enabled a dynamic disaggregation of the 1-hour resolution NWP forecasts to match the temporal resolution of the radar nowcast.

We operationalized the updated blending framework in the flood forecasting platforms of the involved water authorities. Given a forecast duration of 12 hours for the blended forecast and a 10-minute time step, average computation times are 3.4 minutes for a deterministic run and 12.3 minutes for an ensemble forecast with 10 members on a 4-core machine. Preprocessing takes approximately 10 minutes and only needs to occur when a new NWP forecast is issued. We tested the implementation for an entire, rainy summer month (July 15 to August 15, 2023) and analyzed the results over the entire domain. The results demonstrate that the blending method effectively combines radar nowcasts with NWP forecasts. Depending on the statistical score considered (such as RMSE and critical success index), the blending method performs either better or on par with the best-performing individual product (radar nowcast or NWP). A consistent finding is that the blending closely tracks the nowcast quality during the initial 1 to 2 hours of the forecast (in this study, the nowcast had lower errors than NWP during the first 2 – 2.5 hours), after which it gradually transitions into the NWP forecast. At longer lead times, the seamless product retains local precipitation structures and extremes better than the NWP product. It does this by leveraging information from the radar nowcast and the stochastic perturbations. Based on these results, a seamless forecasting approach can be regarded as an improvement for the involved water authorities.

How to cite: Imhoff, R., Van Ginderachter, M., van Heeringen, K.-J., Radema, M., De Kock, S., Reinoso-Rondinel, R., and De Cruz, L.: Towards seamless rainfall and flood forecasting in the Netherlands: improvements to and validation of blending in pysteps, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5849, https://doi.org/10.5194/egusphere-egu24-5849, 2024.

EGU24-5909 | ECS | Posters on site | AS1.2

Impact of Spatial Density of Automatic Weather Station Data on Assimilation Effectiveness in WRF-3DVar Model 

Zeyu Qiao, Bu Li, Aofan Gong, and Guangheng Ni

Implementing the 3-Dimensional Variational (3DVar) data assimilation technique using high-density automatic weather station (AWS) observations substantially improves the precipitation simulation and forecast capabilities in the Weather Research and Forecasting (WRF) model. Given the impact of spatial distribution and quantity of observation data on assimilation effectiveness, there is a growing need to assimilate the most efficient amount of observation data to improve the precipitation forecast accuracy, especially in the context of the proliferation of data from diverse sources. This study investigates the impacts of spatial density of assimilated data on enhancing model predictions, focusing on a squall line event in Beijing on 2 August 2017 which has approximately 2400 AWSs in the simulation domain. Seven experiment groups assimilating varying proportions of AWS data (3.125, 6.25, 12.5, 25, 50, 75, and 100 percent of total AWSs) were conducted, comprising 10 experiments per group. The results were then compared with the experiment without data assimilation (CTRL) and the observations. Results show that while the WRF model roughly captured the evolution of this event, it overestimated the precipitation amount with significant deviations in precipitation locations. A general positive correlation was observed between the spatial density of assimilated data and the enhancement in model performance. However, there is a notable threshold beyond which additional data ceases to enhance forecast accuracy. The model performs best when the ratio of the number of assimilated AWSs to the model simulated area reaches 1/40 km-2. Moreover, significant variations in improvement effects across experiments within the same group indicate the substantial impact of spatial distribution of assimilated AWSs on forecast outcomes. This study provides a reference for devising more efficient and cost-effective data assimilation strategies in numerical weather prediction.

How to cite: Qiao, Z., Li, B., Gong, A., and Ni, G.: Impact of Spatial Density of Automatic Weather Station Data on Assimilation Effectiveness in WRF-3DVar Model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5909, https://doi.org/10.5194/egusphere-egu24-5909, 2024.

EGU24-6155 | ECS | Orals | AS1.2

Enhanced Foundation Model through Efficient Finetuning for Extended-Range Weather Prediction 

Shan Zhao, Zhitong Xiong, and Xiao Xiang Zhu

Weather forecasting is a vital topic in meteorological analysis, agriculture planning, disaster management, etc. The accuracy of forecasts varies with the prediction horizon, spanning from nowcasting to long-range forecasts. The extended range forecast, which predicts weather conditions beyond two weeks to months ahead, is particularly challenging. This difficulty arises from the inherent variability in weather systems, where minor disturbances in the initial condition can lead to significantly divergent future trajectories.

Numerical Weather Prediction (NWP) has been the predominant approach in this field. Recently, deep learning (DL) techniques have emerged as a promising alternative, achieving performance comparable to NWP [1, 2]. However, their lack of embedded physical knowledge often limits their acceptance within the research community. To enhance the trustworthiness of DL-based weather forecasts, we explore a transformer-based framework which considers complex geospatial-temporal (4D) processes and interactions. Specifically, we select the Pangu model [3] with a 24-hour lead time as the initial framework. To extend the prediction horizon to two weeks ahead, we employ a low-rank adaptation for model finetuning, which saves computation resources by reducing the number of parameters to only 1.1% of the original model. Besides, we incorporate multiple oceanic and atmospheric indices to capture a broad spectrum of global teleconnections, aiding in the selection of important features.

Our contributions are threefold: first, we provide an operational framework for foundation models, improving their applicability in versatile tasks by enabling training rather than limiting them to inference stages. Second, we demonstrate how to leverage these models with limited resources effectively and contribute to the development of green AI. Last, our method improves performance in extended-range weather forecasting, offering enhanced prediction skills, physical consistency, and finer spatial granularity. Our methodology achieved reduced RMSE on T2M, Z500, and T850 for 0.13, 139.2, and 0.52, respectively, compared to IFS. In the future, we plan to explore other settings, such as predicting precipitation and extreme temperatures.

REFERENCES
[1] Nguyen, Tung, et al. "ClimaX: A foundation model for weather and climate." arXiv preprint arXiv:2301.10343 (2023).
[2] Lam, Remi, et al. "Learning skillful medium-range global weather forecasting." Science (2023): eadi2336.
[3] Bi, Kaifeng, et al. "Accurate medium-range global weather forecasting with 3D neural networks." Nature 619.7970 (2023): 533-538.

How to cite: Zhao, S., Xiong, Z., and Zhu, X. X.: Enhanced Foundation Model through Efficient Finetuning for Extended-Range Weather Prediction, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6155, https://doi.org/10.5194/egusphere-egu24-6155, 2024.

EGU24-6545 | Posters on site | AS1.2

Improving precipitation nowcasting using deep generative models: a case-study and experiences in R2O  

Kirien Whan, Charlotte Cambier van Nooten, Maurice Schmeits, Jasper Wijnands, Koert Schreurs, and Yuliya Shapovalova

Precipitation nowcasting is essential for weather-dependent decision-making. The combination of radar data and deep learning methods has opened new avenues for research. Deep learning approaches have demonstrated equal or better performance than optical flow methods for low-intensity precipitation, but nowcasting high-intensity events remains a challenge. We use radar data from the Royal Netherlands Meteorological Institute (KNMI) and explore various extensions of deep learning architectures (i.e. loss function, additional inputs) to improve nowcasting of heavy precipitation intensities. Our model outperforms other state-of-the-art models and benchmarks and is skilful at nowcasting precipitation for high rainfall intensities, up to 60-min lead time. 

Transferring research to operations is difficult for many meteorological institutes, particularly for new applications that use AI/ML methods. We discuss some of these challenges that KNMI is facing in this domain. 

How to cite: Whan, K., Cambier van Nooten, C., Schmeits, M., Wijnands, J., Schreurs, K., and Shapovalova, Y.: Improving precipitation nowcasting using deep generative models: a case-study and experiences in R2O , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6545, https://doi.org/10.5194/egusphere-egu24-6545, 2024.

EGU24-6856 | Posters on site | AS1.2

Very Short-Range Precipitation Forecast in Korea Meteorological Administration 

ho yong lee, Jongseong Kim, Joohyung Son, and Seong-Jin Kim

Korea Meteorological Administration (KMA) has been providing the public with an hourly precipitation forecast updated every 10 minutes for the next 6 hours since 2015. This forecasts, named as the Very Short-Range Forecast (VSRF), differs from other longer forecasts ? such as short-range and medium-range forecasts issued by forecasters. The VSRF is automatically generated by a system based on two different models: MAPLE (McGill Algorithm for Precipitation nowcasting by Lagrangian Extrapolation) and KLAPS (Korea Local Analysis and Prediction System). 

MAPLE, based on Variational Echo Tracking (VET) from radar observations, has an intrinsic disadvantage: its performance decreases rapidly. On the other hand, numerical weather prediction systems like KLAPS are not initially as effective as MAPLE due to model balancing factors such as spin-up, but they maintain initial skill for a slightly longer period. Therefore, to provide the best predictions to the public, it is necessary to merge the two models properly. KMA conducted tests to determine the optimal way to utilize both models and established weights for each model based on their performance and precipitation tendencies. According to a 4-year evaluation, MAPLE outperforms for up to 2 hours, while KLAPS performs better after 4 hours. Consequently, the two models were merged with a hyperbolic tangent weight applied between 2 and 4 hours, and we named it as the best guidance. 

The best guidance was verified against precipitation observed by 720 raingauges over South Korea during the summer seasons from 2020 to 2023. It demonstrated better skill compared to both MAPLE and KLAPS. The average threat scores, with a rain intensity threshold of 0.5 mm/h throughout the forecast period, were 0.40 for the best guidance, 0.38 for MAPLE, and 0.35 for KLAPS.

The best guidance depends on both MAPLE and KLAPS. Therefore, KMA is actively working to improve the performance of each model. Additionally, a very short-range model based on AI is currently under development and running in semi-operations.

How to cite: lee, H. Y., Kim, J., Son, J., and Kim, S.-J.: Very Short-Range Precipitation Forecast in Korea Meteorological Administration, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6856, https://doi.org/10.5194/egusphere-egu24-6856, 2024.

EGU24-6873 | Posters on site | AS1.2

Does more frequent Very Short-Range Forecast provide more useful information? 

Joohyung Son, Jongseong Kim, and Seongjin Kim

The Very Short-Range Forecast (VSRF) for precipitation from the Korea Meteorological Administration (KMA) is released every 10 minutes, providing forecasts for the next 6 hours at 10-minute intervals. However, when the forecast is provided to the public, it is updated at 10-minute interval, but only provides up to 6 hours at every hour. Consequently, from the public's perspective, forecasts for specific times may change every 10 minutes. While this allows users to access the latest updates, it also poses a challenge in terms of reduced reliability due to constantly changing predictions.

This study aims to assess the prediction performance and variability between forecasts released at 10-minute intervals and those at 1-hour intervals. We evaluated with the Very Short-Range Forecast numerical model KLAPS in VSRF and seek to determine which approach offers more valuable information from the public's standpoint. The assessment focuses on two distinct types of precipitation. The first involves convective showers, which sporadically appear over short durations, driven by atmospheric instability during the Korean Peninsula's summer. The second relates to systematic precipitation associated with a frontal boundary accompanying a medium-scale low-pressure system. For convective showers, the 1-hour interval exhibits better performance and continuity, particularly as the forecast time extends. In the case of systematic precipitation, the 1-hour interval remains superior, though the skill is not as prominent as with convective showers. This highlights that an abundance of information doesn't always equate to high-quality information.

How to cite: Son, J., Kim, J., and Kim, S.: Does more frequent Very Short-Range Forecast provide more useful information?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6873, https://doi.org/10.5194/egusphere-egu24-6873, 2024.

EGU24-7086 | Posters on site | AS1.2

Development of stadium-specific numerical forecast guidance for weather forecast for the 2024 Gangwon Winter Youth Olympic Games 

Yeon-Hee Kim, Eunju Cho, Sungbin Jang, Junsu Kim, Hyejeong Bok, and Seungbum Kim

The 2024 Gangwon Winter Youth Olympic Games (GANGWON 2024) will be held in the province of Gangwon in the Republic of Korea from January 19 to February 1, 2024, which already hosted the Olympic Winter Games PyeongChang 2018. In order to successfully host these first Winter YOG to be held in Asia, which will be held for the first time in Asia, it is necessary to provide customized weather information for decision-making in game operation and support in establishing game strategies for athletes and their teams. Accordingly, the Korea Meteorological Administration develops point-specific numerical forecast guidance for major stadiums and provides it to the field to support successful hosting of YOG and improvement of performance. Numerical forecast guidance is the final data delivered to consumers or forecasters as post-processed numerical model data that has been corrected by applying altitude correction and statistical methods to produce highly accurate forecasts. For a total of 13 forecast elements (temperature, minimum/maximum temperature, humidity, wind direction/speed, precipitation, new snow cover, sky conditions, precipitation probability, precipitation type), we developed user-customized numerical forecast guidance specialized for competition points  (Gangneung Olympic Park, Pyeongchang Alpensia Venue, Biathlon Center, Olympic Sliding Center departure/arrival, Wellyhilli departure/arrival, High1 departure/arrival). Through the process of Perfect Prognostic Method (PPM), Model Output Statistics (MOS), optimization, and optimal merging, the systematic errors inherent in the numerical model are removed, and the optimal data (BEST) with improved forecasting performance is provided as customized numerical forecast guidance specific to stadium locations.  In the prediction performance evaluation for the period of December 2023, the accuracy (improvement rate) compared to the average of available models was temperature 1.49℃ (18%), humidity 12% (25%), wind speed 1.87m/s (33%), and visibility 12.8km (17%).

How to cite: Kim, Y.-H., Cho, E., Jang, S., Kim, J., Bok, H., and Kim, S.: Development of stadium-specific numerical forecast guidance for weather forecast for the 2024 Gangwon Winter Youth Olympic Games, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7086, https://doi.org/10.5194/egusphere-egu24-7086, 2024.

EGU24-7091 | Posters on site | AS1.2

The Development of precipitation model modifed with ECMWF IFS and XGBoost and its performance verification 

Eunju Cho, Yeon-Hee Kim, Seungbum Kim, and Young Cheol Kwon

This study was conducted to develop a modified precipitation model for its amount and existence by combining machine learning method, Extreme Gradient Boosting(XGBoost), with ECMWF IFS(Integrated forecasting system) and, finally, estimate the related performance.

According to the analysis of regional precipitation characteristic, prior to its development, the ratio of precipitation existence was various on a basis of a forecast’s district and its season. These different patterns on each district makes it necessary to develop the regional and seasonal model respectively.

And, the first attempt at the machine learning showed the importance of each feature as input-variables, as a result of which cloud physics-related features, for example large-area precipitation, total precipitation, visibility and what not, proved so significant. However, the insufficient amount of these feature’s data seemed to result in overfitting. And therefore, the feasible features, except for cloud physics-related things, of IFS data were used. In addition, auxiliary features and their gradient for every lead-time were calculated and added: relative vorticity, divergence, equivalent potential temperature, main 6 patterns for Korean summer and so on. The number of features amounted to around 144 with which for the 9-year training set, 2013~2021, based learning to be conducted regionally, followed by using validation-set of 2022.

As a result of validation for precipitation existence and its amount up to 135 hours ahead on the 10 regions at 00UTC in summer of 2022, Critical Success Index(CSI) was more improved by 10.3% than before. Accuracy(ACC) for each lead-time rose by 6% and its fluctuation also decreased. And the correction by this machine learning alleviated the overfitting trend of precipitation forecast amount produced by the original model, and improved correlation and linearity between observation and forecast. In particular, while the machine learning prevailed over the original model up to 100 hours ahead, from then on, both of them showed similar performance or that of the former was downward slightly. If the above-mentioned cloud physics features are used to further sharpen machine learning technique, its performance should be enhanced more and more.

How to cite: Cho, E., Kim, Y.-H., Kim, S., and Kwon, Y. C.: The Development of precipitation model modifed with ECMWF IFS and XGBoost and its performance verification, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7091, https://doi.org/10.5194/egusphere-egu24-7091, 2024.

EGU24-7291 | ECS | Posters on site | AS1.2

Improvements in fog predictions via a modified reconstruction of moisture distribution using the Weather Research and Forecasting(WRF) model 

Eunji Kim, Soon-Young Park, Jung-Woo You, and Soon-Hwan Lee

Since fog is an important weather phenomenon affecting the traffic safety, accurate fog forecasting should be attained to minimize meteorological disasters. Most fog forecasts determine only the presence or absence of fog based on less visibility than 1 km, which is known as the visibility diagnostic method. During this process, fog could be predicted by the visibility calculated in the numerical weather prediction (NWP) model using the cloud liquid water content (LWC) near the surface. In this study, we investigated to increase the accuracy of fog forecast by optimizing the reconstruction of moisture distribution method, which can simulate the intensity of fog as well as the presence or absence of fog. The performances of the fog simulations were examined by modifying the relative humidity threshold at a height of 2 m and the stability parameters which affect turbulence and also one of the important criteria for fog occurrence. When we applied the optimize parameters to fog prediction in the winter seasons, the probability of detection (POD) has been increased significantly from 0.21 to 0.54. These improvements were attributed to the corrected relative humidity threshold and the stability parameters. Although the false alarm rate (FAR) remained almost unchanged, the critical success index (CSI) has been improved slightly lesser than those of the POD. When we analyzed the life cycle of fog, it takes time for the NWP model to simulate water droplets in the fog-developing stage. Therefore, the accuracy of the fog simulation is intimately related to the reconstruction of moisture distribution. The NWP model, however, showed a better performance in the process of fog dissipation than the reconstruction of moisture distribution method that was sensitive to temperature and turbulence. In conclusion, the reconstruction of moisture distribution led to a considerable improvement of the fog prediction in the generation and development stage since we used the optimized humidity threshold. It is also expected that accurate fog prediction could be achieved in the future by considering the aerosol effects, which is another importance factor for the fog generation.

How to cite: Kim, E., Park, S.-Y., You, J.-W., and Lee, S.-H.: Improvements in fog predictions via a modified reconstruction of moisture distribution using the Weather Research and Forecasting(WRF) model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7291, https://doi.org/10.5194/egusphere-egu24-7291, 2024.

EGU24-7536 | Orals | AS1.2

Nowcasting with Transformer-based Models using Multi-Source Data  

Çağlar Küçük, Apostolos Giannakos, Stefan Schneider, and Alexander Jann

Rapid advancements in data-driven weather prediction have shown notable success, particularly in nowcasting, where forecast lead times span just a few hours. Transformer-based models, in particular, have proven effective in learning spatiotemporal connections of varying scales by leveraging the attention mechanism with efficient space-time patching of data. This offers potential improvements over traditional nowcasting techniques, enabling early detection of convective activity and reducing computational costs. 

In this presentation, we demonstrate the effectiveness of a modified Earthformer model, a space-time Transformer framework, in addressing two specific nowcasting challenges. First, we introduce a nowcasting model that predicts ground-based 2D radar mosaics up to 2-hour lead time with 5-minute temporal resolution, using geostationary satellite data from the preceding two hours. Trained on a benchmark dataset sampled across the United States, our model exhibits robust performance against various impactful weather events with distinctive features. Through permutation tests, we interpret the model to understand the effects of input channels and input data length. We found that the infrared channel centered at 10.3 µm contains skillful information for all weather conditions, while, interestingly, satellite-based lightning data is the most skilled at predicting severe weather events in short lead times. Both findings align with existing literature, enhancing confidence in our model and guiding better usage of satellite data for nowcasting. Moreover, we found the model is sensitive to input data length in predicting severe weather events, suggesting early detection of convective activity by the model in rapidly growing fields. 

Second, we present the initial attempts to develop a multi-source precipitation nowcasting model for Austria, tailored to predict impactful events with convective activities. This model integrates satellite- and ground-based observations with analysis and numerical weather prediction data to predict precipitation up to 2-hour lead time with 5-minute temporal resolution.  

We conclude by discussing the broad spectrum of applications for such models, ranging from enhancing operational nowcasting systems to providing synthetic data to data-scarce regions, and the challenges therein.

How to cite: Küçük, Ç., Giannakos, A., Schneider, S., and Jann, A.: Nowcasting with Transformer-based Models using Multi-Source Data , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7536, https://doi.org/10.5194/egusphere-egu24-7536, 2024.

EGU24-7753 | ECS | Orals | AS1.2

On the usefulness of considering the run-to-run variability for an ensemble prediction system 

Hugo Marchal, François Bouttier, and Olivier Nuissier

The run-to-run variability of numerical weather prediction systems is at the heart of forecasters' concerns, especially in the decision-making process when high-stakes events are considered. Indeed, forecasts that are brutally changing from one run to another may be difficult to handle and can lose credibility. This is all the more true nowadays, as many meteorological centres have adopted the strategy of increasing runs frequency, some reaching hourly frequencies. However, this aspect has received little attention in the literature, and the link with predictability has barely been explored.

In this study, run-to-run variability is investigated through 24h-accumulated precipitations forecasted by AROME-EPS, Météo-France's high resolution ensemble, which is refreshed 4 times a day. Focusing on the probability of some (warning) thresholds being exceeded, results suggest that how forecasts evolve over successive runs can be used to improve their skill, especially reliability. Various possible aspects of run sequence have been studied, from trends to rapid increases or decreases in event probability at short lags, also called "sneaks" or "phantoms", as well as the persistence of a non-zero probability through successive runs. The added value provided by blending successive runs, known as lagging, is also discussed.

How to cite: Marchal, H., Bouttier, F., and Nuissier, O.: On the usefulness of considering the run-to-run variability for an ensemble prediction system, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7753, https://doi.org/10.5194/egusphere-egu24-7753, 2024.

EGU24-8449 | ECS | Orals | AS1.2

Radiation fog nowcasting with XGBoost using station and satellite data 

Michaela Schütz, Jörg Bendix, and Boris Thies

The research project “FOrecasting radiation foG by combining station and satellite data using Machine Learning (FOG-ML)” represents a comprehensive effort to advance radiation fog prediction using machine learning (ML) techniques, with focus on the XGBoost algorithm. The nowcasting period is up to four hours into the future.

The initial phase of the project involved developing a robust classification-based model that could accurately forecast the occurrence of radiation fog, a challenging meteorological phenomenon. Radiation fog is particularly difficult to predict because it depends on a complex interplay of factors such as ground cooling, humidity, and minimal cloud cover. It often forms rapidly and in local areas. This required careful analysis of the chronological order of the data and consideration of autocorrelation to increase the effectiveness of model training.

Building upon this foundation, the next two phases concentrated on improving the model’s forecasting performance for visibility classes (step 2) and for absolute visibility values (step 3). The main focus was then on a nowcasting period of up to two hours. This nowcasting period is critical in fog prediction as it directly impacts transportation planning and safety. The use of ground-level observations in step 2 and integration of satellite data in step 3 provided a rich dataset that allowed for more nuanced model training and validation.

In the latest phase of research, satellite data has been incorporated to further refine the prediction model, especially regarding the fog formation and dissipation. Satellite imagery provides additional variables of atmospheric data that are not readily available from ground-based observations. This integration aims to address one of the inherent limitations in fog forecasting methods, particularly in areas where ground-based observations are sparse.

Throughout the different stages, the project emphasized the need for thorough data processing and validation. This included the implementation of cross-validation techniques to assess the generalizability of the models and the use of various metrics to gauge their predictive power. This has also included the incorporation of trend information, which has proven to be crucial for forecasting with XGBoost. Our research has also shown that not only the overall performance, but also the performance of the transitions (fog formation and resolution) should be analyzed to get a complete picture of the model performance. This finding was consistent throughout the entire study, regardless of classification-based forecast or regression-based forecast.

We have been able to significantly improve the performance of our nowcasting model with each step. We will be presenting the key findings and latest results from this research at EGU24.

All results from step 1 can be found in “Current Training and Validation Weaknesses in Classification-Based Radiation Fog Nowcast Using Machine Learning Algorithms” from Vorndran et al. 2022. All results from step 2 can be found in “Improving classification-based nowcasting of radiation fog with machine learning based on filtered and preprocessed temporal data” from Schütz et al. 2023.

How to cite: Schütz, M., Bendix, J., and Thies, B.: Radiation fog nowcasting with XGBoost using station and satellite data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8449, https://doi.org/10.5194/egusphere-egu24-8449, 2024.

EGU24-9528 | ECS | Orals | AS1.2

Ensemble forecast post-processing based on neural networks and normalizing flows 

Peter Mlakar, Janko Merše, and Jana Faganeli Pucer

Ensemble weather forecast post-processing can generate more reliable probabilistic weather forecasts compared to the raw ensemble. Often, the post-processing method models the future weather probability distribution in terms of a pre-specified distribution family, which can limit their expressive power. To combat these issues, we propose a novel, neural network-based approach, which produces forecasts for multiple lead times jointly, using a single model to post-process forecasts at each station of interest. We use normalizing flows as parametric models to relax the distributional assumption, offering additional modeling flexibility.We evaluate our method for the task of temperature post-processing on the EUPPBench benchmark dataset. We show that our approach exhibits state-of-the-art performance on the benchmark, improving upon other well-performing entries. Additionally, we analyze the performance of different parametric distribution models in conjunction with our parameter regression neural network, to better understand the contribution of normalizing flows in the post-processing context. Finally, we provide a possible explanation as to why our method performs well, exploring per-lead time input importance.

How to cite: Mlakar, P., Merše, J., and Faganeli Pucer, J.: Ensemble forecast post-processing based on neural networks and normalizing flows, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9528, https://doi.org/10.5194/egusphere-egu24-9528, 2024.

EGU24-9659 | Posters on site | AS1.2

Application Research of Multi-source New Detection Data in Snow Depth Prediction for Beijing Winter Olympics 

Jia Du, Bo Yu, Yi Dai, Sang Li, Luyang Xu, Jiaolan Fu, Lin Li, and Hao Jing

According to the demand of the Winter Olympic Organizing Committee for snow depth prediction, the application of multi-source new data in snow depth was studied based on densely artificial snow-depth measurement, microscopic snowflake shape observation and PARSIVEL data. The specific conclusions are as follows: (1) Most of the Snow-Liquid-Ratio(SLR) in Beijing competition zone was between 0.69 and 1.43 (unit: cm/mm, the same below), while that in Yanqing zone was between 0.53 and 1.17. But 7.5% of the SLRs in Yanqing zone exceeded 3.5, which all occurred in the same period of the key service time of 2022 Beijing Winter Olympics, making it more difficult to predict new snow depth. (2) The higher the SLR, the lower the daily minimum surface temperature and lowest air temperature.  Plate or column ice crystals, rimed snowflakes, and dendritic snowflakes were observed, whose corresponding SLRs increased. The average falling speed of particles falling below 2m/s can be used as an indicator of phase transfer. (3) The vertical distributions of temperature and humidity with SLR <1 or >2 were summarized. It was found that when the cloud area coincided with the dendritic growth zone with height close to Yanqing zone, the SLR would be more than 2, higher than that of Beijing zone. (4) A weather concept model generating large SLR was extracted. Snow in Beijing is often accompanied by easterly winds in boundary layer, which is easy to form a wet and ascending layer in the lower troposphere due to the blocking of western mountain. In the late winter season, helped by the temperature’s profile, it tends to produce unrimed dendritic snowflakes, leading to a great SLR.

How to cite: Du, J., Yu, B., Dai, Y., Li, S., Xu, L., Fu, J., Li, L., and Jing, H.: Application Research of Multi-source New Detection Data in Snow Depth Prediction for Beijing Winter Olympics, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9659, https://doi.org/10.5194/egusphere-egu24-9659, 2024.

EGU24-9935 | ECS | Posters on site | AS1.2

Machine and Deep Learning algorithms to improve weather forecasts over a complex orography Mediterranean region 

Luca Furnari, Umair Yousuf, Alessio De Rango, Donato D'Ambrosio, Giuseppe Mendicino, and Alfonso Senatore

The rapid development of artificial intelligence algorithms has generated considerable interest in the scientific community. The number of scientific articles relating to applying these algorithms for weather forecasting has increased dramatically in the last few years. In addition, the recent operational launch of products such as GraphCast has put this area of research even more in the spotlight. This work uses different Machine Learning and Deep Learning algorithms, namely ANN (Artificial Neural Network), RF (Random Forest) and GNN (Graph Neural Network), with the aim to improve the short-term (1-day lead time) forecasts provided by a physically-based forecasting system. Specifically, the CeSMMA laboratory, since January 2020, has been producing daily forecasts accessible via the https://cesmma.unical.it/cwfv2/ webpage related to a large portion of southern Italy. The NWP (Numerical Weather Prediction) system is based on the WRF (Weather Research and Forecasting) model, with boundary and initial conditions provided by the GFS (Global Forecasting System) model. The AI algorithms post-process the NWP output, applying correction factors achieved by a two-year training considering the observations of the dense regional monitoring network composed of ca. 150 rain gauges.

The results show that the AI is able to improve daily rainfall forecasts compared to ground-based observations. Specifically, the ANN reduces the average MSE (Mean Square Error) by approximately 29% and the RF by 21% with respect to the WRF forecast for the whole study area (about 15’000 km2). Moreover, the GNN applied to a smaller area (considering only 22 rain gauges) further reduces the MSE by 35% during the heaviest rainfall months.

In addition to improving the performance of the forecast, the AI-based post-processing provides reasonable precipitation spatial patterns, reproducing the main physical phenomena such as the orographic enhancement since it is not a surrogate model and benefits from the original physically-based forecasts.

 

Acknowledgements. This work was funded by the Next Generation EU - Italian NRRP, Mission 4, Component 2, Investment 1.5, call for the creation and strengthening of ‘Innovation Ecosystems’, building ‘Territorial R&D Leaders’ (Directorial Decree n. 2021/3277) - project Tech4You - Technologies for climate change adaptation and quality of life improvement, n. ECS0000009. This work reflects only the authors’ views and opinions, neither the Ministry for University and Research nor the European Commission can be considered responsible for them.

How to cite: Furnari, L., Yousuf, U., De Rango, A., D'Ambrosio, D., Mendicino, G., and Senatore, A.: Machine and Deep Learning algorithms to improve weather forecasts over a complex orography Mediterranean region, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9935, https://doi.org/10.5194/egusphere-egu24-9935, 2024.

On May 17, 2019, a rare severe convective weather occurred in Beijing, accompanied by local heavy rainstorm, hail, thunderstorm and gale. This severe convective weather occurred significantly earlier than normal years, bringing great challenge to the forecast. Using multiple observation data and radar four-dimensional variational assimilation products to analyze the triggering and development evolution of this severe convection. Under the conditions of no obvious weather scale system and local high potential unstable energy, the eastward advancement of the sea breeze front was the main factor triggering strong convection. As the northwest wind in the air increasing, the environmental conditions became stronger vertical wind shear, which was beneficial for the storm to maintain for a longer period of time. The supercell was the main cause of the convective weather. During the development of storms, they split into two parts and moved counterclockwise. The southern echo gradually weakened as it moved northward, while the northern echo moved southward, strengthening and developing into a super cell accompanied by a mesocyclone. The significant fluctuations in the height of the 0 ℃ layer within a small range resulted in different melting rates of hail during its descent, leading to the formation of spiky hail.

How to cite: Yu, B.: Analysis of a rare severe convective weather event in spring in Beijing of China, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10809, https://doi.org/10.5194/egusphere-egu24-10809, 2024.

EGU24-12420 | ECS | Orals | AS1.2

Can convection permitting forecasts solve the tropical African precipitation forecasting problem? 

Felix Rein, Andreas H. Fink, Tilmann Gneiting, Philippe Peyrille, James Warner, and Peter Knippertz

Forecasting precipitation over Africa, the largest landmass in the tropics, has been a long standing problem. The unique conditions of the West African monsoon result in large and long lasting mesoscale convective systems. Global numerical weather prediction (NWP) models have gridsizes in the 10s of kilometers, particular when run in ensemble mode, leaving convection to be parameterized. This often results in precipitation being forecast on too large scales, in the wrong places, and with too weak intensity, ultimately leading to little to no skill in tropical Africa.


It has been argued that convection permitting (CP) NWP forecasts would cure some of the problems described above but those have only recently become feasible in an operational setting, although ensembles are still deemed to be too expensive. Here, we systematically compare regional deterministic CP and global ensemble forecasts in the region over multiple rainy seasons for the first time. We analyze CP forecasts from AROME and Met Office Tropical African Model, and seven global ensemble forecasts from the TIGGE archive, both individually and as a multi-model ensemble. In order to create an uncertainty estimate, we create neighborhood ensembles from CP forecasts at surrounding grid points, which allows for a fair comparison to the ensembles and a probabilistic climatology. Considering both precipitation occurrence and amount, we use the Brier score (BS) and the continuous ranked probability score (CRPS), along with their decompositions in discrimination, miscalibration and uncertainty, for evaluation.


Using neighborhood methods, deterministic forecasts are turned into probabilistic forecasts, allowing a fair comparison with ensembles. All numerical forecasts benefit from Neighborhoods, improving their BS and CRPS in terms of both miscalibration and discrimination. We find all individual forecasts to have skill over most of tropical Africa, with some ensemble models lacking skill in some regions and the multi model showing the most overall skill. The CP forecasts TAM and AROME outperform non-CP forecasts mainly in the region of the little dry Season and the Soud. However, large areas of low skill in terms of CRPS remain and even with high resolution, numerical models still struggle to predict precipitation in tropical Africa. 

How to cite: Rein, F., Fink, A. H., Gneiting, T., Peyrille, P., Warner, J., and Knippertz, P.: Can convection permitting forecasts solve the tropical African precipitation forecasting problem?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12420, https://doi.org/10.5194/egusphere-egu24-12420, 2024.

EGU24-12855 | Posters on site | AS1.2

Project IMA: Lessons Learned from Building the Belgian Operational Seamless Ensemble Prediction System 

Lesley De Cruz, Michiel Van Ginderachter, Maarten Reyniers, Alex Deckmyn, Idir Dehmous, Simon De Kock, Wout Dewettinck, Ruben Imhoff, Esteban Montandon, and Ricardo Reinoso-Rondinel

 

In recent years, several national meteorological services (NMSs) have invested considerable resources in the development of a seamless prediction system: rapidly updating forecasts that integrate the latest observations, covering timescales from minutes to days or longer ahead (e.g. DWD's SINFONY; FMI's ULJAS, MetOffice's IMPROVER and Geosphere Austria's SAPHIR) [1]. This move was motivated mainly by rising expectations from end users such as hydrological services, local authorities, the renewable energy sector and the general public. The development of seamless prediction systems was made possible thanks to the increasing availability of high-resolution observations, continuing advances in numerical weather prediction (NWP) models, nowcasting algorithms, and improved strategies to combine multiple information sources optimally. Moreover, the rise of AI/ML techniques in forecasting and nowcasting can further reduce the computational cost to generate frequently updating seamless operational forecast products.

 

We present the journey of building the Belgian seamless prediction system at the Royal Meteorological Institute of Belgium, with the working title "Project IMA". IMA uses both the deterministic INCA-BE and the probabilistic pysteps-BE systems to combine nowcasts with the ALARO and AROME configurations of the ACCORD NWP model. In the lessons learned along the way, we focus on what is often omitted, moving from research to operations, and integrating what we learn from operations back into research. We discuss the benefits of integrating new developments within the free and open-source software (FOSS) pysteps [2]. Our experience shows that using and contributing to FOSS not only leads to more transparency and reproducible, open science; it also enhances international collaboration and can benefit other users, including developing countries, bringing us a step closer to the ambitious goal of Early Warnings for All by 2027 [3].

 

References

 

[1] Bojinski, Stephan, et al. "Towards nowcasting in Europe in 2030." Meteorological Applications 30.4 (2023): e2124.

[2] Imhoff, Ruben O., et al. "Scale‐dependent blending of ensemble rainfall nowcasts and numerical weather prediction in the open‐source pysteps library." Quarterly Journal of the Royal Meteorological Society 149.753 (2023): 1335-1364.

[3] WMO, "Early warnings for all: Executive action plan 2023-2027", 8 Nov 2022,  https://www.preventionweb.net/quick/75125.

How to cite: De Cruz, L., Van Ginderachter, M., Reyniers, M., Deckmyn, A., Dehmous, I., De Kock, S., Dewettinck, W., Imhoff, R., Montandon, E., and Reinoso-Rondinel, R.: Project IMA: Lessons Learned from Building the Belgian Operational Seamless Ensemble Prediction System, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12855, https://doi.org/10.5194/egusphere-egu24-12855, 2024.

Moderate to heavy rain produced by slantwise ascent of moist air above the cold high is prevalent in cold season in East China. The slantwise ascent is usually characterized by a southwest moist flow aroused by the so-called southern branch trough of 500hPa level to the south of the Qinghai-Tibet Plateau, while the cold high is usually formed by cold air damming, which is familiar to weather forecasters due to topographic feature of East China. The routine short-range forecast skill for this kind of precipitation of weather forecasters is usually limited by model performance. Through large sample model verification, our study indicates that, for the rainfall produced by southwesterly moist flow ascending above the cold high, the ECMWF model always underestimates the rainfall amount on the northeastern part of the rainfall belt, which could be taken as a systematic bias of the state-of-the-art global model. Our case studies indicate that the underestimation of rainfall amount is related to the weaker slant ascent of moist southwest flow forecast by ECMWF model than observation or reanalysis. The southwest flow above the northeastern flow induced by the cold high forms strong wind shear and warm-moist advection, which favors the occurrence of conditional symmetric instability producing strong slantwise ascent not well reflected by global model.

How to cite: Hu, N. and Fu, J.: Investigating Model Forecast Bias for Rainfall Produced by Slantwise Ascent above Cold High, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13809, https://doi.org/10.5194/egusphere-egu24-13809, 2024.

EGU24-13853 | Orals | AS1.2 | Highlight

A Research Agenda for the Evaluation of AI-Based Weather Forecasting Models 

Imme Ebert-Uphoff, Jebb Q. Stewart, and Jacob T. Radford and the CIRA-NOAA team

Over the past few years purely AI-driven global weather forecasting models have emerged that show increasingly impressive skill, raising the question whether AI models might soon compete with NWP models for selected forecasting tasks. At this point these AI-based models are still in the proof-of-concept stage and not ready to be used for operational forecasting, but entirely new AI-models emerge every 2-3 months, with rapidly increasing abilities. Furthermore, many of these models are orders of magnitude faster than NWP models and can run on modest computational resources enabling repeatable on-demand forecasts competitive with NWP. The low computational cost enables the creation of very large ensembles, which better represent the tails of the forecast distribution, which, if an ensemble is well calibrated, allows for better forecasting of rare and extreme events.

However, these AI-based weather forecasting models have not yet been rigorously tested by the meteorological community, and their utility to operational forecasters is unknown. In this presentation we propose several studies to address the above issues, grouped into two central foci:

(1) Nature of AI models: AI-based models have very different characteristics from NWP models. Thus, in addition to applying evaluation procedures developed for NWP models, we need to develop procedures that test for AI-specific weaknesses. For example, NWP models and their physics backbone guarantee certain properties - such as dynamic coupling between fields - that AI-based models are not required to uphold. Developing suitable tests is based on a fundamental understanding of the AI-based models.

(2) Forecaster Perspective: Evaluation of weather forecasting models should be performed with respect to particular applications of weather forecasts, and it is critical to have research meteorologists and operational forecasters involved in the evaluation process. Our initial evaluation of AI-based models in CIRA weather briefings revealed that these models have characteristics that make interpretation of their forecasts fundamentally different from the physics-based NWP model predictions meteorologists are familiar with. For example, the increasing “blurriness” of AI-based predictions with longer lead times is not a reflection of weaker atmospheric circulations, but rather a reflection of uncertainty. Evaluations aimed at specific meteorological phenomena and atmospheric processes will allow the community to make informed decisions in the future regarding in what environments and for which applications AI-based weather forecasting models may be safe and beneficial to use.

In summary, AI-based weather forecasts have different characteristics from familiar dynamically-based forecasts, and it is thus important to have a robust research plan to evaluate many different characteristics of the models in order to provide guidelines to operational forecasters and feedback to model developers. In this abstract we propose a number of characteristics to evaluate, present results we already obtained, and suggest a research plan for future work.

How to cite: Ebert-Uphoff, I., Stewart, J. Q., and Radford, J. T. and the CIRA-NOAA team: A Research Agenda for the Evaluation of AI-Based Weather Forecasting Models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13853, https://doi.org/10.5194/egusphere-egu24-13853, 2024.

Large-eddy simulations of an idealized tropical cyclone (TC) were conducted as benchmarks to provide statistical information about subgrid convective clouds at a convection-permitting resolution over a TC convection system in different stages. The focus was on the vertical and spatial distributions of the subgrid cloud and associated mass flux that need to be parameterized in convection-permitting models. Results showed that the characteristics of the subgrid clouds varied significantly in various parts of the TC convection system. Statistical analysis revealed that the subgrid clouds were mainly located in the lower troposphere and exhibited shallow vertical extents of less than 4 km. The subgrid clouds were also classified into various cloud regimes according to the maximum mass flux height. Local subgrid clouds differed in mass-flux profile shape and magnitude at various regimes in the TC convection system.

How to cite: Zhang, X. and Bao, J.-W.: Statistics of the Subgrid Cloud of an Idealized Tropical Cyclone at Convection-Permitting Resolution, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14232, https://doi.org/10.5194/egusphere-egu24-14232, 2024.

EGU24-14541 | Posters on site | AS1.2

Status and Plan of Standard Verification System for the NWP model in Korea Meteorological Administration 

Sora Park, Hyeja Park, Haejin Lee, Saem Song, Jong-Chul Ha, and Young Cheol Kwon

The Korea Meteorological Administration (KMA) has established and operated a standard verification system of the operational NWP models to evaluate the predictive performance of NWP model and compare them with other NWP models operated by domestic and foreign organization. This secures the objectivity of the verification results by applying the verification standards (WMO-No.485) presented by World Meteorological Organization (WMO), and being able to compare the performance with the numerical forecasting models of other institutions under the same conditions. The NWP models to be verified is a global, a regional, very short-range, and an ensemble prediction system and verification against analyses and observations are performed twice a day (00 UTC, 12 UTC). In addition to standard verification, precipitation, typhoon and various verification indexes (CBS index, KMA index, jumpiness index) are verified and used to evaluate the utilization of NWP models. The Korea Integrated Model (KIM), which is developed for Korea’s own NWP model, has been in operation since April 2020. Since the start of operation, the RMSE of 500hPa geopotential height (in Northern Hemisphere) has decreased every year, showing that forecast performance is improving. In addition, it can be seen that the 72-hour prediction accuracy for 12-hour accumulated precipitation (1.0 mm or more) in the Korean Peninsula area (75 ASOS stations) is also improving. As such, this study intends to discuss the predictive performance of the numerical forecast model based on the standard verification system and plans to improve the verification system in the future. 

How to cite: Park, S., Park, H., Lee, H., Song, S., Ha, J.-C., and Kwon, Y. C.: Status and Plan of Standard Verification System for the NWP model in Korea Meteorological Administration, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14541, https://doi.org/10.5194/egusphere-egu24-14541, 2024.

EGU24-15431 | ECS | Orals | AS1.2 | Highlight

Nowcasting of extreme precipitation events: performance assessment of Generative Deep Learning methods 

Gabriele Franch, Elena Tomasi, Rishabh Umesh Wanjari, and Marco Cristoforetti

Radar-based precipitation nowcasting is one of the most prominent applications of deep learning (DL) in weather forecasting. The accurate forecast of extreme precipitation events remains a significant challenge for deep learning models, primarily due to their complex dynamics and the scarcity of data on such events. In this work we present the application of the latest state-of-the-art generative architectures for radar-based nowcasting, focusing on extreme event forecasting performance. We analyze a declination for the nowcasting task of all the three main current architectural approaches for generative modeling, namely: Generative Adversarial Networks (DGMRs), Latent Diffusion (LDCast), and our novel proposed Transformer architecture (GPTCast). These models are trained on a comprehensive 1-km scale, 5-minute timestep radar precipitation dataset that integrates multiple radar data sources from the US, Germany, the UK, and France. To ensure a robust evaluation and to test the generalization ability of the models, we concentrate on a collection of out-of-domain extreme precipitation events over the Italian peninsula extracted from the last 5 years. This focus allows us to assess the improvements these techniques offer compared to extrapolation methods, evaluating continuous (MSE, MAE) and categorical scores (CSI, POD, FAR), ensemble reliability, uncertainty quantification, and warning lead time. Finally, we analyze the computational requirements of these new techniques and highlight the caveats that must be considered when operational usage of these methods is envisaged. 

How to cite: Franch, G., Tomasi, E., Wanjari, R. U., and Cristoforetti, M.: Nowcasting of extreme precipitation events: performance assessment of Generative Deep Learning methods, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15431, https://doi.org/10.5194/egusphere-egu24-15431, 2024.

EGU24-16617 | Posters on site | AS1.2

Forecasting extreme events with the crossing-point forecast  

Zied Ben Bouallegue

The crossing-point forecast (CPF) is a new type of ensemble-based forecast developed at the European Centre for Medium-Range Weather Forecasts. The crossing point refers to the intersection between the cumulative probability distribution of a forecast and the cumulative probability distribution of a model climatology. Originally, the CPF has emerged as a consistent forecast with the diagonal score, a weighted version of the continuous ranked probability score targeting high-impact events. Ranging between 0 and 1, the CPF can serve as an index for high-impact weather and thus directly be compared with the well-established extreme forecast index. The CPF is also interpretable in terms of a return period and conveys a sense of a “probabilistic worst-case scenario”.  Using a recent example of an extreme event affecting Europe, we illustrate and discuss the performance and specificities of this new type of forecast for extreme weather forecasting.

Ben Bouallegue, Z (2023).  Seamless prediction of high-impact weather events: a comparison of actionable forecasts. arXiv:2312.01673

How to cite: Ben Bouallegue, Z.: Forecasting extreme events with the crossing-point forecast , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16617, https://doi.org/10.5194/egusphere-egu24-16617, 2024.

EGU24-17158 | Orals | AS1.2 | Highlight

AIFS – ECMWF’s Data-Driven Probabilistic Forecasting  

Zied Ben Bouallegue, Mihai Alexe, Matthew Chantry, Mariana Clare, Jesper Dramsch, Simon Lang, Christian Lessig, Linus Magnusson, Ana Prieto Nemesio, Florian Pinault, Baudouin Raoult, and Steffen Tietsche

In just two years, the idea of an operational data-driven system for medium-range weather forecasting has been transformed from dream to very real possibility. This has occurred through a series of publications from innovators, which have rapidly improved deterministic forecast skill. Our own evaluation confirms that these forecasts have comparable deterministic skill to NWP models across a range of variables. However, on medium-range timescales probabilistic forecasting, typically achieved through ensembles, is key for providing actionable insights to users. ECMWF is building on top of these recent works to develop a probabilistic forecasting system, AIFS. We will showcase results from our progress towards this system and outline our roadmap to operationalisation.

How to cite: Ben Bouallegue, Z., Alexe, M., Chantry, M., Clare, M., Dramsch, J., Lang, S., Lessig, C., Magnusson, L., Prieto Nemesio, A., Pinault, F., Raoult, B., and Tietsche, S.: AIFS – ECMWF’s Data-Driven Probabilistic Forecasting , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17158, https://doi.org/10.5194/egusphere-egu24-17158, 2024.

The increasing integration of renewable energy resources to the national grids necessitates
accurate prediction of power generation from those sources in terms of secure operation of
electricity grid system and energy trading. Electricity generation of renewable energy power
plants such as wind and solar are inherently affected by weather conditions. The wind condition
particularly is affected by surface characteristics such as orography and vegetation, therefore it is
the one of the near surface atmospheric variables having the strongest local variability. The high-
resolution Numerical Weather Prediction (NWP) models are utilized to take the local conditions
into account. WRF model is the one of the most common NWP models having been widely
investigated by various researchers. On the other hand, The Model for Prediction Across Scales
(MPAS) is a relatively new NWP model utilizing non-uniform mesh structures, developed by the
National Center for Environmental Predictions (NCEP). However, there are limited studies in the
literature which compare the prediction performance of WRF and MPAS model in terms of
surface wind speed. This study evaluates the prediction accuracy of near surface wind of two
downscaled NWP models namely, WRF-ARW and MPAS. Both models are configured with
almost identical physics suites and initialized with 3 hourly 00-UTC initialization of Global
Forecast System (GFS) data. The model outputs are obtained at 10 minutes interval for 48 hours
horizon. Hourly averaged model results are compared with observations from 104 on-site
meteorological stations located in Turkiye having different complexity in terms of correlation
coefficient and RMSE.

How to cite: Yalcin, R. D., Yilmaz, M. T., and Yucel, İ.: Evaluation of the Impact of Uniform and Non-Uniform Resolution Implementations in Numerical Weather Prediction Models over the Accuracy of Short-Term Wind Prediction, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17339, https://doi.org/10.5194/egusphere-egu24-17339, 2024.

EGU24-18548 | ECS | Posters on site | AS1.2

Enhancing Regional NWP Model with GNSS Zenith Total Delay Assimilation: A WRF and WRFDA 3D-Var Approach in the Greater Region of Luxembourg 

Haseeb Ur Rehman, Felix Norman Teferle, Addissu Hunegnaw, Guy Schumann, Florian Zus, and Rohith Muraleedharan Thundathil

Compared to alluvial floods, flash or pluvial floods are difficult to predict because they result from intense and brief periods of extreme precipitation. Luxembourg has a history of being impacted by floods, with notable occurrences in January 2011, May 2016, December 2017, January 2018, February 2019, and February 2020. However, July 2021 stands out as the most severe flood year on record in the region. In this study we are aiming to develop, a high-resolution numerical weather prediction (NWP) model for effective local heavy rainfall prediction in a nowcasting scenario and provide real time for flood simulation. The modeling relies on the Weather Research and Forecasting (WRF) model, which incorporates local Global Navigation Satellite System (GNSS) data assimilation and local precipitation observations to simulate small-scale, high-intensity convective precipitation.

As part of this, we will also test run the LISFlood flood model in an operational inundation forecast mode, meaning that the flood model will be run with the WRF precipitation forecasts as inputs.

The WRF model was configured for the Greater Region, utilizing a horizontal grid resolution of 12 km and incorporating high-resolution static datasets. Meteorological data i.e. July 13 -14 2021, from the Global Forecast System (GFS) were employed in the model setup as initial boundary condition. Zenith Total Delay (ZTD) data collected from various GNSS stations (112) across Germany and Luxembourg were assimilated into the model. Additionally, observational datasets including Surface Synoptic Observations (SYNOP), Upper Air Data, Radiosonde measurements (TEMP), and Tropospheric Airborne Meteorological Data Reporting (TAMDAR) were assimilated. Following this integration, an sensitivity analysis of various meteorological parameters such as precipitation, surface temperature (T2), and relative humidity was performed.

 

Keywords: NWP, WRF, Flash flood, LISFlood, Weather forecast, High-Resolution, GNSS, ZTD

How to cite: Rehman, H. U., Teferle, F. N., Hunegnaw, A., Schumann, G., Zus, F., and Muraleedharan Thundathil, R.: Enhancing Regional NWP Model with GNSS Zenith Total Delay Assimilation: A WRF and WRFDA 3D-Var Approach in the Greater Region of Luxembourg, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18548, https://doi.org/10.5194/egusphere-egu24-18548, 2024.

EGU24-18938 | Posters on site | AS1.2

Forecasting tropical high-impact rainfall events using a hybrid statistical dynamical technique based on equatorial waves 

Samantha Ferrett, Gabriel Wolf, John Methven, Tom Frame, Christopher Holloway, Oscar Martinez-Alvarado, and Steve Woolnough

Recent work within the WCSSP FORSEA project and its successor FORWARDS has demonstrated that a hybrid statistical-dynamical forecasting technique combining model ensemble forecasts of equatorial waves with climatological rainfall statistics conditioned on wave phase and amplitude can provide additional skill in predicting high impact weather. The underlying rationale for the technique is twofold. Firstly that high impact rainfall events in the tropics are commonly associated with presence of equatorial waves; and secondly that while global models can adequately predict the evolution of dynamical structure of equatorial waves on time-scales of several days they do not predict the relationship between waves and rainfall well. In tests using the Met Office Global and Regional Forecasting System (MOGREPS) the hybrid forecast is found to outperform model rainfall forecasts from both the global and regional convection permitting versions of MOGREPS, however a weighted blend of the MOGREPS forecasts and the hybrid forecast was found to have the highest skill and further improvements in the method may be obtained by taking into consideration the effects of wave-superposition and interaction. To ascertain whether forecasts can be further improved by better predictions of wave amplitude and phase we compare to hypothetical best-case hybrid forecast computed using wave amplitudes and phases taken from reanalysis. This best-case scenario indicates that errors in forecasting all wave types diminish the hybrid forecast's skill, with the most significant reduction observed for Kelvin waves, suggesting that a significant improvement in the prediction of the propagation of equatorial waves would have a significant impact on rainfall prediction in the tropics. 

How to cite: Ferrett, S., Wolf, G., Methven, J., Frame, T., Holloway, C., Martinez-Alvarado, O., and Woolnough, S.: Forecasting tropical high-impact rainfall events using a hybrid statistical dynamical technique based on equatorial waves, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18938, https://doi.org/10.5194/egusphere-egu24-18938, 2024.

EGU24-19257 | ECS | Orals | AS1.2

Dynamic Locally Binned Density Loss 

Jan Prosi, Sebastian Otte, and Martin V. Butz

In the field of precipitation nowcasting recent deep learning models now outperform traditional approaches such as optical flow [1,2]. Despite their principled effectiveness, these models and their respective training setups suffer from particular shortcomings.  For instance, they often rely on pixel-wise losses, which lead to blurred predictions by which the model expresses its uncertainty [2]. Additionally, these losses can negatively impact training dynamics by overly penalizing small spatial or temporal discrepancies between predictions and actual observations, i.e., the double penalty problem [3]. Generative methods such as discriminative losses or diffusion models do not suffer from the blurring effect as much [1, 4]. However, training these methods is complicated because training success is highly sensitive to the network architecture as well as to the learning setup and its parameterization [5].

Previous research has shown that spatial verification methods such as the fractions skill score offer an easy-to-implement alternative to solve the problem of pixel-wise losses [6, 7]. However, the fact that each pixel within the neighborhood of a spatial kernel is weighted equally poses a limiting factor to their performance and potential. Inspired by theories of cognitive modeling and in relation to the fractions skill score loss, we introduce a dynamic locally binned density (DLBD) loss: Forecasting target is not the actual precipitation in a grid cell but a target distribution, which encodes the density of binned precipitation values in a locally weighted area of grid cells. The loss is then determined via the cross-entropy of the predicted and the target distribution. We show that our novel prediction loss avoids the double penalty problem.  It thus diminishes the negative impact of small spatial offsets. Moreover, it enables the learning model to gradually shift focus towards progressively more accurate predictions.

We achieve best performance by simultaneously training on multiple concurrent forecasting targets that cover different local extents. We schedule the weighting of the loss terms such that the focus shifts from larger to smaller neighborhoods over the course of training. This way, the DL model first learns density dynamics and basic precipitation shifts. Later, it focuses on minimizing small spatial deviations, tuning into the local dynamics towards the end of training.  Our DLBD loss is easy-to-implement and shows great performance improvements.  We thus believe that DLBD losses can also be used by other forecasting architectures where the current forecasting loss precludes smooth loss landscapes.

 


1: Leinonen et al. 2023: Latent diffusion models for generative precipitation nowcasting with accurate uncertainty quantification
2: Espeholt et al. 2022: Deep learning for twelve hour precipitation forecasts
3: Grilleland et al. 2009: Intercomparison of spatial forecast verification methods.
4: Ravuri et al. 2021: Skilful precipitation nowcasting using deep generative models of radar
5: Mescheder et al. 2018: Which training methods for GANs do actually converge?
6: Roberts et al. 2008: Scale-selective verification of rainfall accumulations from high resolution forecasts of convective events.
7: Lagerquist et al. 2022: Can we integrate spatial verification methods into neural-network loss functions for atmospheric science?

How to cite: Prosi, J., Otte, S., and Butz, M. V.: Dynamic Locally Binned Density Loss, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19257, https://doi.org/10.5194/egusphere-egu24-19257, 2024.

EGU24-19321 | ECS | Orals | AS1.2

Viability of satellite derived irradiance data for ML-based nowcasts 

Pascal Gfäller, Irene Schicker, and Petrina Papazek

Photovoltaic (PV) power production is increasingly becoming a central pillar in the shift to renewable power sources. The use of solar irradiance has great potential, as it is practically limitless and globally provides magnitudes more energy to the Earth than currently or foreseeable required. Solar irradiance as a power source does, however come with certain downsides. Besides the effects of seasonality and day-night-cycles on its usable potential, it´s broad use suffers mostly from uncertainty through its volatility. The actual extent of solar irradiance at the surface of the Earth is strongly influenced by a variety of atmospheric phenomena, most prominently clouds and atmospheric turbidity. The forecasting of near-future solar irradiance can thereby be beneficial in the estimation of PV power production in itself and with the goal of maintaining a stable equilibrium in electrical grids.

To achieve nowcasts on a larger grid scope, forecasting of solar irradiance from satellite data can substitute forecasting of power output for individual sites. Satellite data, in contrast to ground-based data sources or NWP model estimates, is less reliant on the proper workings of a wide range of externalities. General-purpose spatiotemporal neural networks can be adapted to this task and provide predictions within a very short timeframe, with no requirement of HPC-infrastructure. A sparse model relying on a single satellite-based data source has less points of failure that could affect its forecasting performance and can be very efficient, but this sparsity could also reduce the achievable predictive accuracy. Benefits of smaller and simpler forecasting pipelines therefore may need to be balanced with requirements in terms of accuracy.

To gather more meaningful and reliable results, a variety of spatiotemporal neural networks is implemented and tested to provide a more meaningful foundation. The models were selected and evaluated with respect to their different architectural patterns and designs, to get a notion of architectures beneficial to this task and achieve a more generalizable argument concerning the use satellite data as the sole basis of solar irradiance nowcasting.

In an attempt of improving the viability of satellite-based nowcasting a commonly occurring flaw in near-real-time satellite data sources, missing or skipped frames, solutions to mitigate issues in operational nowcasting are considered. In place of ad-hoc preprocessing such as interpolation of missing data frames, an attempt to condition the models to missing frames is made.

How to cite: Gfäller, P., Schicker, I., and Papazek, P.: Viability of satellite derived irradiance data for ML-based nowcasts, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19321, https://doi.org/10.5194/egusphere-egu24-19321, 2024.

EGU24-19377 | ECS | Posters on site | AS1.2

Advancing Spatiotemporal Rainfall Nowcasting through Deep Learning Techniques 

Ahmed Abdelhalim, Miguel Rico-Ramirez, Weiru Liu, and Dawei Han

For weather forecasters and hydrologists, predicting rainfall in the short term – minutes to a few hours – is crucial for a range of applications. While traditional nowcasting methods excel in operational settings, they face limitations in predicting convective storm formation and high-intensity events. Enter deep learning, a powerful tool transforming numerous fields. Convolutional neural networks, in particular, have shown promise in improving nowcasting accuracy. These networks can learn complex patterns and relationships within data, like the intricate tapestry of rainfall variations observed in historical radar sequences. However, capturing long-term dependencies in this data remains a challenge, resulting in fuzzy nowcasts and underestimating high-intensity events. This study proposes a novel deep learning model that goes beyond simple extrapolation, effectively capturing both the spatial correlations and temporal dependencies within rainfall data. Our hybrid convolutional neural network architecture tackles this challenge through three key components: Decoder & Encoder: These modules focus on unraveling the intricate spatial patterns of rainfall and a temporal Module to learn the subtle long-term evolutions and interactions between rain cells over time. By capturing these temporal dependencies, the model can produce more accurate forecasts. To evaluate the model performance, it is compared against both deep learning and optical flow baselines. This presentation will introduce the model and provide a summary of its performance in spatiotemporal rainfall nowcasting.

Keywords: deep learning; spatiotemporal encoding, rainfall nowcasting; radar; optical flow

How to cite: Abdelhalim, A., Rico-Ramirez, M., Liu, W., and Han, D.: Advancing Spatiotemporal Rainfall Nowcasting through Deep Learning Techniques, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19377, https://doi.org/10.5194/egusphere-egu24-19377, 2024.

EGU24-19699 | ECS | Posters on site | AS1.2

Evaluation of seamless forecasts for severe weather warnings  

Verena Bessenbacher, Jonas Bhend, Lea Beusch, Daniele Nerini, Colombe Siegenthaler, Christoph Spirig, and Lionel Moret

At MeteoSwiss, NWP and ML-based models are run operationally on a daily basis to provide weather forecasts and weather warnings for the general public. These forecasts come from various models that differ in lead times, initialization frequency, spatial resolution, and extents. We aim at combining those sources into a probabilistic, gridded weather forecast that is seamless in space and time. Creating a seamless forecast needs careful post-processing so as not to introduce cut-offs or unphysical behavior at the seams between the model runs. This includes using multiple forecast sources and forecast initializations (called lagged ensembles) and combining these using comprehensive blending methods. 

The first minimal viable product of a seamless forecast is currently being produced at MeteoSwiss, and will soon be available to the forecasters in real time. 

We evaluate the merit of these forecasts in terms of warning thresholds for rain and wind gusts. To do so, we compare reforecasts and observations from ground stations as well as rain radar observations from a set of past severe weather events over Switzerland. We benchmark the seamless forecast with individual forecast sources and post-processed products to evaluate the added value of seamlessly combining different forecast sources into one blended product. We furthermore plan to compare different methods for blending between sources soon.

How to cite: Bessenbacher, V., Bhend, J., Beusch, L., Nerini, D., Siegenthaler, C., Spirig, C., and Moret, L.: Evaluation of seamless forecasts for severe weather warnings , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19699, https://doi.org/10.5194/egusphere-egu24-19699, 2024.

Integrating the hybrid and multiscale analyses and the parallel computation is necessary for current data assimilation schemes. A local data assimilation method, Local DA, is designed to fulfill these needs. This algorithm follows the grid-independent framework of the local ensemble transform Kalman filter (LETKF) and is more flexible in hybrid analysis than the LETKF. Local DA employs an explicitly computed background error correlation matrix of model variables mapped to observed grid points/columns. This matrix allows Local DA to calculate static covariance with a preset correlation function. It also allows using the conjugate gradient (CG) method to solve the cost function and allows performing localization in model space, observation space, or both spaces (double-space localization). The Local DA performance is evaluated with a simulated multiscale observation network that includes sounding, wind profiler, precipitable water vapor, and radar observations. In the presence of a small-size time-lagged ensemble, Local DA can produce a small analysis error by combining multiscale hybrid covariance and double-space localization. The multiscale covariance is computed using error samples decomposed into several scales and independently assigning the localization radius for each scale. Multiscale covariance is conducive to error reduction, especially at a small scale. The results further indicate that applying the CG method for each local analysis does not result in a discontinuity issue. The wall clock time of Local DA implemented in parallel is halved as the number of cores doubles, indicating a reasonable parallel computational efficiency of Local DA.

How to cite: Wang, S. and Qiao, X.: A Local Data Assimilation Method (Local DA v1.0) and its Application in a Simulated Typhoon Case, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21770, https://doi.org/10.5194/egusphere-egu24-21770, 2024.

EGU24-21772 | Orals | AS1.2

Calibration of Convective-scale Hourly Precipitation Based on the Frequency-Matching Method 

Xiaoshi Qiao, Shizhang Wang, and Mingjian Zeng

Calibration of convective-scale hourly precipitation based on the frequency-matching method was carried on using CMPASS observation and CMA-MESO 3km forecast data. The character of hourly precipitation bias was studied.The effect of frequency-matching method (FMM) on the bias correction of CMA-MESO 3km hourly precipitation forecasts was analyzed. In the bias characteristic analysis, the differences in precipitation intensity in different regions of the country and the differences in precipitation in different months were considered. The whole country was divided into 7 sub-regions for monthly analysis. In the bias correction based on the frequency-matching method, the daily variations of precipitation bias and the impact of increasing and decreasing precipitation values on the corrected precipitation scores were analyzed. The results show that CMA-MESO 3km forecasts have a wet bias in light rainfall in the cold season, while a dry bias dominates in moderate to heavy rainfall. In the warm season, except for the Tibet region, the hourly precipitation forecast bias of CMA-MESO 3km shows significant daily variations, with more precipitation in the afternoon and less at night and in the morning, especially for heavy rainfall. Therefore, whether to consider the daily variations of precipitation bias in the use of FMM correction mainly reflects in the summer, especially at night and in the morning. Considering the daily variations of precipitation bias is beneficial to improving the forecast skills (TS scores) for nighttime and morning in the summer. Further analysis shows that the positive contribution of FMM correction to forecast scores mainly comes from the increase in frequency adjustment, especially for heavy rainfall. However, for light rainfall with wet bias, FMM often results in negative contribution. Therefore, FMM has a significant improvement effect on heavy rainfall in winter and nighttime rainfall in summer. The reason for this result is that the hit rate of CMA-MESO hourly precipitation forecast is low, and the false alarm rate is generally high, especially for heavy rainfall. In this case, the increased precipitation significantly increases the hit rate, while the false alarm rate increases to a lesser extent, thereby improving the precipitation scores.

How to cite: Qiao, X., Wang, S., and Zeng, M.: Calibration of Convective-scale Hourly Precipitation Based on the Frequency-Matching Method, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21772, https://doi.org/10.5194/egusphere-egu24-21772, 2024.

EGU24-375 | ECS | Posters on site | AS1.3

Subseasonal forecast of the MJO over Tropical America 

Luis Lazcano and Christian Dominguez

The Intraseasonal Oscillation (ISO) is commonly divided into two oscillations: the Madden-Julian Oscillation (MJO), which commonly occurs from November to April in winter, and the Boreal Summer Intraseasonal Oscillation (BSISO), which occurs from May to October. Recent studies have classified these two modes into different types using cluster analysis. Here, we analyze the oceanic and atmospheric variables from the reanalysis ERA5 to determine the influence of MJO and BSISO over the Tropical Americas during the period 1980-2018. We also evaluate how the models of the S2S represent the diverse types of MJO and BSISO by using the Pearson correlation, the root mean square error, and the Brier skill score.

The analysis shows that the four MJO types (slow, fast, stationary, and jumping) exhibit no convective signal over the Tropical Americas and the three BSISO types (canonical, north dipole, and east-expansion) have a strong signal on OLR, winds at 850 and 200 mb over the Tropical Americas. Considering the MJO types, the jumping and slow MJO reveal a small warm pool area, areas where the sea surface temperatures (SSTs) are higher than 28.5°C, over the Mexican Pacific, while the stationary and fast MJOs do not reach such high temperatures. Slow (fast) MJO has strong negative (positive) anomalies in SSTs over the central and Eastern Pacific Ocean. Considering the BSISO types, the canonical BSISO has the strongest westerly burst signal before the initiation of the BSISO events over the Maritime Continent, followed by easterly winds later. In contrast, the east-expansion BSISO shows weaker winds and negative OLR anomalies over Mexico. The northward dipole produces a small warm pool area over the Eastern Pacific Ocean when compared to the canonic and east expansion BSISO.

We conclude that the MJO and BSISO types have different physical mechanisms for modulating the intraseasonal changes in the atmospheric and oceanic variables over the Tropical Americas. We also find that the ECMWF model has the best correlation skill when compared to other models from the S2S project.

How to cite: Lazcano, L. and Dominguez, C.: Subseasonal forecast of the MJO over Tropical America, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-375, https://doi.org/10.5194/egusphere-egu24-375, 2024.

EGU24-572 | ECS | Posters on site | AS1.3

Intraseasonal Oscillation of Land Surface Moisture and  its role in the maintenance of land CTCZ during the active  phases of the Indian Summer Monsoon 

Pratibha Gautam, Rajib Chattopadhyay, Gill Martin, Susmitha Joseph, and Atul kumar Sahai

This study focuses on the soil moisture characteristics and its role in supporting the continental tropical convergence zone (CTCZ) during the active phase of the monsoon. Like rainfall, land surface parameters (soil moisture and evaporation) also show intraseasonal oscillation. Furthermore, the sub-seasonal and seasonal features of soil moisture are different from each other. During the summer monsoon season, the maximum soil moisture is found over western coastal regions, central parts of India, and the northeastern Indian subcontinent. However, during active phases of the monsoon (i.e., on sub-seasonal timescales), the maximum positive soil moisture anomaly was found in northern India. Land surface characteristics (soil moisture) also play a pre-conditioning role during active phases of the monsoon over the monsoon core zone of India. When it is further divided into two boxes, the north monsoon core zone and the south monsoon core zone, it is found that the preconditioning depends on that region's soil type and climate classification. Also, we calculate the moist static energy (MSE) budget during the monsoon phases to show how soil moisture feedback affects the boundary layer MSE and rainfall. A similar analysis is applied to the model run, but it cannot show the realistic preconditioning role of soil moisture and its feedback on the rainfall as in observations. We conclude that to get proper feedback between soil moisture and precipitation during the active phase of the monsoon in the model, the pre-conditioning of soil moisture should be realistic.

How to cite: Gautam, P., Chattopadhyay, R., Martin, G., Joseph, S., and Sahai, A. K.: Intraseasonal Oscillation of Land Surface Moisture and  its role in the maintenance of land CTCZ during the active  phases of the Indian Summer Monsoon, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-572, https://doi.org/10.5194/egusphere-egu24-572, 2024.

Drought, an extreme meteorological phenomenon, has significant impacts on a country's social, economic, and environmental stability. Early prediction of drought is crucial to provide warning and preparedness measures. Sub-seasonal prediction, which encompasses a few weeks to a few months ahead, is a critical timescale with limited memory of initial conditions, and not significantly controlled by boundary conditions. Presently, dynamical models have drawn much attention in the sub-seasonal precipitation forecast, however, the accuracy in drought prediction remains low. Currently, various dynamical models such as North American Multi-Model Ensemble (NMME) provide sub-seasonal prediction of hydro-meteorological variables for the entire globe. The efficacy of NMME model output for sub-seasonal drought prediction has not been explored in India. Also, a comprehensive study regarding the inclusion of climate indices as potential predictors for S2S drought prediction is lacking in the literature. We have investigated the potentiality of NMME precipitation output for sub-seasonal drought prediction over India and found out that the NMME model output doesn’t show a reasonable S2S forecast for 3-months standardized precipitation index (SPI3). Further, the study utilized data-driven models such as auto-regression, support vector regression (SVR), XGboost, and recurrent neural network (RNN) with climatic indices and previous month lagged value as predictors to improve the prediction skill. The results show that statistical models are superior to dynamic models. Although the previous monthly data is adequate for lead 1 drought prediction for most of the grids over India, the inclusion of climatic oscillation information was found to be the potential predictor and necessary for higher lead predictions. For example, the western disturbance index helped predict droughts at 2-months lead for the Northwest region of India. Moreover, the wavelet-based post-processing technique has shown the potential to enhance drought predictions significantly. The outcomes of this study will provide an outlook for the sub-seasonal to seasonal drought prediction over India and aid in the improvement of decision-making.

How to cite: Singh, S. and Valiya Veetil, S.: Sub-seasonal to seasonal (S2S) prediction of droughts over India using different data-driven models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-975, https://doi.org/10.5194/egusphere-egu24-975, 2024.

Recent studies suggest that La Niña events can be classified into two categories: mega La Niña and equatorial La Niña. The understanding of the variations in boreal summer intraseasonal oscillation (BSISO) behaviors between such two conditions remains uncertain. Results in this work show during equatorial La Niña summers, in conjunction with the more adequate intraseasonal column-integrated moisture anomalies, the weaker intraseasonal outgoing longwave radiation anomalies are observed over the western North Pacific (WNP) at 3 pentads lag of the peak phase for the Maritime Continent (MC) BSISO events than during mega conditions. Such changes are closely linked with the different propagation features, specifically northwestward and northeastward propagations under mega and equatorial conditions respectively. The distinct propagations under these two conditions could be partly explained by the background column-integrated moisture anomalies. Under equatorial conditions, the less sufficient background moisture anomalies over the tropical western Pacific (WP), in comparison to mega conditions, suppress the activities of the BSISO and its northwestward propagation here. Meanwhile, the enhanced moisture anomalies over the northwestern MC and its surrounding area (NWMC) facilitate the northeastward propagation. Under mega conditions, the background moisture anomalies over the tropical WP are not significant. The southward moisture anomaly gradient over the NWMC hinders the meridional northward propagation and makes some BSISO activities move to the tropical WP region, performing the zonal westward propagation as a whole. The moisture budget and multi-scale interaction diagnoses also emphasize the significant role of the propagation change in the moisture tendency difference averaged over the WNP. Moreover, the extratropical circulation anomalies associated with the MC BSISO events are also discussed. These findings provide new insights into BSISO activity and offer potential improvements for subseasonal forecast.

How to cite: Cao, C. and Wu, Z.: Distinct changes in boreal summer intraseasonal oscillation over the western North Pacific under mega and equatorial La Niña conditions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1244, https://doi.org/10.5194/egusphere-egu24-1244, 2024.

Subseasonal prediction of extremes has emerged as a top forecast priority but remains a great challenge. In this work, we explored two physical modes controlling the subseasonal variation and prediction of land cold extremes over Eurasia: the so-called North Atlantic Oscillation (NAO) and the Eurasian Meridional Dipole mode (EMD). The ECMWF model has shown its skill in predicting the Eurasian land cold extremes 2-4 weeks in advance mainly because of the skillful prediction of NAO and EMD. Further, we separated these observed events into the good prediction and poor prediction groups for those two modes to reveal the potential factors influencing the subseasonal prediction of land cold extremes. It is found that the good prediction group has a stronger initial amplitude and longer persistence, while the poor prediction group has a relatively weaker initial amplitude but rapid intensification. For EMD, the predictability is mainly due to the skillful prediction of the Ural blocking which is further traced back to the stratospheric variations.  

How to cite: Xiang, B.: The window of opportunity for subseasonal land cold extreme prediction over Eurasia  , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1376, https://doi.org/10.5194/egusphere-egu24-1376, 2024.

EGU24-1548 | Posters on site | AS1.3

Prediction Skill and Practical Predictability Depending on the Initial Atmospheric States in S2S Forecasts 

Masaru Inatsu, Mio Matsueda, Naoto Nakano, and Sho Kawazoe

The hypothesis that predictability depends on the atmospheric state in the planetary-scale low-frequency variability in boreal winter was examined.We first computed six typical weather patterns from 500-hPa geopotential height anomalies in the Northern Hemisphere using self-organizing map (SOM) and k-clustering analysis. Next, using 11 models from the subseasonal-to-seasonal (S2S) operational and reforecast archive, we computed each model’s climatology as a function of lead time to evaluate model bias. Although the forecast bias depends on the model, it is consistently the largest when the forecast begins from the atmospheric state with a blocking-like pattern in the eastern North Pacific. Moreover, the ensemble-forecast spread based on S2S multimodel forecast data was compared with empirically estimated Fokker– Planck equation (FPE) parameters based on reanalysis data. The multimodel mean ensemble-forecast spread was correlated with the diffusion tensor norm; they are large for the cases when the atmospheric state started from a cluster with a blocking-like pattern. As the multimodel mean is expected to substantially reduce model biases and may approximate the predictability inherent in nature, we can summarize that the atmospheric state corresponding to the cluster was less predictable than others.

How to cite: Inatsu, M., Matsueda, M., Nakano, N., and Kawazoe, S.: Prediction Skill and Practical Predictability Depending on the Initial Atmospheric States in S2S Forecasts, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1548, https://doi.org/10.5194/egusphere-egu24-1548, 2024.

EGU24-1591 | ECS | Orals | AS1.3

Process-based analysis of the MJO phase speed error in the coupled NWP model of the UK Met Office: a two-way feedback between the MJO and the diurnal warm layers 

Eliza Karlowska, Adrian Matthews, Benjamin Webber, Tim Graham, and Prince Xavier

The diurnal cycle of SST (dSST) is influenced by the development of diurnal warm layers in the upper ocean. Observations show that the dSST rectifies intraseasonal SSTs, potentially leading to changes in intraseasonal weather patterns such as the Madden-Julian Oscillation (MJO). Here we analyze 15-day forecast composites of the coupled ocean-atmosphere and the atmosphere-only configurations of the Numerical Weather Prediction (NWP) models of the UK Met Office to show that a strong dSST in the coupled model leads to a faster MJO propagation compared with the atmosphere-only version of the model. A set of experiments using the coupled model was designed to reduce the strength of the dSST by imposing instant vertical mixing in the top 5 and 10 m of the ocean model. On a 15 lead-day time scale, weakening the dSST slows the MJO phase speed in the coupled model. On a 7 lead-day time scale, all coupled model runs display an underlying 5% increase in the MJO phase speed compared to the atmosphere-only model due to the presence of thermodynamic coupling unrelated to the dSST. The MJO phase speed increase due to the dSST is linearly related to the mean tropical dSST at lead day 1 in the coupled model. An additional 4% of the MJO phase speed increase between the control coupled model and the atmosphere-only model on a 7 lead-day timescale can be attributed to the presence of the dSST in the coupled model. Over 15 lead days, the coupled model produces a two-way feedback between the MJO and the dSST. The MJO conditions set the strength of the dSST in the coupled model. Consistent with observations, the dSST in the coupled model rectifies intraseasonal anomalies of SSTs such that stronger dSST leads to positive intraseasonal SST anomalies. The MJO convection response to these SST anomalies peaks 7 days later, and subsequently feeds back onto SST anomalies. The phase relationship between MJO convection, dSST and intraseasonal SST anomalies is consistent with the relationship between dSST and MJO propagation speed. Overall, our experiments demonstrate the importance of high vertical resolution of the upper ocean in predicting the eastward propagation of the MJO in an NWP setting, potentially creating repercussions for seasonal predictions and climate projections should this feedback be unrepresented in the models.

How to cite: Karlowska, E., Matthews, A., Webber, B., Graham, T., and Xavier, P.: Process-based analysis of the MJO phase speed error in the coupled NWP model of the UK Met Office: a two-way feedback between the MJO and the diurnal warm layers, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1591, https://doi.org/10.5194/egusphere-egu24-1591, 2024.

EGU24-1706 | ECS | Orals | AS1.3

Real-time subseasonal prediction of cold waves over India 

Raju Mandal, Susmitha Joseph, Atul Kumar Sahai, Avijit Dey, Phani Murali Krishna, Dushmanta Pattanaik, Manpreet Kaur, and Nirupam Karmakar

Cold wave (CW) events over India are usually observed during the boreal winter months, November to February. This study proposes an objective criterion using the actual, departure from normal and the percentile values of the daily gridded minimum temperature (Tmin) data for the monitoring of the CW events over the Indian region and also checks its usefulness in a multi-model ensemble extended range prediction system. The large-scale features associated with these CW events are also discussed.

The CW-prone region has been identified by utilizing this proposed criterion and considering the number of average CW days/year for the entire study period and recent decades. By calculating the standardized area-averaged (over the CW-prone region) Tmin anomalies time series, the CW events are identified from 1951 to 2022. Analyzing the temporal variability of these events, it is seen that there is no compromise in the occurrences of the CW events, even under the general warming scenarios. It is found that the long CW events (>7 days) are favoured by the La-Nina condition, and short CW events (≤7 days) are favoured by the neutral condition in the Pacific. Also, the blocking high to the northwest of Indian longitude with the very slow movement of the westerly trough to the east is found to be associated with the long CW events. In contrast, in the case of short events, the blocking high is not so significant. The multi-model ensemble prediction system is found to be reasonably skilful in predicting the CW events over the CW-prone region up to 2-3 weeks in advance with decreasing confidence in longer leads. Based on the forecast verifications, it is noticed that this forecasting system has a remarkable strength to provide an overall indication about the forthcoming CW events with sufficient lead time despite its uncertainties in space and time. 

How to cite: Mandal, R., Joseph, S., Sahai, A. K., Dey, A., Krishna, P. M., Pattanaik, D., Kaur, M., and Karmakar, N.: Real-time subseasonal prediction of cold waves over India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1706, https://doi.org/10.5194/egusphere-egu24-1706, 2024.

EGU24-2747 | Orals | AS1.3

Development of a Multi-physics Multi-ensemble Subseasonal Prediction System and its Real-time Performance during Contrasting Indian monsoons 

Susmitha Joseph, Avijit Dey, Raju Mandal, Mahesh Kalshetti, Ravuri Phani, Shubham Waje, and Atul Sahai

Subseasonal predictions with a time scale of 2-4 weeks, which fills the gap between the weather and seasonal forecasts, are limited by the uncertainties arising from the initial conditions as well as the model physics. Therefore, to develop an efficient subseasonal prediction system, both these uncertainties need to be addressed. With this background, a multi-physics multi-ensemble approach has been adopted to develop a competent second-generation subseasonal prediction system at the Indian Institute of Tropical Meteorology (IITM), Pune, India. The first-generation prediction system developed at IITM is run operationally at the India Meteorological Department and has useful skills for up to two weeks.

A combination of physics perturbations and initial condition perturbations with a total of 18 ensemble members is present in the system. This system has been experimentally run since May 2022. The hindcast runs during 2003-2018 are also made on-the-fly. The initial results indicate a considerable improvement in the forecast skill compared to its predecessor and have reasonable deterministic prediction skill for up to three weeks. The system could provide skilful prediction of the subseasonal variations during the two contrasting monsoon seasons 2022 (above normal) and 2023 (below normal) 2-3 weeks in advance.

How to cite: Joseph, S., Dey, A., Mandal, R., Kalshetti, M., Phani, R., Waje, S., and Sahai, A.: Development of a Multi-physics Multi-ensemble Subseasonal Prediction System and its Real-time Performance during Contrasting Indian monsoons, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2747, https://doi.org/10.5194/egusphere-egu24-2747, 2024.

This study investigates the influence of the boreal summer intraseasonal oscillation (BSISO) on 10-30-day summer rainfall anomalies in Southwestern China (SWC) under the effects of Qinghai-Tibetan Plateau monsoon (QTPM) based on ERA5 reanalysis data and CN05.1 precipitation in 1981-2018. The results show that the 10-30-day rainfall anomalies in SWC have significant and joint feedback to variation of the second component of BSISO (BSISO2) and QTPM at lagging strong (weak) BSISO events by 0-12 days. Their lagged causal linkage and corresponding physical processes have been revealed by causal effect networks and composite analyses, which are most significant at 4-day and 12-day lag. Simultaneously, BSISO2 can induce wetter 10-30-day rainfall over southern SWC by motivating water vapor transport from the Bay of Bengal towards Yunnan province. More importantly, BSISO2 can modulate a northwest-propagating wave train from the western north Pacific towards SWC at the upper troposphere by vertical wave energy transport, which blocks the wave train propagating from the Lake Balkhash to east China–Japan most significantly at a 4-day lag and leads to drier eastern SWC. The process can be influenced by QTPM significantly which leads to the response of 10-30-day rainfall over SWC with lags of 0-12 days. Specifically, same-phase QTPM can trigger more active wave train propagation from high-latitude while opposite-phase QTPM enhances the low-latitude wave energy transport. The interference then facilitates baroclinic structure over eastern SWC at lagging 12 days with positive precipitation anomalies for same-phase events and negative precipitation for opposite-phase events.

How to cite: Yang, L., Chen, H., and Wang, S.: The joint effects of the boreal summer intraseasonal oscillation and Qinghai-Tibetan Plateau monsoon on the precipitation over Southwestern China , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2784, https://doi.org/10.5194/egusphere-egu24-2784, 2024.

EGU24-2862 | ECS | Posters on site | AS1.3

Robust Relationship between Mean State Moisture and Interannual MJO Activity in Observations and CMIP6 Models 

Daehyun Kang, Daehyun Kim, and Seon-Yu Kang

The Madden-Julian Oscillation (MJO) is the dominant intraseasonal variability of eastward propagating atmospheric disturbances in the tropics. From its vast impacts on the sub-seasonal extreme events and predictability, the mean states controlling the MJO activity have been investigated. For example, the robust relationship between the Quasi-Biennial Oscillation (QBO) and the MJO has been suggested in the past several years. In the easterly QBO winters, the MJO exhibits stronger activity than the westerly QBO winters. 
Our study suggests another crucial factor that affects the MJO: a meridional humidity gradient of the atmospheric column in the vicinity of the Maritime Continent. With the change in the shape of the column humidity distribution, MJO variance shows a robust interannual modulation regardless of the QBO. The northward (southward) extension of the moisture increases (decreases) the mean state meridional humidity gradient, which leads to MJO development (decay) over the MC with increasing (decreasing) horizontal moisture advection. This robust relationship between mean state humidity and MJO activity is investigated in the CMIP6 models as two aspects: i) interannual variation of MJO and ii) future change in MJO. Both simulated MJO activities are largely affected by the mean state MHG, supporting the robust role of mean state moisture on the MJO shown in the observations. The results of this study provide a further understanding of seasonal MJO activity and sub-seasonal predictability.MJO activity and sub-seasonal predictability.

How to cite: Kang, D., Kim, D., and Kang, S.-Y.: Robust Relationship between Mean State Moisture and Interannual MJO Activity in Observations and CMIP6 Models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2862, https://doi.org/10.5194/egusphere-egu24-2862, 2024.

EGU24-2890 | ECS | Orals | AS1.3

Influence of Arctic sea ice concentration on extreme Ural blocking predictability in subseasonal timescales 

Guokun Dai, Mu Mu, Xueying Ma, and Yangjiayi Gao

Utilizing the Community Atmospheric Model version 4, the influence of Arctic sea ice concentration (SIC) on the predictability of the Ural Blocking (UB) in subseasonal timescale is investigated. Taking the zonal flows as the reference states, the optimal Arctic SIC perturbations that trigger zonal flows into UB events on subseasonal timescale are obtained with the conditional nonlinear optimal perturbation (CNOP) approach. The numerical results show that the Arctic SIC decline in the Greenland, Barents and Okhotsk Seas can trigger zonal flows into UB events on a timescale of four pentads (20 days). Further diagnosis shows that the SIC decline in these regions locally warms the low troposphere via diabatic processes in the first pentad. Then, dynamic processes, such as temperature advection, modulate the temperature in the middle troposphere and weaken the meridional temperature gradient between the Arctic and mid-latitudes upstream of the Ural sector. The weakened meridional temperature gradient further decelerates the background zonal flow near the Ural sector and triggers UB formation in four pentads. After that, the optimal Arctic SIC perturbations that have great influences on subseasonal UB predictions are also obtained with CNOP approach. It is found that SIC increase in the Greenland Sea, Barents Sea, and Okhotsk Sea would weaken the UB intensity while SIC decline in these regions would strengthen it. Further diagnoses show that the physical mechanisms are similar to those triggering UB formation. Moreover, utilizing the observing system simulation experiments, it is shown that targeted observations in the Barents Sea, Greenland Sea, and Okhotsk Sea can remarkably improve the prediction skills of UB in the fourth pentad. Numerical results show that targeted observations have a positive effect on 75% of 160 experiment members, reduce 35% forecast errors of the fourth pentad mean blocking index, and perform even better when the original forecast errors are greater. Further diagnosis shows that the improvement is related to the well-described westerly winds in the Ural region and its adjacent regions, corresponding to the more skillful predictions of blocking circulations. The above results supply a theoretical base for the design of Arctic SIC observations and more skillful subseasonal predictions for mid-latitude extreme weather.

How to cite: Dai, G., Mu, M., Ma, X., and Gao, Y.: Influence of Arctic sea ice concentration on extreme Ural blocking predictability in subseasonal timescales, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2890, https://doi.org/10.5194/egusphere-egu24-2890, 2024.

EGU24-3148 | Posters on site | AS1.3

Subseasonal Warming of Surface Soil Enhances Precipitation Over the Eastern Tibetan Plateau in Early Summer 

Xin Qi, Jing Yang, Yongkang Xue, Qing Bao, Guoxiong Wu, and Duoying Ji

The precipitation over the eastern Tibetan Plateau (ETP, here defined as 29°–38°N, 91°–103°E) usually exhibits significant subseasonal variation during boreal summer. As the hot spot of land-air interaction, the influences of ETP surface soil temperature (Tsoil) on the local precipitation through subseasonal land-air interaction are still unclear but urgently needed for improving subseasonal prediction. Based on station and reanalysis datasets of 1979–2018, this study identifies the evident quasi-biweekly (QBW) (9–30 days) periodic signal of ETP surface Tsoilvariation during the early summer (May–June), which results from the anomalies of southeastward propagating mid-latitude QBW waves in the mid-to-upper troposphere. The observational results further show that the maximum positive anomaly of precipitation over the ETP lags the warmest surface Tsoil by one phase at the QBW timescale, indicating that the warming surface Tsoil could enhance the subseasonal precipitation. The numerical experiments using the WRF model further demonstrate the effect of warming surface Tsoil  on enhancing the local cyclonic and precipitation anomaly through increasing upward sensible heat flux, the ascending motion, and water vapor convergence at the QBW timescale. In contrast, the effect of soil moisture over the ETP is much weaker than Tsoil  at the subseasonal timescale. This study confirms the importance of surface Tsoil over the ETP in regulating the precipitation intensity, which suggests better simulating the land thermal feedback is crucial for improving the subseasonal prediction.

How to cite: Qi, X., Yang, J., Xue, Y., Bao, Q., Wu, G., and Ji, D.: Subseasonal Warming of Surface Soil Enhances Precipitation Over the Eastern Tibetan Plateau in Early Summer, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3148, https://doi.org/10.5194/egusphere-egu24-3148, 2024.

Global warming is accelerating drought onset, causing more frequent flash drought events. These events occur at the subseasonal timescale in which rapid decreases in root-zone soil moisture (RZSM) increase risks of crop failure, wildfire, and heat stress globally. However, forecasting soil moisture and flash droughts at lead times beyond 2 weeks remains a significant challenge. Recently, machine learning methods with historical reanalysis data have shown improved forecast accuracy compared to state-of-the-art numerical weather prediction methods, but they can only produce skillful forecast within 10 days. Here we show that a convergence forecast model combining a deep learning approach with subseasonal retrospective forecasts (reforecast) from numerical models produces skillful subseasonal soil moisture and flash drought forecasts at lead times beyond 2 weeks. We train a deep learning architecture on combinations of reanalysis and reforecast from 2000 to 2015 and validate results during the testing period from 2018 to 2019. The subseasonal forecast skill of soil moisture of the convergence forecast model is much higher than those of current state-of-the-art numerical forecast models, deep learning bias corrected numerical forecast models, or the reanalysis-based deep learning models, which showed no skill after 2 weeks lead time. The convergence model also showed significantly improved performance for predicting flash droughts compared to the original or deep learning bias corrected numerical forecast models or reanalysis-based deep learning models.  A permutation analysis indicates that reanalysis precursors and soil moisture reforecast at lead times within 2 weeks both contribute significantly to the forecast skill at longer lead times. The convergence forecast model provides accurate and efficient subseasonal soil moisture and flash drought forecasting and is promising for accurately forecasting key variables and extreme events at the subseasonal timescale.

How to cite: Lesinger, K. and Tian, D.: Converging Deep Learning and Numerical Prediction for Skillful Subseasonal Soil Moisture and Flash Drought Forecasting, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3194, https://doi.org/10.5194/egusphere-egu24-3194, 2024.

EGU24-3242 | ECS | Orals | AS1.3 | Highlight

Predicting Forest Damage in Europe: A Subseasonal-to-Seasonal Forecasting Approach for Hydro-meteorological Drivers 

Pauline Rivoire, Sonia Dupuis, Antoine Guisan, and Pascal Vittoz

Extreme meteorological events such as frost, heat, and drought can induce significant damage to vegetation and ecosystems. In particular, heat and drought events are projected to become more frequent in a changing climate. On the subseasonal-to-seasonal (S2S) forecasting timescale, skillful forecasts of hydro-meteorological hazards combined with targeted actions can prevent various vegetation damage and large-scale impacts (e.g. agriculture and food security, wildfire risk management, forest management,  biodiversity and flora protection,etc.).

We here focus on forest damage in Europe, defined as negative anomalies of the normalized difference vegetation index (NDVI). Compound drought and heat wave events are known to trigger low NDVI events in summer. A dry summer combined with warm and moist conditions during the previous winter can also have a negative impact. However, to our knowledge, there exists no comprehensive study of hydro-meteorological drivers triggering forest damage in Europe. Hence, the goal of our study is a) finding the optimal variables to predict summer forest damage in Europe, and b) assessing the S2S forecast skill of these variables. We develop an automated procedure to systematically identify hydro-meteorological conditions leading to forest damage, up to 18 months prior to occurrence. We train a model using AVHRR remote sensing observation of NDVI for the impact data, and ERA5 and ERA5-Land reanalysis datasets for the explicative variables. These variables include temperature, precipitation, dew point temperature, surface latent heat flux, soil moisture, and soil temperature. To bridge the research gap between the S2S forecasts of hydrometeorological variables and vegetation damage, we assess the forecast skill of variables from the S2S hindcast database of ECMWF identified as responsible for low NDVI events. The idea is to determine to what extent S2S models can predict conditions triggering forest damage, by identifying the sources of predictability or potential need for improvement.

How to cite: Rivoire, P., Dupuis, S., Guisan, A., and Vittoz, P.: Predicting Forest Damage in Europe: A Subseasonal-to-Seasonal Forecasting Approach for Hydro-meteorological Drivers, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3242, https://doi.org/10.5194/egusphere-egu24-3242, 2024.

EGU24-3737 | ECS | Posters on site | AS1.3 | Highlight

Arctic sea ice loss and La Niña as precursors of extreme East Asian cold winters 

Yeon-Soo Jang, Hyung-Gyu Lim, Sang-Yoon Jun, and Jong-Seong Kug

Despite current global warming due to increasing greenhouse gases, severe cold winters have devastated the East Asia in recent decades. Efforts are being made to predict cold events using dynamic models and physically-based statistical models. In this study, we explore the potential predictability of the East Asian winter surface temperature by establishing a multiple linear regression model based on three precursors of time-evolved preconditions: 1) autumn Arctic sea-ice loss, 2) northern Eurasian sea level pressure pattern, and 3) the El Niño-Southern Oscillation (ENSO). Reduced autumn Arctic sea-ice was favorable for extreme cold events in the East Asia. Furthermore, the autumn Arctic sea-ice loss was accompanied by cyclonic circulations over northern Eurasia in November, which could have led to cold anomalies over the East Asia in the late winter. The preconditioning deep convection in La Niña events is a well-known indicator of exerted atmospheric wave propagation, resulting in cold winters over the East Asia. We suggested here that by combining Arctic sea-ice, atmospheric circulations, and ENSO, the predictability of East Asian winter surface temperature variability could be improved.

How to cite: Jang, Y.-S., Lim, H.-G., Jun, S.-Y., and Kug, J.-S.: Arctic sea ice loss and La Niña as precursors of extreme East Asian cold winters, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3737, https://doi.org/10.5194/egusphere-egu24-3737, 2024.

EGU24-3796 | ECS | Posters on site | AS1.3 | Highlight

Targeted Observations on Arctic Sea Ice Concentration for Improving Extended-range Prediction of Ural Blocking 

Yangjiayi Gao, Mu Mu, and Guokun Dai

The predictability of certain extreme weather events can exceed the traditional two weeks by considering the boundary conditions. Targeted observations in sensitive areas on Arctic sea ice concentration (SIC) can improve the extended-range (4 pentads) forecast skills of long-lasting and strong Ural blocking (UB). The sensitive areas are determined based on the SIC optimally growing boundary errors, obtained by the conditional nonlinear optimal perturbation method. The sensitive areas are mainly located in the Barents Sea, Greenland Sea, and Okhotsk Sea. The results of observing system simulation experiments for 8 UB cases indicate that the targeted observations can remarkably improve the prediction skills of UB in the 4th pentad. Targeted observations have a positive effect on 75% of 160 experiment members, reduce 35% forecast errors of the 4th pentad mean blocking index, and perform even better when the original forecast errors are greater. Further diagnosis shows that targeted observations contribute to more accurate SIC boundary conditions in the Barents Sea, Greenland Sea, and Okhotsk Sea and reduce temperature errors in the lower and middle troposphere. It further results in well-described westerly winds in the Ural region and its adjacent regions, corresponding to the more skillful predictions of blocking circulations. The above results supply a theoretical base for the design of Arctic SIC observations and more skillful extended-range predictions for mid-latitude extreme weather.

How to cite: Gao, Y., Mu, M., and Dai, G.: Targeted Observations on Arctic Sea Ice Concentration for Improving Extended-range Prediction of Ural Blocking, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3796, https://doi.org/10.5194/egusphere-egu24-3796, 2024.

EGU24-3798 | ECS | Posters on site | AS1.3

The role of stratospheric processes in the trans-seasonal connection between spring and summer northern annular modes 

Xiran Xu, Lei Wang, Tao Wang, and Gang Chen

The summer northern annular mode (NAM) variability plays a crucial role in the summer climate variability and extremes of the Northern Hemisphere. In this study, we report a significant negative correlation between the March NAM and summer NAM during 1979–2022 and reveal the role of the spring stratosphere in this seasonal linkage. Particularly, it is found that the negative phase of March NAM features a strong meridional shear in the extended-North-Atlantic jet, which tends to generate planetary scale Rossby waves that propagate upward and poleward into the stratosphere. This increased stratospheric planetary wave activity in March transitions to weakened wave activity in May, leading to positive zonal wind anomalies in the polar stratosphere in May, extending downward to the troposphere in June and promoting the formation and persistence of positive summer NAM. The results provide both statistical and dynamical evidence for the role of the spring stratosphere in connecting the spring and summer circulation. 

How to cite: Xu, X., Wang, L., Wang, T., and Chen, G.: The role of stratospheric processes in the trans-seasonal connection between spring and summer northern annular modes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3798, https://doi.org/10.5194/egusphere-egu24-3798, 2024.

The main objective of this study is to assess typhoon precipitation forecast skill on the subseasonal timescale. The 20-year reforecasts from the ECMWF 46-day ensemble (ENS) are utilized to compare with gridded surface observations in Taiwan. The analysis focuses on the dates when typhoons affect Taiwan (117-129°E and 19-28°N). 15 ENS grids around Taiwan area are used with the grid size of 0.8 x 0.8 degree. Historical rainfall observations are provided by the Central Weather Administration (CWA), which the observations from the surface stations are interpolated into a resolution of 1km x 1km grid box. A comparison between the ENS forecast data and gridded CWA rainfall observations is performed by searching the optimal percentile rank (PR) of gridded CWA rainfall that has the smallest mean difference against the ENS data. The result reveals that the ENS can somewhat capture the rainfall contrast between the mountainous area and plain area, despite its relatively lower horizontal resolution. However, the difference between ENS rainfall forecasts and surface observations significantly increases for the forecasts beyond 72 hours, due to the model's coarser resolution and typhoon track forecast errors.

The ENS typhoon track forecast errors in weeks 1-4 are analyzed by comparing the ensemble vortex tracks with the JTWC best tracks. The track forecast error is decomposed into the along-track (AT) and cross-track (CT) components. The analysis result shows negative mean AT errors, indicating slower translation speed biases in the model. The mean AT errors could reach up to 400 km for the 168 h forecasts after TC formations.

Given the significant typhoon track forecast errors, using the raw ENS rainfall forecasts for the operational TC forecasting/outlook become challenging. In response, we have developed a statistical Quantitative Precipitation Forecast (QPF) model to predict typhoon rainfall, considering the track biases in the ENS forecasts. The forecast tools developed in this study will be integrated into CWA’s subseasonal typhoon forecast system to support water resources management and disaster risk reduction.

How to cite: Hsu, H.-Y. and Tsai, H.-C.: Subseasonal Typhoon Precipitation Forecast in Taiwan Area Using the ECMWF Reforecasts: Forecast Verification and Application, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4208, https://doi.org/10.5194/egusphere-egu24-4208, 2024.

Land surface processes are strongly associated with heat waves (HWs). However, how the uncertainties in land surface processes owing to inaccurate physical parameters influence subseasonal HW predictions has rarely been explored. To examine the impact of parameter errors of land surface processes on the uncertainty of subseasonal HW predictions, five strong and long-lasting HW events over the middle and lower reaches of the Yangtze River (MLYR) are investigated. Based on the Weather Research and Forecasting (WRF) model, the conditional nonlinear optimal perturbation related to parameters (CNOP-P) approach is employed to address the aforementioned issues.

Numerical results demonstrate that the CNOP-P type errors of physical parameters cause large prediction errors for five HW event onsets. Two types of CNOP-Ps are obtained for HW events, called the type-1 CNOP-P and the type-2 CNOP-P. The type-1 (type-2) CNOP-P causes an approximately 3 °C (2 °C) warm (cold) bias during the HW period. Surface sensible and latent heat flux errors, especially flux exchange between vegetation canopy and canopy air, provide considerable uncertainty in subseasonal HW predictions. The type-1 (type-2) CNOP-P exhibits an underestimation (overestimation) of transpiration. Furthermore, it should be noted that the type-1 CNOP-P results in a substantial difference in soil moisture, a phenomenon that is demonstrated to be challenging to observe in the type-2 CNOP-P. The results indicate that understanding vegetation-atmosphere dynamics is crucial for improving subseasonal HW predictions. Jointly lowering soil-atmosphere and vegetation-atmosphere uncertainty can notably improve subseasonal HW prediction skills.

How to cite: Zhang, Q., Mu, M., Sun, G., and Dai, G.: Impact of Uncertainties in Land Surface Processes on Subseasonal Predictability of Heat Waves Onset Over the Yangtze River Valley, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4245, https://doi.org/10.5194/egusphere-egu24-4245, 2024.

EGU24-4272 | Posters on site | AS1.3

Verifications of Week-1 to Week-4 Tropical Cyclone Forecasts in the Western North Pacific from the ECMWF 46-Day Ensemble 

Hsiao-Chung Tsai, Han-Yu Hsu, Tzu-Ting Lo, and Meng-Shih Chen

This study uses the ECMWF 46-day ensemble to evaluate the subseasonal forecasts of tropical cyclones (TCs) in the western North Pacific, including TC formations, tracks, intensity, and precipitation forecasts. TC formations and the subsequent tracks are objectively detected in both real-time forecasts and also the 20-year ECMWF reforecasts. Additionally, a spatial-temporal track clustering technique is utilized to group similar vortex tracks in the 101-member real-time forecasts for operational application. The forecast verification focuses on evaluating the influence of large-scale environmental factors on TC forecast skills during weeks 1-4, such as the Western North Pacific Summer Monsoon (WNPSM), Madden Julian Oscillation (MJO), and Boreal Summer Intraseasonal Oscillation (BSISO). The Precision-Recall (PR) curve is used to represent the imbalanced TC data instead of the Receiver Operating Characteristic (ROC) curve. Better TC forecast skills are observed if model initialized on MJO Phases 6 and 7 for the week-1 forecasts, and on MJO Phases 4 and 5 for the weeks 2 and 3 forecasts. Also, TC forecast skills are better if the cumulative percentage of the WNPSM index (Wang et al. 2001) is larger than 60%. This study also investigats the TC precipitation forecast skill around Taiwan area.

The evaluation results obtained from this study has been integrated into the TC Tracker 2.0 system developed by Central Weather Administration (CWA). The system can generate a "Subseasonal TC Threat Potential Forecast" product to assist in disaster mitigation and water resources management for the Water Resources Agency. More details about the subseasonal TC forecast verifications and applications will be presented in the meeting

How to cite: Tsai, H.-C., Hsu, H.-Y., Lo, T.-T., and Chen, M.-S.: Verifications of Week-1 to Week-4 Tropical Cyclone Forecasts in the Western North Pacific from the ECMWF 46-Day Ensemble, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4272, https://doi.org/10.5194/egusphere-egu24-4272, 2024.

EGU24-4665 | Orals | AS1.3 | Highlight

Evaluating Real-time Subseasonal to Seasonal Tropical Cyclone Prediction 

Xiaochun Wang and Frederic Vitart

The real-time WWRP/WCRP Subseasonal to Seasonal (S2S) Prediction Project Phase 2 database was used to evaluate the prediction skill of tropical cyclone from eleven forecasting systems for the North Western Pacific. The variable introduced to evaluate S2S tropical cyclone prediction is daily tropical cyclone probability, which is the occurrence probability of tropical cyclone within 500 km in one day. Using such a definition, the occurrence of tropical cyclone is a dichotomous event. The skill of S2S tropical cyclone prediction can be evaluated using debiased Brier Skill Score, which is the traditional Brier Skill Score with impact of forecast ensemble size removed. Sensitivity tests were conducted to analyze the influence of difference in temporal window and radius in the definition of daily tropical cyclone probability. It is demonstrated that though the daily tropical cyclone probability would vary with a changed radius and temporal window, the debiased Briere Skill Score does not change much since it is related with the ratio of mean error of model forecast and the mean error of a reference climatological forecast. The robustness of the prediction skill indicates the suitability of using the daily tropical cyclone probability and debiased Brier Skill Score to measure tropical cyclone prediction skill at S2S timescale. Compared with the prediction skill of the S2S Prediction Project Phase 1, the real-time S2S tropical cyclone prediction is improved for some forecast systems. Some early results by combining multi-model tropical cyclone forecasts to improve tropical cyclone prediction will also be presented.

How to cite: Wang, X. and Vitart, F.: Evaluating Real-time Subseasonal to Seasonal Tropical Cyclone Prediction, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4665, https://doi.org/10.5194/egusphere-egu24-4665, 2024.

EGU24-4955 | ECS | Posters on site | AS1.3

Subseasonal Predictability of Early and Late Summer Rainfall Over East Asia 

Xiaojing Li

Considering the significant differences in the rainfall characteristics over East Asia between the early [May–June (MJ)] and late [July–August (JA)] summer, this study investigates the subseasonal predictability of the rainfall over East Asia in early and late summer, respectively. Distinctions are obvious for both the spatial distribution of the prediction skill and the most predictable patterns, that is, the leading pattern of the average predictable time (APT1) between the MJ and JA rainfall. Further analysis found that the distinct APT1s of MJ and JA rainfall are attributable to their different predictability sources. The predictability of the MJ rainfall APT1 is mainly from the boreal intraseasonal oscillation signal, whereas that of the JA rainfall APT1 is provided by the Pacific–Japan teleconnection pattern. This study sheds light on the temporal variation of predictability sources of summer precipitation over East Asia, offering a possibility to improve the summer precipitation prediction skill over East Asia through separate predictions for early and late summer, respectively.

How to cite: Li, X.: Subseasonal Predictability of Early and Late Summer Rainfall Over East Asia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4955, https://doi.org/10.5194/egusphere-egu24-4955, 2024.

Summer monsoon precipitation over the Bay of Bengal (BoB) has pronounced intraseasonal variability (ISV), which has a close relationship to the local intraseasonal sea surface temperature (SST). Before heavy precipitation, intraseasonal SST in the BoB often has a warm anomaly and propagates northward, which drives the atmosphere and tends to trigger the convection. Besides the local air-sea interaction, the ISV of SST in the Arabian Sea (AS) also has an effect on the precipitation over the BoB. Results show that a prominent heavy precipitation usually occurs when the warm intraseasonal SST anomaly appears early in the AS and moves northward prior to that emerges in the BoB. The warm SST anomaly in the AS affects the sea level pressure and then trigger a southwestly wind anomaly in the center of AS. This wind anomaly promotes the wind convergence moving northward from the southern tip of Indian peninsula to the north India and northern BoB, which directly influence the vertical moisture advection and finally the precipitation. Understanding this process will be helpful to improve the predictive skill of the ISVs during the Indian Summer Monsoon.

How to cite: Xi, J.: Influence of Intraseasonal Variability of Sea Surface Temperature in the Arabian Sea on the Summer Monsoon Precipitation Over the Bay of Bengal, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5422, https://doi.org/10.5194/egusphere-egu24-5422, 2024.

EGU24-6229 | Orals | AS1.3

Decadal variability of the extratopical response to the MJO: AMV and PDO modulation in the UKESM climate model 

Adrian Matthews, Daniel Skinner, and David Stevens

The extratropical response to the Madden-Julian Oscillation (MJO) is modulated by two prominent modes of low-frequency sea surface temperature (SST) variability: the Atlantic Multidecadal Variability (AMV) and the Pacific Decadal Oscillation (PDO). Utilizing the UK Earth System Model (UKESM) 1100 year pre-industrial control simulation from CMIP6, this study offers a unique opportunity to explore decadal variability with an extensive dataset, surpassing the limitations of previous studies which focussed on reanalysis products.

The results underscore a statistically significant influence of both AMV and PDO on the extratropical response across all MJO phases. Non-linear interactions between the MJO teleconnection and SST forcing are observed prominently in the modification of the response to MJO phase 6 (enhanced convection over the western Pacific), with AMV+ and PDO+ background states amplifying distinct teleconnection patterns, notably the negative North Atlantic Oscillation (NAO-) and the deepened Aleutian Low responses, respectively. These changes are greater in magnitude than would be expected from the linear superposition of the individual atmospheric responses to the SST mode and the MJO. The amplification of the MJO phase 6 teleconnection to the North Atlantic aligns with prior research based on ERA5 reanalysis data.

While modulation of the response to MJO phase 3 (enhanced convection over the eastern Indian Ocean) is evident, it is less pronounced compared to phase 6, and the mechanisms via which it acts are less clear. Intriguingly, alterations in the teleconnection, such as a weaker Aleutian Low during PDO+, contradict the anticipated modulation. Since MJO phase 3 and PDO+ tend to weaken and strengthen the Aleutian Low, respectively, it would be reasonable to expect that these effects would cancel. Instead, the weakening of the Low after MJO phase 3 is increased during PDO+.

A possible mechanism for the modulation of the teleconnections is a linear superposition of Rossby wave modes excited by the MJO, contingent upon the SST state. In the case of MJO phase 6, this corresponds to an amplification of the existing modes, and hence of the expected response. For MJO phase 3, however, there is an indication that other Rossby wave modes may also be excited in certain SST states, leading to interference which is out of phase with the primary response.

Acknowledging the limitations of observational and reanalysis datasets, this study underscores the pivotal role of climate models in the effective study of decadal and multi-decadal variability. Importantly, the study has significant implications for extratropical forecasting over the coming decades. The modulation of the MJO teleconnection by AMV and PDO suggests modifications in predictability, crucial for refining forecasting techniques. Furthermore, these results provide a contextual foundation for studies examining MJO teleconnections in future climates, enabling a more accurate dissection of responses influenced by internal and anthropogenically forced variability.

How to cite: Matthews, A., Skinner, D., and Stevens, D.: Decadal variability of the extratopical response to the MJO: AMV and PDO modulation in the UKESM climate model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6229, https://doi.org/10.5194/egusphere-egu24-6229, 2024.

EGU24-6452 | Posters on site | AS1.3

Local and remote sources of error inMJO forecasts in the Navy ESPC  

Stephanie Rushley, Matthew Janiga, and Carolyn Reynolds

The Navy Earth System Prediction Capability (ESPC) is the Navy’s coupled ocean-atmosphere-sea ice model.  The current version of the Navy ESPC has 16 ensemble members and been operational since August 2020. The Navy ESPC has known biases in Madden-Julian Oscillation (MJO), which has a too strong amplitude and too fast propagation speed. During boreal winter, the MJO in the Navy ESPC is too strong due to biases in the vertical motion, which supports larger vertical moisture advection.  The MJO is too strong in this season due to excessive evaporation in the western Pacific supporting moistening to the east of the MJO convective center.  In this study, we examine the boreal winter MJO in the operational Navy ESPC ensemble.  We use process oriented diagnostics to explore the local and remote sources of biases that drive good and poor MJO forecasts. 

MJO forecasts are split into those that are well predicted and those that are poorly predicted.  Individual MJO events are tracked following Chikira (2014), using Hovmöllers of MJO filtered OLR averaged between 10N and 10S.  The MJO forecast performance is determined by comparing the forecasted MJO to the observed MJO based on the magnitude of the maximum amplitude of the MJO, the phase speed, duration of the event, and the location of the MJO convection.  Using the moisture mode framework, we examine the maintenance and propagation of moisture anomalies to identify how the local and remote sources of error affect MJO skill.  We use a moisture budget analysis to diagnose and understand the difference between the forecasts that performed well and those that performed poorly.  Additionally, we examine the effects that these forecast errors in the MJO have on extratropical cyclones, surface winds, and clouds in the Navy ESPC and how biases in the extratropics affect the skill of MJO-teleconnections.

How to cite: Rushley, S., Janiga, M., and Reynolds, C.: Local and remote sources of error inMJO forecasts in the Navy ESPC , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6452, https://doi.org/10.5194/egusphere-egu24-6452, 2024.

EGU24-6688 | Orals | AS1.3

Sources of S2S and MJO predictability 

Chidong Zhang

One main justification for subseasonal-to-seasonal (S2S) prediction is its identified sources of predictability. These sources include slowly varying phenomena, such as the MJO, stratospheric conditions, upper-ocean heat content, soil moisture, and sea ice. In practice, however, these presumed sources of S2S predictability have become the main targets of S2S prediction. For example, predicting the MJO, especially its propagation over the Indo-Pacific Maritime Continent, has been challenging. This raises a fundamental question: What are the predictability sources of the MJO? For global coupled prediction models, the primary sources of predictability are initial conditions and the governing laws. It is unclear, however, what elements in the initial conditions are more important to MJO prediction than others. It can be argued that the current practice of initializing forecasts using a single state of the system may not be optimal. Embedded initial conditions may provide an additional source of predictability that has yet to be fully explored.

How to cite: Zhang, C.: Sources of S2S and MJO predictability, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6688, https://doi.org/10.5194/egusphere-egu24-6688, 2024.

EGU24-7386 | Posters on site | AS1.3 | Highlight

Weather and Climate conditions over the Arctic and mid-latitude regions affecting air quality 

Jeong-Min Park, Dasom Lee, Kwanchul Kim, Seong-min Kim, Gahye Lee, and Kwon Ho Lee

Recently, it has been noticed that weather and climate changes over the Arctic and mid-latitude regions may have influenced the particulate matter concentrations and haze over East Asia. Among the various weather and climate conditions and climate indices could be an important factor in affecting variation of particulate matter (PM) concentrations. In this study, we examined the long-term changes in the sea ice cover, soil moisture, near-surface temperature and its link with the lower atmospheric circulation over Arctic and mid-latitude from 1950 to 2022, using modern reanalysis datasets. Long-term analyses show negative trends in sea ice cover over the Arctic and positive trends in near-surface temperature and SST, implying atmospheric stagnant and variation of PM concentration. Additionally, climate indices, related to teleconnection between the Arctic region and mid-latitude, co-related with understanding air quality. Based on climate indices, we have developed the air quality prediction model for reflecting variations in weather and climate conditions. Therefore, the findings in this study can likely be used for actual prediction systems based on long-term weather measurement datasets over the Arctic region.

Acknowledgment: This research was supported by a National Research Foundation of Korea Grant from the Korean Government (MSIT ; the Ministry of Science and ICT) (NRF- 2023M1A5A1090715).

How to cite: Park, J.-M., Lee, D., Kim, K., Kim, S., Lee, G., and Lee, K. H.: Weather and Climate conditions over the Arctic and mid-latitude regions affecting air quality, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7386, https://doi.org/10.5194/egusphere-egu24-7386, 2024.

EGU24-7705 | ECS | Orals | AS1.3 | Highlight

Soil enthalpy: an unheeded source of subseasonal predictability? 

Constantin Ardilouze and Aaron Boone

Accurate soil moisture initial conditions in dynamical subseasonal forecast systems are known to improve the temperature forecast skill regionally, through more realistic water and energy fluxes at the land-atmosphere interface. Recently, results from the GEWEX-GASS LS4P (Impact of initialized land temperature and snowpack on sub-seasonal to seasonal prediction) multi-model coordinated experiment have provided evidence of the primal contribution of the initial surface and subsurface soil temperature over the Tibetan Plateau for capturing a hemispheric scale atmopsheric teleconnection leading to improved subseasonal forecasts. Yet, both the soil temperature and water content are key components of the soil enthalpy and we hypothesize that properly initializing one of them without modifying the other in a consistent manner can alter the soil thermal equilibrium, thereby potentially reducing the benefit of land initial conditions on subsequent atmospheric forecasts. This study builds on the protocol of the above-mentioned multi-model experiment, by testing different land initialization strategies in an Earth system model. Results of this pilot study suggest that a better mass and energy balance in land initial conditions of the Tibetan Plateau triggers a wave train which propagates through the northern hemisphere mid-latitudes, resulting in an improved large scale circulation and temperature anomalies over multiple regions of the globe. While this study is based on a single case, it strongly advocates for enhanced attention towards preserving the soil energy equilibrium at initialization to make the most of land as a driver of atmospheric extended-range predictability.

How to cite: Ardilouze, C. and Boone, A.: Soil enthalpy: an unheeded source of subseasonal predictability?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7705, https://doi.org/10.5194/egusphere-egu24-7705, 2024.

EGU24-8357 | Orals | AS1.3

Quantifying sources of subseasonal prediction skill in CESM2 

Jadwiga Richter, Anne Glanville, Teagan King, Sanjiv Kumar, Stephen Yeager, Yanan Duan, Megan Fowler, Abby Jaye, Jim Edwards, Julie Caron, Paul Dirmeyer, Gokhan Danabasoglu, and Keith Oleson

Subseasonal prediction fills the gap between weather forecasts and seasonal outlooks. There is evidence that predictability on subseasonal timescales comes from a combination of atmosphere, land, and ocean initial conditions. Predictability from the land is often attributed to slowly varying changes in soil moisture and snowpack, while predictability from the ocean is attributed to sources such as the El Niño Southern Oscillation. Here we use a unique set of subseasonal reforecast experiments with CESM2 to quantify the respective roles of atmosphere, land, and ocean initial conditions on subseasonal prediction skill over land. These reveal that the majority of prediction skill for global surface temperature in weeks 3-4 comes from the atmosphere, while ocean initial conditions become important after week 4, especially in the Tropics. In the CESM2 subseasonal prediction system, the land initial state does not contribute to surface temperature prediction skill in weeks 3-6 and climatological land conditions lead to higher skill, disagreeing with our current understanding. However, land-atmosphere coupling is important in week 1. Subseasonal precipitation prediction skill also comes primarily from the atmospheric initial condition, except for the Tropics, where after week 4 the ocean state is more important.

How to cite: Richter, J., Glanville, A., King, T., Kumar, S., Yeager, S., Duan, Y., Fowler, M., Jaye, A., Edwards, J., Caron, J., Dirmeyer, P., Danabasoglu, G., and Oleson, K.: Quantifying sources of subseasonal prediction skill in CESM2, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8357, https://doi.org/10.5194/egusphere-egu24-8357, 2024.

EGU24-8918 | ECS | Posters on site | AS1.3 | Highlight

Can Machine Learning Models be a Suitable Tool for Predicting Central European Cold Winter Weather on Subseasonal Timescales? 

Selina M. Kiefer, Sebastian Lerch, Patrick Ludwig, and Joaquim G. Pinto

For many practical applications, e.g. agricultural planning, skillful weather predictions on the subseasonal timescale (2-4 weeks in advance) are key for making sensible decisions. Since traditional numerical weather prediction (NWP) models are often not capable of delivering such forecasts, we use an alternative forecasting approach combining both, physical knowledge and statistical models. Selected meteorological variables from ERA-5 reanalysis data are used as predictors for wintertime Central European mean 2-meter temperature and the occurrence of cold wave days at lead times of 14, 21 and 28 days. The forecasts are created by Quantile Regression Forests in case of continuous temperature values and Random Forest Classifiers in case of binary occurrence of cold wave days. Both model types are evaluated for the winters 2000/2001 to 2019/2020 using the Continuous Ranked Probability Skill Score for the continuous forecasts and the Brier Skill Score for the binary forecasts. As a benchmark model, a climatological ensemble obtained from E-OBS observational data is considered. We find that the used machine learning models are able to produce skillful weather forecasts on all tested lead times. As expected, the skill depends on the exact winter to be forecasted and generally decreases for longer lead times but is still achieved for individual winters and in the 20-winter mean at 28 days lead time. Since machine learning models are often subject to a lack of interpretability and thus considered to be less trustworthy, we apply Shapley Additive Explanations to gain insight into the most relevant predictors of the models’ predictions. The results suggest that both Random-Forest based models are capable of learning physically known relationships in the data. This is, besides the capability of producing skillful forecasts on the subseasonal timescale, a selling point of the combination of physical knowledge and statistical models. Finally, we compare the skill of our statistical models to subseasonal state-of-the-art NWP forecasts.

How to cite: Kiefer, S. M., Lerch, S., Ludwig, P., and Pinto, J. G.: Can Machine Learning Models be a Suitable Tool for Predicting Central European Cold Winter Weather on Subseasonal Timescales?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8918, https://doi.org/10.5194/egusphere-egu24-8918, 2024.

EGU24-9510 | ECS | Orals | AS1.3

Stratospheric impact on subseasonal forecast uncertainty in the Northern extratropics  

Jonas Spaeth, Philip Rupp, Hella Garny, and Thomas Birner

Extreme events of the stratospheric polar vortex can modulate subsequent surface weather at subseaonal to seasonal (S2S) timescales. Moreover, they are considered to form windows of opportunity for tropospheric forecasting. This study aims to improve understanding of how the canonical surface response of polar vortex events translates into modulated surface predictability. 

First, we confirm that in the ECMWF extended-range prediction model, the mean signal of weak (strong) polar vortex events projects onto a negative (positive) phase of the North Atlantic Oscillation. The associated equatorward (poleward) shift of the eddy-driven jet then enhances or suppresses synoptic variability in specific regions. By constructing a leadtime, seasonal and model version-dependent climatology of forecast ensemble spread, we link these regions to anomalous forecast uncertainty. For example, sudden stratospheric warmings (SSWs) are followed by a southerly jet shift, which translates into suppressed Rossby wave breaking over Northern Europe, resulting in anomalously high forecast confidence in that region.

In general, both signatures in the mean and spread can contribute to predictability. However, when forecasts are compared to reanalyses, they manifest differently in different skill scores, such as the Root-Mean-Squared Error or the Continuously Ranked Probability Skill Score. We therefore discuss how separate consideration of anomalies in the ensemble mean and ensemble spread may aid to interpret predictability following polar vortex events.

Finally, we apply the diagnostics also to tropical teleconnections. We find indications that windows of forecast opportunity might be dominated by stratospheric polar vortex variability over the Atlantic and by ENSO variability over the Pacific.

How to cite: Spaeth, J., Rupp, P., Garny, H., and Birner, T.: Stratospheric impact on subseasonal forecast uncertainty in the Northern extratropics , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9510, https://doi.org/10.5194/egusphere-egu24-9510, 2024.

EGU24-9738 | ECS | Orals | AS1.3

The dynamics of persistent hotspells in European summers 

Duncan Pappert, Alexandre Tuel, Dim Coumou, Mathieu Vrac, and Olivia Martius

Persistent summer weather can result in extreme events with enormous socio-economic impacts; recent summers in Europe have notably demonstrated this. The dynamics that cause persistent surface weather, as well as potential changes under anthropogenic climate change, are the subject of active scientific debate. Summertime atmospheric dynamics have nevertheless received less attention and we are far from obtaining a comprehensive understanding of the mechanisms involved in the formation of persistent weather conditions in summer. This study investigates the drivers responsible for making some surface extreme events more prone to being long-lasting than others.

Gaining a comprehensive understanding of such processes poses challenges due to the complex interactions of variables and fluxes operating at various timescales – from individual weather events (daily to weekly), to the general circulation of the atmosphere and its modulation by specific changes in sea surface temperature or soil moisture interactions (monthly, seasonal to interannual). Furthermore, studies are recently observing that persistent (quasi-stationary or recurrent) circulation patterns do not necessarily always translate to extreme events and persistence at the surface. This discussion extends to open questions about, such as the potential role of soil moisture preconditioning in extending the lifetime of these events.

Starting from an impact-based definition of persistent hot conditions for different European regions, we characterise their persistence by looking at the associated circulation patterns and surface conditions. Through a comparison of long-lived (persistent) and short-duration events, we discern dynamical differences and regional variations that shed light on the common ingredients and potential mechanisms influencing the persistence of extreme heat events in summer. We use the ERA5 reanalysis dataset to take advantage of its high spatiotemporal resolution and relatively long temporal coverage from the 1950s up to today.

A deeper investigation into the dynamical processes controlling persistent surface conditions over Europe in summer is essential for improved predictability at the sub-seasonal to seasonal (S2S) timescale, and it holds significant relevance for risk preparedness. Results from the study aim to advance the discussion on summer dynamics, weather persistence and climate impacts.

How to cite: Pappert, D., Tuel, A., Coumou, D., Vrac, M., and Martius, O.: The dynamics of persistent hotspells in European summers, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9738, https://doi.org/10.5194/egusphere-egu24-9738, 2024.

EGU24-11457 | ECS | Posters on site | AS1.3 | Highlight

Deep Learning improved seasonal forecasts for the Blue Nile Basin 

Rebecca Wiegels, Luca Glawion, Julius Polz, Christian Chwala, Jan Niklas Weber, Tanja C. Schober, Christof Lorenz, and Harald Kunstmann

Seasonal predictions are essential in mitigating damage to people and nature as a result of climate change and extreme events by improving timely decision-making particularly for water and irrigation management. The newly constructed Grand Ethiopian Renaissance Dam, located in the Blue Nile (BN) Basin in Ethiopia at the border to Sudan, increases the urgency of optimized transboundary water management and improved seasonal predictions. However, the global seasonal forecasting systems have known limitations such as biases and drifts. Specifically at regional level, such as in the highlands of Ethiopia, the seasonal predictions need accurate post-processing. Recent developments have shown the large potential of Deep Learning (DL) applications to improve weather and climate predictions. The goal of this study is to improve the global seasonal forecasting system SEAS5 of ECMWF specifically for the BN Basin using DL approaches such as conventional Convolutional Neural Networks (CNN) or more advanced Adaptive Fourier Neural Operators (AFNO). We present first results for improving and downscaling SEAS5 global seasonal precipitation forecasts in the BN Basin with a particular emphasis on ensemble generation and calibration. The neural networks are trained with ERA5-Land-reanalysis data as a ground-truth, which has a higher resolution than SEAS5 (~9km compared to ~36km). This additional downscaling step allows us to consider the high variations in precipitation intensities in the Ethiopian highlands. The results show that the applied DL models have high potential in improving forecasting scores such as the continuous ranked probability skill score. They therefore allow for improved timely decision-making for water management in the transboundary BN Basin.

How to cite: Wiegels, R., Glawion, L., Polz, J., Chwala, C., Weber, J. N., Schober, T. C., Lorenz, C., and Kunstmann, H.: Deep Learning improved seasonal forecasts for the Blue Nile Basin, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11457, https://doi.org/10.5194/egusphere-egu24-11457, 2024.

EGU24-11637 | ECS | Orals | AS1.3

Seasonal classification of North American weather regimes and their effect on extreme weather 

Swatah Snigdha Borkotoky, Kathleen Schiro, and Kevin Grise

Large-scale (synoptic to planetary), quasi-stationary circulation patterns in the atmosphere modulate the local weather dynamics from seasonal to sub-seasonal scale. These circulation patterns are known as Weather Regimes (WRs) and are a prominent feature in the midlatitudes. Most studies so far have focused on specific regions (such as the west coast of the United States or the European sector), and during a specific time of the year (namely the boreal winter season). Little work has been done on understanding the spatiotemporal characteristics (frequency, duration, and orientation) of seasonal North American WRs and how they affect local weather, especially in terms of extremes. This study aims to fill this knowledge gap with an investigation of North American WRs independently for all four seasons. Using a k-means clustering algorithm on daily geopotential height anomalies (de-seasonalized at monthly scale) at the 500-hPa pressure level, we identify five WRs in each of the four seasons across three independent reanalysis datasets: 1) MERRA2; 2) ERA5; and 3) NCEP-NCAR Reanalysis 1, for the period 1980-2022. Initial analysis shows that the spatial patterns of these WRs are robust but have non-trivial differences in the frequency and duration of occurrences across different reanalysis datasets. Additionally, we explore the occurrence of local extreme weather (precipitation and temperature) across the contiguous United States (CONUS) during the presence of these seasonal WRs. This study aims to improve the understanding of the seasonal to sub-seasonal variations of North American WRs and their influence on local extreme weather.

How to cite: Borkotoky, S. S., Schiro, K., and Grise, K.: Seasonal classification of North American weather regimes and their effect on extreme weather, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11637, https://doi.org/10.5194/egusphere-egu24-11637, 2024.

EGU24-11702 | Orals | AS1.3

Attributing the role of sudden stratospheric warming events in surface weather extremes and their impacts: insights from SNAPSI Working Group 2 

William Seviour, Amy Butler, Chaim Garfinkel, and Peter Hitchcock and the SNAPSI Working Group 2

Sudden stratospheric warming events (SSWs)–in which the westerly polar vortex rapidly breaks down during winter–are  some of the most dramatic examples of dynamical variability in Earth’s atmosphere. It is now well established that SSWs are, on average, followed by large scale anomalies in near-surface circulation patterns, including an equatorward shift of the eddy driven jet that can persist for several months. These anomalies have, in turn, been related to an increase in the likelihood of a variety of high impact weather extremes. However, not all SSWs are followed by impactful weather events; equally, most winter weather extremes are not preceded by SSWs.

Here we will discuss the extent to which the occurrence of individual extreme weather events and their impacts can be attributed to polar stratospheric variability, drawing upon new results from the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project (Working Group 2). This project involves a set of controlled subseasonal hindcast experiments, targeted at three SSW case study events, in which the stratospheric state can be either freely-evolving or nudged towards a climatological or observational state. These simulations reveal that the stratospheric evolution can more than double the regional risk of extreme temperature, rainfall, and snow events. We will go on to explore the attribution of the subsequent impacts of these weather extremes, including on the energy sector, health, and wildfires.  

How to cite: Seviour, W., Butler, A., Garfinkel, C., and Hitchcock, P. and the SNAPSI Working Group 2: Attributing the role of sudden stratospheric warming events in surface weather extremes and their impacts: insights from SNAPSI Working Group 2, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11702, https://doi.org/10.5194/egusphere-egu24-11702, 2024.

EGU24-13143 | ECS | Posters on site | AS1.3

Land-Atmosphere Coupling Simulation and Its Role in Subseasonal-to-Seasonal Prediction 

Yuna Lim, Andrea Molod, Randal Koster, and Joseph Santanello

Land-atmosphere (L-A) coupling can significantly contribute to subseasonal-to-seasonal (S2S) prediction. During periods of strong L-A coupling, land-atmosphere feedbacks are expected to enhance the memory of the system and therefore also the predictability and prediction skill. This study aims to evaluate S2S prediction of ambient surface air temperature under conditions of strong versus weak L-A coupling in forecasts produced with NASA’s state-of-the-art GEOS S2S forecast system. Utilizing three L-A coupling metrics that together capture the connection between the soil and the free atmosphere, enhanced prediction skill for surface air temperature is observed for 3-4 week boreal summer forecasts across the eastern Great Plains when strong L-A coupling is detected at this lead by all three indices. The forecasts with strong L-A coupling in these “hot spot” regions exhibit warm and dry anomalies, signals that are well simulated in the model. Overall, this study provides insight into how better capturing relevant L-A coupling processes might improve prediction on subseasonal-to-seasonal timescales.

How to cite: Lim, Y., Molod, A., Koster, R., and Santanello, J.: Land-Atmosphere Coupling Simulation and Its Role in Subseasonal-to-Seasonal Prediction, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13143, https://doi.org/10.5194/egusphere-egu24-13143, 2024.

EGU24-14585 | Posters on site | AS1.3 | Highlight

Improved long-range forecasts in South Korea through integrated forecast information 

OKYeon Kim, Seul-Hee Im, and Gaeun Kim

We explored the objective methods to improve long-range forecasting through enhanced forecast skills and integrated forecast information. The objective process we used in this study includes the selection of monitoring factors for more reliable monthly seasonal forecasts. Therefore, we chose the three most significant monitoring factors, i.e., ENSO, snow cover over Eurasia Continent and Arctic sea ice. We first examined the effect and response of the monitoring factors on the boreal winter temperature in South Korea. To improve the information related to the ENSO in seasonal forecasting, the impact of the tropical precipitation which act as an oceanic ENSO forcing was investigated. As one of the important monitoring factors for boreal winter temperature prediction, we analyzed the availability of the index describing austral Eurasian snow cover. We also analyzed the usage of Arctic conditions for predicting monthly temperature for boreal winter. We then investigated how well the effect and response of the factors are simulated in the operational seasonal models. Finally, the link between observation-based monitoring factors and model-based prediction is proposed for objective forecasting.

How to cite: Kim, O., Im, S.-H., and Kim, G.: Improved long-range forecasts in South Korea through integrated forecast information, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14585, https://doi.org/10.5194/egusphere-egu24-14585, 2024.

EGU24-14626 | ECS | Orals | AS1.3

The impact of storm event likelihood on the forecast uncertainty over Europe at S2S time scales 

Philip Rupp, Hilla Afargan-Gerstman, Jonas Spaeth, and Thomas Birner

Weather forecasts at subseasonal-to-seasonal (S2S) timescales have little or no deterministic forecast skill in the troposphere. Individual ensemble members are uncorrelated and span a range of scenarios that are possible for the given set of boundary conditions. The uncertainty of such probabilistic forecasts is then determined by this range of scenarios – often quantified in terms of ensemble spread. For certain boundary conditions, the ensemble spread can be highly anomalous, with conditions associated with reduced spread sometimes referred to as „windows of opportunity“. Various dynamical processes can affect the ensemble spread within a given region, including extreme weather events present in individual members. For geopotential height forecasts over Europe, such extremes are mainly comprised of synoptic storms travelling on the North Atlantic storm track.

We use ECMWF re-forecasts from the S2S database to investigate the connection between storm characteristics and increases in ensemble spread in more detail. We find that the presence of storms in individual ensemble members at s2s time scales forms a major contribution to the geopotential height forecast uncertainty over Europe. In our study, we quantify the magnitude of this contribution and analyse the underlying dynamics, using both Eulerian and Lagrangian frameworks. We further show that certain atmospheric conditions, like various blocked weather regimes, are associated with reduced geopotential height ensemble spread over Europe due to changes in the North Atlantic storm track and associated anomalies in storm density. This connection sheds light on the occurrence of some “windows of opportunity” in the troposphere on S2S time scales.

How to cite: Rupp, P., Afargan-Gerstman, H., Spaeth, J., and Birner, T.: The impact of storm event likelihood on the forecast uncertainty over Europe at S2S time scales, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14626, https://doi.org/10.5194/egusphere-egu24-14626, 2024.

EGU24-14701 | Posters virtual | AS1.3

Northern Hemisphere extratropical cyclone biases in ECMWF sub-seasonal forecasts 

Michael Sprenger, Dominik Büeler, and Heini Wernli

Extratropical cyclones influence midlatitude surface weather directly via precipitation and wind and indirectly via upscale feedbacks on the large-scale flow. Biases in cyclone frequency and characteristics in medium-range to sub-seasonal numerical weather prediction might therefore hinder exploiting the potential predictability on these timescales. We thus, for the first time, identify and track extratropical cyclones in 21 years (2000 – 2020) of sub-seasonal ensemble reforecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) in the Northern Hemisphere in all seasons. Overall, the reforecasts reproduce the climatology of cyclone frequency and life-cycle characteristics qualitatively well up to six weeks ahead. However, there are significant regional biases in cyclone frequency, which can result from a complex combination of biases in cyclone genesis (locally and upstream), size, location, lifetime, and propagation speed. Their magnitude is largest in summer, with the strongest deficit of cyclones of up to 15% in the North Atlantic, relatively large in spring, and smallest in winter and autumn. Moreover, the reforecast cyclones are too deep in both ocean basins during most seasons, although intensification rates are captured well. An overestimation of cyclone lifetime and differences between the native spatial resolutions of the reforecasts and the verification dataset might explain this intensity bias in some cases, but there are likely further so far unidentified processes involved. While the patterns of cyclone frequency and life cycle biases often appear in lead time weeks 1 and 2, their magnitudes typically grow further at sub-seasonal lead times and, in some cases, saturate in weeks 5 and 6 only. Most of the dynamical sources of these biases thus likely appear in the early medium range, but biases on longer timescales probably contribute to their further increase with lead time. Our study provides a useful basis to identify, better understand, and ultimately reduce biases in the large-scale flow and in surface weather in sub-seasonal weather forecasts. Given the considerable biases during summer, when sub-seasonal predictions of precipitation and surface temperature will become increasingly important, this season deserves particular attention for future research.

How to cite: Sprenger, M., Büeler, D., and Wernli, H.: Northern Hemisphere extratropical cyclone biases in ECMWF sub-seasonal forecasts, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14701, https://doi.org/10.5194/egusphere-egu24-14701, 2024.

EGU24-15134 | Posters on site | AS1.3

How far in advance can we skillfully predict meteorological drought indices? 

Adel Imamovic, Dominik Büeler, Maria Pyrina, Vincent Humphrey, Christoph Spirig, Lionel Moret, and Daniela Domeisen

Given the limited skill of precipitation forecasts, the question arises to what extent ensemble forecasting systems can be used for early warning systems that require longer lead times, such as drought early warning. In this study, we use ECMWF’s IFS extended range forecasts, statistically downscaled to a 2 km grid encompassing Switzerland, to quantify the spatially and seasonally stratified predictability of several precipitation statistics. Consistent with existing analyses we find the predictability of extratropical instantaneous precipitation to be limited to week 1. However, when considering accumulated precipitation and the standardized precipitation index (SPI) forecasts, which is commonly used for drought management, the forecasts are skillful well into week 3. This extension in predictability horizon is attributed to the characteristic of accumulated precipitation, which is less sensitive to differences in timing of precipitating systems. The enhanced predictability of SPI enhances the utility of extended range forecasts for monthly drought forecasts. We discuss the practical applicability of these findings in the context of the new Swiss drought early warning and monitoring platform, planned for operations in 2025. Leveraging the enhanced predictability of SPI, this platform stands to benefit from our research outcomes, providing stakeholders with tools for proactive drought management and response strategies. 

How to cite: Imamovic, A., Büeler, D., Pyrina, M., Humphrey, V., Spirig, C., Moret, L., and Domeisen, D.: How far in advance can we skillfully predict meteorological drought indices?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15134, https://doi.org/10.5194/egusphere-egu24-15134, 2024.

EGU24-15210 | ECS | Posters on site | AS1.3

Domain adaptation for deep learning ENSO forecasts 

Miriam Rodriguez

In recent years, deep learning (DL) models have been shown to be able to make competitive forecasts of El Niño Southern Oscillation (ENSO). In most cases, due to the short observational record, the outputs of global circulation models (GCM) are used to train DL models. However, GCMs themselves show biases when modeling ENSO dynamics, such as the lack of phase-locking behavior, shifted precipitation trends, or missing El Niño - La Niña asymmetry. The biases of the GCMs are likely inherited by the DL models during pre-training, raising the question of how we can obtain unbiased DL ENSO models while pre-training on GCM output. In this study, we contend that a possible solution to correct these biases is to use well-established domain adaptation methods, which allow DL models to account for shifts in data distribution between training and validation data sets. In particular, we use a ConvLSTM network trained on CESM2 simulations where we first use a supervised objective to fine-tune our model to reanalysis data. Secondly, we employ test-time training to adapt our model for the domain shift between CESM2 and reanalysis data. This study serves as a first step toward comparing domain adaptation techniques for data-driven seasonal-to-annual DL models in a limited data regime.

How to cite: Rodriguez, M.: Domain adaptation for deep learning ENSO forecasts, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15210, https://doi.org/10.5194/egusphere-egu24-15210, 2024.

EGU24-16022 | ECS | Posters on site | AS1.3

Global bias-corrected seasonal forecasts: Towards efficient and near real-time solutions 

Jan Niklas Weber, Christof Lorenz, Tanja Schober, Rebecca Wiegels, and Harald Kunstmann

Droughts, prolonged heat-waves, heavy precipitation events and large-scale flooding - the last years have demonstrated that global climate change is already hitting hard in many places of the Earth. This, inevitably, leads to increased water stress that requires a more sustainable and timely water management across scales. In particular, for optimized use of water resources for irrigation or hydropower generation, it is essential to know their expected availability in the coming months all over the world. This sub-seasonal to seasonal temporal domain, from weeks to months ahead, is addressed by seasonal forecasting systems such as SEAS5, developed by the European Centre for Medium-Range Weather Forecasts (ECMWF). These systems have the potential to provide essential data for enhancing water management practices. Without a bias correction though, the data exhibit a notable deficiency in skill. We have shown for several regions of the world that the “Bias Correction and Spatial Disaggregation” method (BCSD) can improve the forecasting skill substantially. Our next step is now to expand our efforts from the regional to the global scale, i.e., to provide the BCSD-forecasts for the entire globe. Here, the challenge lies in significantly reducing the computational demand for the bias correction: Presently, the BCSD requires several days to execute on a global scale. However, if such forecasts should be used as decision support, a timely provision is crucial.

We therefore present a method to achieve this task: The utilization of fixed Cumulative Distribution Functions (CDFs) rather than their recalculation for each pixel has the potential to enhance the computational efficiency of the bias correction. This approach not only significantly reduces the required data volume but also improves accessibility. To further achieve transferability of the system, we also demonstrate the performance of this system in a containerized environment. Our goal is to achieve a globally corrected SEAS5 forecasts within a time frame of ideally less than one day. With the provision of these bias-corrected data in near-real time, better estimations become available for direct utilization by water managers or as input for subsequent modeling processes.

How to cite: Weber, J. N., Lorenz, C., Schober, T., Wiegels, R., and Kunstmann, H.: Global bias-corrected seasonal forecasts: Towards efficient and near real-time solutions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16022, https://doi.org/10.5194/egusphere-egu24-16022, 2024.

The seasonal cycle (SC) anomalies of winter surface air temperature (SAT) over China mainly include three modes: consistent changes throughout the winter, inverse changes in the early and late winter, and opposite changes in the southern and northern China, respectively.  The positive EOF1 phase (i.e., uniformly warming throughout winter) can be attributed to global warming, especially in the North Atlantic and tropical Pacific. The EOF2 is mainly related to the dipole sea surface temperature (SST) pattern in the North Atlantic. In the early winter, the Rossby wave originating from North Atlantic strengthens Ural blocking high (UBH) and Siberian high (SH) in the early winter, resulting in cold SAT anomalies in most of China. While the large-scale zonal circulation with weakened SH has transformed SAT over China into a warm state in the later winter. The EOF3 can be attributed to the tripole SST in the North Atlantic and El Niño-like SST pattern in the tropical Pacific. In December, the Rossby wave train originating from the mid-latitudes of the North Atlantic Ocean enhances cold air activity in the Northern Hemisphere, causing cold SAT anomalies in Northeast China, while the dominating southerly winds in southern China cause warm SAT anomalies. In the late winter, the large-scale circulation resembles negative AO phase, resulting in the northerly winds and cold SAT anomalies in the northern China. Meanwhile, the anomalous anticyclonic circulation in the Northwest Pacific causes warm SAT anomalies in southern China. Therefore, the combined effects of tropical and extratropical SST should be considered when predicting interannual variability of winter SAT anomalies over China.

How to cite: Yu, M.: Diversity of seasonal cycle anomalies of surface air temperature in winter over China , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16753, https://doi.org/10.5194/egusphere-egu24-16753, 2024.

EGU24-17318 | ECS | Posters on site | AS1.3

A barycenter-based approach for the multi-model ensembling of subseasonal forecasts 

Camille Le Coz, Alexis Tantet, Rémi Flamary, and Riwal Plougonven

Combining ensemble forecasts from different models into a multi-model ensemble (MME) have been shown to improve forecast skill at different time-scales, including the sub-seasonal to seasonal (S2S) one. Here, we investigate a new method to build such MME based on barycenter.

Recognizing ensemble forecasts as discrete probability distributions, we work directly in the probability distribution space. This allows us to use existing tools in this space, and in particular the concept of barycenter. The barycenter of a collection of distributions (or the ensemble forecasts here) is the distribution that best represents them, based on a given metric. The barycenter can thus be seen as the combination of these distributions, and so used to build a MME. We compare two barycenters based on different metrics: the L2 and the Wasserstein distances. The Wasserstein distance corresponds to the cost of the optimal transport between two distributions and has interesting properties in the distribution space. We compare it to the L2-barycenter which is in fact shown to be equivalent to the well-known “pooling” MME method (i.e. the concatenation of the different ensembles members). Another interesting point of the barycenters is that they allow you to give different weights to the models and so to easily build a weighted-MME. The weights have an important impact on the skill of the MMEs. We are thus optimizing the weights by learning them from the data using cross-validation on the forecasts.

The two barycenter-based MMEs are applied to the combination of the models from the S2S project’s database. Their performances are evaluated for the prediction of weekly 2m-temperature during European winter with respect to different metrics. As a proof of concept, we first start with the combination of two models, namely the European Centre Medium-Range Weather Forecasts (ECMWF) and the National Center for Environmental Prediction (NCEP) models. We show that the two MMEs are generally able to perform as well or better than both the single-models, but that the best combination method depends on the chosen metric. We then extend the barycenter approach to the combination of more models, of which we will discuss preliminary results.

How to cite: Le Coz, C., Tantet, A., Flamary, R., and Plougonven, R.: A barycenter-based approach for the multi-model ensembling of subseasonal forecasts, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17318, https://doi.org/10.5194/egusphere-egu24-17318, 2024.

EGU24-17920 | Orals | AS1.3 | Highlight

Verification of seasonal forecast for facilitating agricultural applications 

Yuhei Takaya, Toshichika Iizumi, Yuji Masutomi, and Toshiyuki Nakaegawa

Seasonal forecasting has the potential to support agricultural activities by offering crop-yield forecasts and facilitating measures to mitigate weather-related damages. This study aims to enhance the application of subseasonal to seasonal (S2S) forecasts in agriculture by evaluating them through tailored verification methods that consider crop calendars and areas.

The verification employs the so-called 1-norm continuous ranked probability score (CRPS), which utilizes the absolute norm instead of a square to quantify forecast errors. While the 1-norm CRPS is not a proper score and does not suit for ensemble forecast verification, it offers an advantage in terms of user-friendliness. Specifically, the score is proportional to the expectation of the absolute error, and thus, it is easier to relate the outcomes of crop models under the assumption of linearity compared to other scores like the ordinal CRPS.

Crop regions and seasons for major commodity crops such as wheat, rice, and maize were identified using global datasets of crop yields and crop calendars. Using the crop calendar information, we can assess the within-season forecast performance in relation to crop growth stages globally. Reforecast data from seasonal forecasts archived by the EU-funded Copernicus Climate Change Service (C3S) were evaluated, allowing for a multi-model comparison of forecast skill. The presentation illustrates a set of example verification products targeted to the common commodity crops. A comprehensive overview of forecast skill for the target crops is anticipated to facilitate a dialogue between meteorological and agricultural experts, thereby enhancing the usability of the seasonal forecast.

How to cite: Takaya, Y., Iizumi, T., Masutomi, Y., and Nakaegawa, T.: Verification of seasonal forecast for facilitating agricultural applications, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17920, https://doi.org/10.5194/egusphere-egu24-17920, 2024.

EGU24-18260 | Posters on site | AS1.3

Improving daily-to-seasonal sea ice forecasts of the AWI coupled prediction system with sea-ice and ocean data assimilation and atmospheric large-scale wind nudging. 

Svetlana Loza, Marylou Athanase, Longjiang Mu, Jan Streffing, Antonio Sánchez-Benítez, Miguel Andrés-Martínez, Lars Nerger, Tido Semmler, Dmitry Sidorenko, and Helge Goessling

Predictive skills of coupled sea-ice/ocean and atmosphere models are limited by the chaotic nature of the atmosphere. Assimilation of observational information on ocean hydrography and sea ice allows to obtain a coupled-system state that provides a basis for subseasonal-to-seasonal ocean and sea-ice forecast (Mu et al., 2022). However, if the atmosphere is not additionally constrained, the quasi-random atmospheric states within an ensemble forecast lead to a fast divergence of the ocean and sea-ice states, degrading the system’s performance with respect to the sea ice forecasts. As reported previously, imposing an additional constraint by nudging large-scale winds to the ERA5 reanalysis data (Sánchez-Benítez et al., 2021; Athanase et al., 2022) improves predictive skills of the AWI Coupled Prediction System (AWI-CPS, Mu et al. 2022) with regard to sea ice drift (Losa et al., 2023). Here we provide results based on a much more extensive set of ensemble-based data assimilation experiments spanning the time period from 2002 to 2023 and a series of long forecast experiments over 2010 – 2023, initialized in four different seasons. We compare the performance of forecasts initialized from two sets of data assimilation experiments, with and without atmospheric wind nudging. The additional relaxation of the large-scale atmospheric circulation to the ERA5 reanalysis data for the initialization leads to reasonable atmospheric forecast skill on weather timescales: Despite the simple technique, the coarse resolution compared to NWP systems, and the limited optimization efforts, 10-day forecasts of the 500 hPa geopotential height are about as skillful as the best performing NWP forecasts were about 10 –15 years ago. Among other aspects, this leads to significantly improved subseasonal-to-seasonal sea-ice concentration and thickness forecasts.

 

Athanase, M., Schwager, M., Streffing, J., Andrés-Martínez, M., Loza, S., and Goessling, H.: Impact of the atmospheric circulation on the Arctic snow cover and ice thickness variability , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5836, https://doi.org/10.5194/egusphere-egu22-5836, 2022.

Losa, S. N., Mu, L., Athanase, M., Streffing, J., Andrés-Martínez, M., Nerger, L., Semmler, T., Sidorenko, D., and Goessling, H. F.: Combining sea-ice and ocean data assimilation with nudging atmospheric circulation in the AWI Coupled Prediction System, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-14227, https://doi.org/10.5194/egusphere-egu23-14227, 2023.

Mu, L. , Nerger, L. , Streffing, J. , Tang, Q. , Niraula, B. , Zampieri, L., Loza, S. N. and Goessling, H. F. (2022): Sea‐Ice Forecasts With an Upgraded AWI Coupled Prediction System , Journal of Advances in Modeling Earth Systems, 14 (12) . doi: 10.1029/2022ms003176

Sánchez-Benítez, A. , Goessling, H. , Pithan, F. , Semmler, T. and Jung, T. (2022): The July 2019 European Heat Wave in a Warmer Climate: Storyline Scenarios with a Coupled Model Using Spectral Nudging , Journal of Climate, 35 (8), pp. 2373-2390 . doi: 10.1175/JCLI-D-21-0573.1

How to cite: Loza, S., Athanase, M., Mu, L., Streffing, J., Sánchez-Benítez, A., Andrés-Martínez, M., Nerger, L., Semmler, T., Sidorenko, D., and Goessling, H.: Improving daily-to-seasonal sea ice forecasts of the AWI coupled prediction system with sea-ice and ocean data assimilation and atmospheric large-scale wind nudging., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18260, https://doi.org/10.5194/egusphere-egu24-18260, 2024.

EGU24-19908 | ECS | Posters on site | AS1.3 | Highlight

Improving sub-seasonal forecasting of East Asian monsoon precipitation with deep learning 

Zhou Jiahui and Fei Liu

Accurate subseasonal forecast of East Asian summer monsoon precipitation (EASM) is pivotal, impacting the livelihoods of billions. However, the proficiency of state-of-the-art subseasonal-to-seasonal (S2S) models in forecasting precipitation remains constrained. We developed a convolutional neural network regression model, harnessing the more reliably predicted atmospheric variables from dynamic models to enhance their forecast skills for precipitation. The outcomes of the CNN model are promising: a 12% increase in accuracy and a 10% reduction in RMSE for precipitation forecast at the lead time of one week. The predictive skill of dynamic models for atmospheric variables shows a significant correlation with the performance of the CNN model. Ablation experiments on various predictors reveal that xx is the most influential factor affecting the CNN model's performance.

How to cite: Jiahui, Z. and Liu, F.: Improving sub-seasonal forecasting of East Asian monsoon precipitation with deep learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19908, https://doi.org/10.5194/egusphere-egu24-19908, 2024.

EGU24-20725 | ECS | Posters on site | AS1.3

A Parametric Model of Elliptic Orbits for Annual Evolutions of Northern Hemisphere Stratospheric Polar Vortex and Their Interannual Variability 

Michael Secor, Yueyue Yu, Jie Sun, Ming Cai, and Xinyue Luo

The year-to-year varying annual evolutions of the stratospheric polar vortex (SPV) have an important downward impact on the weather and climate from winter to summer and thus potential implications for seasonal forecasts. This study constructs a parametric elliptic orbit model for capturing the annual evolutions of mass-weighted zonal momentum at 60° N (MU) and total air mass above the isentropic surface of 400 K (M) over the latitude band of 60–90° N from 1 July 1979 to 30 June 2022. The elliptic orbit model naturally connects two time series of a nonlinear oscillator. As a result, the observed coupling relationship between MU and M associated with SPV as well as its interannual variations can be well reconstructed by a limited number of parameters of the elliptic orbit model. The findings of this study may pave a new way for short-time climate forecasts of the annual evolutions of SPV, including its temporal evolutions over winter seasons as well as the spring and fall seasons, and timings of the sudden stratospheric warming events by constructing its elliptic orbit in advance.

How to cite: Secor, M., Yu, Y., Sun, J., Cai, M., and Luo, X.: A Parametric Model of Elliptic Orbits for Annual Evolutions of Northern Hemisphere Stratospheric Polar Vortex and Their Interannual Variability, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20725, https://doi.org/10.5194/egusphere-egu24-20725, 2024.

EGU24-20993 | ECS | Orals | AS1.3

Machine Learning Models Use Large Scale Signals to Forecast the MJO 

Lin Yao, Da Yang, James Duncan, Ashesh Kumar Chattopadhyay, Pedram Hassanzadeh, Wahid Bhimji, and Bin Yu

The Madden-Julian Oscillation (MJO) is a large-scale tropical phenomenon where fluctuations of clouds, rainfall, winds, and pressure propagate eastward around the globe every 30 to 90 days on average. The MJO has significant impacts on weather and climate both locally and globally. Despite its importance, forecasting the MJO remains challenging due to the limitations of traditional numerical and statistical methods. To address this, machine learning has emerged as a promising avenue for MJO forecasting (Martin et al. 2022, Silini et al. 2021, Delaunay and Christensen 2022). Apart from accurate forecasts emphasized in previous research, our study aims to get more physical insights: we build a predictive and interpretable convolution neural network (CNN) and unravel what tropical waves at which spatial scales are essential for MJO forecasting.

Our CNN model takes tropical reanalysis maps as input and predicts the MJO index, achieving forecast skills comparable to NCEP Climate Forecast System (CFSv2). This level of skill is state-of-art in interpretable neural networks. To understand what information is crucial to our MJO forecast, we decompose the output of each convolution layer into tropical waves at different zonal scales. We find that the CNN focuses on large-scale patterns whose zonal scale is above 2500 km. In fact, even when fed exclusively with large-scale features as input, the CNN achieves MJO forecasts akin to the skill of the original model. Furthermore, the CNN chooses to reconstruct large-scale features from input containing solely small-scale features instead of relying directly on small scales for forecasting. This reconstruction further emphasizes the critical role of large-scale patterns in MJO predictions.

In future research, we plan to perform a systematic analysis to evaluate the contribution of different tropical waves to MJO forecasting. We will also simplify the model architecture to facilitate better understanding. Additionally, we plan to incorporate more previous time steps as input memories to enhance forecast accuracy. This work represents a promising advance towards economic yet precise MJO forecasting.

How to cite: Yao, L., Yang, D., Duncan, J., Kumar Chattopadhyay, A., Hassanzadeh, P., Bhimji, W., and Yu, B.: Machine Learning Models Use Large Scale Signals to Forecast the MJO, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20993, https://doi.org/10.5194/egusphere-egu24-20993, 2024.

The latest assessment report (AR6) of the Intergovernmental Panel on Climate Change includes a new element to climate research, i.e. the Interactive Atlas (IA), which is very useful for users from different sectors. As the new CMIP6 global climate model simulations use the brand-new SSP-scenarios paired with the RCP-scenarios, the latest climate change projections should be evaluated in order to update the regional and national adaptation strategies. Keeping this in mind we focused on Europe, with a special emphasis on Hungary in our study.

Our aim was to analyse the potential future changes of different temperature indices for Europe, in order to recognize spatial patterns and trends that may shape our climate in the second half of the 21st century. For this purpose, multi-model mean simulation data provided by the IPCC AR6 WG1 IA were downloaded on a monthly base. We chose two climate indices beside the mean temperature values, which represent temperature extremes, namely, the number of days with maximum temperature above 35 °C and the number of frost days (i.e. when daily minimum temperature is below 0 °C). We focused on the end of the 21st century (2081–2100) with also briefly considering the medium-term changes of the 2041–2060 period (both compared to the last two decades of the historical simulation period, i.e. 1995–2014 as the reference period). For both future periods we used all scenarios provided in the IA, namely, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5.

Several zonal and meridional segments over the continent were defined, where we analysed the projected changes of the indices. The zonal segments provide an insight on two different effects that may induce spatial differences between future regional changes. (i) Continentality can be recognized as an increasing effect from the western parts of the segment towards the east. (ii) Topography also appears as the influence of mountains, plains, and basins emerge. The meridional segments provide information about the north-to-south differences as well, as the effects of sea cover. The changes in the indices are plotted on diagrams representing the different months, where the differences in the scenarios are also shown. These diagrams are compared to their respective landscape profiles, furthermore, statistical parameters were calculated. In addition, a monotony index was defined as the cumulative direction of differences between the neighbouring grid cells and analysed within the study.

Our results show that in the changes of mean temperature, both the zonal location and sea cover will play a key role in forming spatial differences within Europe. However, for the extreme temperature indices, topography and continentality are likely to become more dominant than sea cover, while the zonal location remains an important factor. 

Acknowledgements: This work was supported by the Hungarian National Research, Development and Innovation Fund [grant numbers PD138023, K-129162], and the National Multidisciplinary Laboratory for Climate Change [grant number RRF-2.3.1-21-2022-00014]. 

How to cite: Divinszki, F., Kis, A., and Pongrácz, R.: Analysing the projected monthly changes of temperature-related climate indices over Europe using zonal and meridional segments based on CMIP6 data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-389, https://doi.org/10.5194/egusphere-egu24-389, 2024.

EGU24-868 | ECS | Posters on site | CL4.3

Relationship of the predictability of North Pacific Mode and ENSO with predictability of PDO 

Jivesh Dixit and Krishna M. AchutaRao

PDO and ENSO are most prominent variability modes in the Pacific Ocean at decadal and interannual timescales respectively. Mutual independence between ENSO and PDO is questionable (Chen & Wallace, 2016). Linear combination of the first two orthogonal modes of SST variability in our Study Region (SR; 70oN - 20oS, 110oE - 90oW) i.e. mode 1 (interannual mode, we call it, IAM; ENSO like variability) and mode 2 (North Pacific Mode (NPM; Deser & Blackmon (1995)); a decadal mode) produces a PDO like variability (Chen & Wallace, 2016). It suggests that PDO is not independently hosted in the Pacific Ocean and can be represented by two linearly independent variability modes.

To produce credible and skillful climate information at multi-year to decadal timescales, Decadal Climate Prediction Project (DCPP), led by the Working Group on Subseasonal to Interdecadal Prediction (WGSIP), focuses on both the scientific and practical elements of forecasting climate by employing predictability research and retrospective analyses within the Coupled Model Intercomparison Project Phase 6 (CMIP6). Component A under DCPP experiments concentrates on hindcast experiments to examine the prediction skill of participating models with respect to actual observations.

As linear combination of  IAM and NPM in SR produces PDO pattern and timescales efficiently, we compared the  ability of DCPP-A hindcasts to predict  IAM, NPM, and  PDO. In this analysis we use output from 9 models (a total of 128 ensemble members), initialised every year from 1960 to 2010. To produce the prediction skill estimates.

At lead year 1 from initialisation, the prediction of NPM,  IAM and PDO is quite skillful as the models are initialised with observations. In subsequent years, skill of either IAM or NPM or both drop significantly and that leads to drop in skill of predicted PDO index. Both the deterministic estimates and probabilistic estimates of prediction skill for DCPP hindcast experiments suggest that the ability of hindcast experiments to predict NPM governs the prediction skill to predict PDO index.

Keywords: PDO, ENSO, NPM, CMIP6, DCPP, hindcast

References

Chen, X., & Wallace, J. M. (2016). Orthogonal PDO and ENSO indices. Journal of Climate, 29(10), 3883–3892. https://doi.org/10.1175/jcli-d-15-0684.1

Deser, C., & Blackmon, M. L. (1995). On the Relationship between Tropical and North Pacific Sea Surface Temperature Variations. Journal of Climate, 8(6), 1677–1680. https://doi.org/10.1175/1520-0442(1995)008<1677:OTRBTA>2.0.CO;2

How to cite: Dixit, J. and AchutaRao, K. M.: Relationship of the predictability of North Pacific Mode and ENSO with predictability of PDO, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-868, https://doi.org/10.5194/egusphere-egu24-868, 2024.

EGU24-1757 | Posters on site | CL4.3

Is the NAO signal-to-noise paradox exacerbated by severe winter windstorms? 

Lisa Degenhardt, Gregor C. Leckebusch, Adam A. Scaife, Doug Smith, and Steve Hardiman

The signal-to-noise paradox is known to be a limitation in multiple seasonal and decadal forecast models where the model ensemble mean predicts observations better than individual ensemble members. This ‘paradox’ occurs for different parameters, like the NAO, temperature, wind speed or storm counts in multiple seasonal and decadal forecasts. However, investigations have not yet found the origin of the paradox. First hypotheses are that weak ocean – atmosphere coupling or a misrepresentation of eddy feedback in these models is responsible.

Our previous study found a stronger signal-to-noise error in windstorm frequency than for the NAO despite highly significant forecast skill. In combination with the underestimation of eddy feedback in multiple models, this led to the question: Might the signal-to-noise paradox over the North-Atlantic be driven by severe winter windstorms?

To assess this hypothesis, the signal-to-noise paradox is investigated in multiple seasonal forecast suites from the UK Met Office, ECMWF, DWD and CMCC. The NAO is used to investigate the changes in the paradox depending on the storminess of the season. The results show a significant increase of the NAO-signal-to-noise error in stormy seasons in GloSea5. Other individual models like the seasonal model of the DWD or CMCC do not show such a strong difference. A multi-model approach, on the other hand, shows the same tendency as GloSea5. Nevertheless, these model differences mean that more hindcasts are needed to conclusively demonstrate that the signal-to-noise error arises from Atlantic windstorms.

How to cite: Degenhardt, L., Leckebusch, G. C., Scaife, A. A., Smith, D., and Hardiman, S.: Is the NAO signal-to-noise paradox exacerbated by severe winter windstorms?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1757, https://doi.org/10.5194/egusphere-egu24-1757, 2024.

EGU24-1940 | ECS | Orals | CL4.3

Study of the Decadal Predictability of Mediterranean Sea Surface Temperature Based on Observations 

Xiaoqin Yan, Youmin Tang, and Dejian Yang

Sea surface temperature (SST) changes in the Mediterranean Sea have profound impacts on both the Mediterranean regions and remote areas. Previous studies show that the Mediterranean SST has significant decadal variability that is comparable with the Atlantic multidecadal variability (AMV). However, few studies have discussed the characteristics and sources of the decadal predictability of Mediterranean SST based on observations. Here for the first time we use observational datasets to reveal that the decadal predictability of Mediterranean SST is contributed by both external forcings and internal variability for both annual and seasonal means, except that the decadal predictability of the winter mean SST in the eastern Mediterranean is mostly contributed by only internal variability. Besides, the persistence of the Mediterranean SST is quite significant even in contrast with that in the subpolar North Atlantic, which is widely regarded to have the most predictable surface temperature on the decadal time scale. After the impacts of external forcings are removed, the average prediction time of internally generated Mediterranean SST variations is more than 10 years and closely associated with the multidecadal variability of the Mediterranean SST that is closely related to the accumulated North Atlantic Oscillation forcing.

How to cite: Yan, X., Tang, Y., and Yang, D.: Study of the Decadal Predictability of Mediterranean Sea Surface Temperature Based on Observations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1940, https://doi.org/10.5194/egusphere-egu24-1940, 2024.

EGU24-3190 | ECS | Orals | CL4.3

Seasonal forecasting of the European North-West shelf seas: limits of winter and summer sea surface temperature predictability 

Jamie Atkins, Jonathan Tinker, Jennifer Graham, Adam Scaife, and Paul Halloran

The European North-West shelf seas (NWS) support economic interests and provide environmental services to several adjacent populous countries. Skilful seasonal forecasts of the NWS would be useful to support decision making. Here, we quantify the skill of an operational large-ensemble ocean-atmosphere coupled dynamical forecasting system (GloSea), as well as a benchmark persistence forecasting system, for predictions of NWS sea surface temperature (SST) at 2-4 months lead time in winter and summer. We also identify sources of- and limits to NWS SST predictability with a view to what additional skill may be available in the future. We find that GloSea NWS SST skill is generally high in winter and low in summer. Persistence of anomalies in the initial conditions contributes substantially to predictability. GloSea outperforms simple persistence forecasts, by adding atmospheric variability information, but only to a modest extent. Where persistence is low – for example in seasonally stratified regions – both GloSea and persistence forecasts show lower skill. GloSea skill can be degradeded by model deficiencies in the relatively coarse global ocean component, which lacks a tidal regime and likely fails to properly fine-scale NWS physics. However, using “near perfect atmosphere” tests, we show potential for improving predictability of currently low performing regions if atmospheric circulation forecasts can be improved, underlining the importance of development of atmosphere-ocean coupled models for NWS seasonal forecasting applications.

How to cite: Atkins, J., Tinker, J., Graham, J., Scaife, A., and Halloran, P.: Seasonal forecasting of the European North-West shelf seas: limits of winter and summer sea surface temperature predictability, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3190, https://doi.org/10.5194/egusphere-egu24-3190, 2024.

EGU24-4538 | ECS | Orals | CL4.3

Statistical downscaling of extremes in seasonal predictions - a case study on spring frosts for the viticultural sector 

Sebastiano Roncoroni, Panos Athanasiadis, and Silvio Gualdi

Spring frost events occurring after budburst of grapevines can damage new shoots, disrupt plant growth and cause large economic losses to the viticultural sector. Frost protection practices encompass a variety of vineyard management actions across timescales, from seasonal to decadal and beyond. The cost-effectiveness of such measures depends on the availability of accurate predictions of the relevant climate hazards at the appropriate timescales.

In this work, we present a statistical downscaling method which predicts variations in the frequency of occurrence of spring frost events in the important winemaking region of Catalunya at the seasonal timescale. The downscaling method exploits the seasonal predictability associated with the predictable components of the atmospheric variability over the Euro-Atlantic region, and produces local predictions of frost occurrence at a spatial scale relevant to vineyard management.

The downscaling method is designed to address the specific needs highlighted by a representative stakeholder in the local viticultural sector, and is expected to deliver an actionable prototype climate service. The statistical procedure is developed in perfect prognosis mode: the method is trained with large-scale reanalysis data against a high-resolution gridded observational reference, and validated against multi-model seasonal hindcast predictions.

Our work spotlights the potential benefits of transferring climate predictability across spatial scales for the design and provision of usable climate information, particularly regarding extremes.

How to cite: Roncoroni, S., Athanasiadis, P., and Gualdi, S.: Statistical downscaling of extremes in seasonal predictions - a case study on spring frosts for the viticultural sector, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4538, https://doi.org/10.5194/egusphere-egu24-4538, 2024.

EGU24-4873 | ECS | Orals | CL4.3

Why does the Signal-to-Noise Paradox Exist in Seasonal Climate Predictability? 

Yashas Shivamurthy, Subodh Kumar Saha, Samir Pokhrel, Mahen Konwar, and Hemant Kumar Chaudhari

Skillful prediction of seasonal monsoons has been a challenging problem since the 1800s. However, significant progress has been made in Indian summer monsoon rainfall prediction in recent times, with skill scores reaching 0.6 and beyond, surpassing the estimated predictability limits. This phenomenon leads to what is known as the “Signal-to-noise Paradox.” To investigate this paradox, we utilized 52 ensemble member hindcast runs spanning 30 years.

Through the application of ANOVA and Mutual Information methods, we estimate the predictability limit globally. Notably, for the boreal summer rainfall season, the Indian subcontinent exhibited the paradox, among several other regions, while the Equatorial Pacific region, despite demonstrating high prediction skill, does not have the Signal-to-Noise paradox. We employed a novel approach to understand how sub-seasonal variability and their projection in association with predictors are linked to the paradoxical behavior of seasonal prediction skill.

We propose a new method to estimate predictability limits that is free from paradoxical phenomena and shows much higher seasonal predictability. This novel method provides valuable insights into the complex dynamics of monsoon prediction, thereby creating opportunities for expanded research and potential improvements in seasonal forecasting skill in the coming years.

How to cite: Shivamurthy, Y., Saha, S. K., Pokhrel, S., Konwar, M., and Chaudhari, H. K.: Why does the Signal-to-Noise Paradox Exist in Seasonal Climate Predictability?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4873, https://doi.org/10.5194/egusphere-egu24-4873, 2024.

EGU24-7134 | ECS | Orals | CL4.3

Towards the Predictability of Compound Dry and Hot Extremes through Complexity Science 

Ankit Agarwal and Ravikumar Guntu

Compound Dry and Hot Extremes (CDHE) have an adverse impact on socioeconomic factors during the Indian summer monsoon, and a future exacerbation is anticipated. The occurrence of CDHE is influenced by teleconnections, which play a crucial role in determining its likelihood on a seasonal scale. Despite the importance, there is a lack of studies unravelling the teleconnections of CDHE in India. Previous investigations specifically focused on teleconnections between precipitation, temperature, and climate indices. Hence, there is a need to unravel the teleconnections of CDHE. This study presents a framework combining event coincidence analysis (ECA) with complexity science. ECA evaluates the synchronization between CDHE and climate indices. Subsequently, complexity science is utilized to construct a driver-CDHE network to identify the critical drivers of CDHE. A logistic regression model is employed to evaluate the proposed drivers' effectiveness. The occurrence of CDHE exhibits distinct patterns from July to September when considering intra-seasonal variability. Our findings contribute to the identification of drivers associated with CDHE. The primary driver for Eastern, Western India and Central India is the indices in the Pacific Ocean and Atlantic Ocean, respectively, followed by the indices in the Indian Ocean. These identified drivers outperform the traditional Niño 3.4-based predictions. Overall, our results demonstrate the effectiveness of integrating ECA and complexity science to enhance the prediction of CDHE occurrences.

How to cite: Agarwal, A. and Guntu, R.: Towards the Predictability of Compound Dry and Hot Extremes through Complexity Science, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7134, https://doi.org/10.5194/egusphere-egu24-7134, 2024.

EGU24-8028 | ECS | Orals | CL4.3

Constraining near to mid-term climate projections by combining observations with decadal predictions 

Rémy Bonnet, Julien Boé, and Emilia Sanchez

The implementation of adaptation policies requires seamless and relevant information on the evolution of the climate over the next decades. Decadal climate predictions are subject to drift because of intrinsic model errors and their skill may be limited after a few years or even months depending on the region. Non-initialized ensembles of climate projections have large uncertainties over the next decades, encompassing the full range of uncertainty attributed to internal climate variability. Providing the best climate information over the next decades is therefore challenging. Recent studies have started to address this challenge by constraining uninitialized projections of sea surface temperature using decadal predictions or using a storyline approach to constrain uninitialized projections of the Atlantic Meridional Overturning Circulation using observations. Here, using a hierarchical clustering method, we select a sub-ensemble of non-initialized climate simulations based on their similarity to observations. Then, we try to further refine this sub-ensemble of trajectories by selecting a subset based on its consistency with decadal predictions. This study presents a comparison of these different methods for constraining surface temperatures in the North-Atlantic / Europe region over the next decades, focusing on CMIP6 non-initialized simulations.

How to cite: Bonnet, R., Boé, J., and Sanchez, E.: Constraining near to mid-term climate projections by combining observations with decadal predictions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8028, https://doi.org/10.5194/egusphere-egu24-8028, 2024.

EGU24-9049 | Posters on site | CL4.3

Constraining internal variability in CMIP6 simulations to provide skillful near-term climate predictions 

Rashed Mahmood, Markus G. Donat, Pablo Ortega, and Francisco Doblas-Reyes

Adaptation to climate change requires accurate and reliable climate information on decadal and multi-decadal timescales. Such near-term climate information is obtained from future projection simulations, which are strongly affected by uncertainties related to, among other things, internal climate variability. Here we present an approach to constrain variability in future projection simulations of the coupled model intercomparison project phase 6 (CMIP6). The constraining approach involves phasing in the simulated with the observed climate state by evaluating the area-weighted spatial pattern correlations of sea surface temperature (SST) anomalies in individual members and observations. The constrained ensemble, based on the top ranked members in terms of pattern correlations with observed SST anomalies, shows significant added value over the unconstrained ensemble in predicting surface temperature 10 and also 20 years  after the synchronization with observations, thus extending the forecast range of the standard initialised predictions. We also find that while the prediction skill of the constrained ensemble for the first ten years is similar to the initialized decadal predictions, the added value against the unconstrained ensemble extends over more regions than the decadal predictions. In addition, the constraining approach can also be used to attribute predictability of regional and global climate variations to regional SST variability.

How to cite: Mahmood, R., G. Donat, M., Ortega, P., and Doblas-Reyes, F.: Constraining internal variability in CMIP6 simulations to provide skillful near-term climate predictions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9049, https://doi.org/10.5194/egusphere-egu24-9049, 2024.

There is an ongoing discussion about the contributions from forced and natural sources to the Atlantic Multi-decadal Variability (AMV).  As the AMV influences the general climate in large regions, this question has important consequences for climate predictions on decadal timescales and for a robust estimation of the influence of climate forcings.

Here, we investigate the Atlantic Multi-decadal Variability (AMV) in observations and in a large CMIP6 historical climate model ensemble. We compare three different definitions of the AMV aimed at extracting the variability intrinsic to the Atlantic region. These definitions are based on removing from the Atlantic temperature the non-linear trend, the part congruent to the global average, or the part congruent to the multi-model ensemble mean of the global average. The considered AMV definitions agree on the well-known low-frequency oscillatory variability in observations, but show larger differences for the models. In general, large differences between ensemble members are found.

We estimate the forced response in the AMV as the mean of the large multi-model ensemble.  The forced response resembles the observed low-frequency oscillatory variability for the detrended AMV definition, but this definition is also the most inefficient in removing the forced global mean signal. The forced response is very weak for the other definitions and only few of their individual ensemble members show oscillatory variability and, if they do, not with the observed phase.

The observed spatial temperature pattern related to the AMV is well captured for all three AMV definitions, but with some differences in the spatial extent. The observed instantaneous connection between NAO and AMV is well represented in the models for all AMV definitions. Only non-significant evidence of NAO leading the AMV on decadal timescales is found.

How to cite: Christiansen, B., Yang, S., and Drews, A.: The Atlantic Multi-decadal Variability in observations and in a large historical multi-model ensemble: Forced and internal variability, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9100, https://doi.org/10.5194/egusphere-egu24-9100, 2024.

EGU24-9274 | ECS | Orals | CL4.3 | Highlight

The Role of the North Atlantic for Heat Wave Characteristics in Europe 

Sabine Bischof, Robin Pilch Kedzierski, Martje Hänsch, Sebastian Wahl, and Katja Matthes

The recent severe European summer heat waves of 2015 and 2018 co-occurred with cold subpolar North Atlantic (NA) sea surface temperatures (SSTs). However, a significant connection between this oceanic state and European heat waves was not yet established.

We investigate the effect of cold subpolar NA SSTs on European summer heat waves using two 100-year long AMIP-like model experiments: one that employs the observed global 2018 SST pattern as a boundary forcing and a counter experiment for which we removed the negative NA SST anomaly from the 2018 SST field, while preserving daily and small-scale SST variabilities. Comparing these experiments, we find that cold subpolar NA SSTs significantly increase heat wave duration and magnitude downstream over the European continent. Surface temperature and circulation anomalies are connected by the upper-tropospheric summer wave pattern of meridional winds over the North Atlantic European sector, which is enhanced with cold NA SSTs. Our results highlight the relevance of the subpolar NA region for European summer conditions, a region that is marked by large biases in current coupled climate model simulations.

How to cite: Bischof, S., Pilch Kedzierski, R., Hänsch, M., Wahl, S., and Matthes, K.: The Role of the North Atlantic for Heat Wave Characteristics in Europe, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9274, https://doi.org/10.5194/egusphere-egu24-9274, 2024.

EGU24-9690 | ECS | Orals | CL4.3

Hybrid statistical-dynamical seasonal prediction of summer extreme temperatures over Europe 

Luca Famooss Paolini, Paolo Ruggieri, Salvatore Pascale, Erika Brattich, and Silvana Di Sabatino

Several studies show that the occurrence of summer extreme temperatures over Europe is increased since the middle of the twentieth century and is expected to further increase in the future due to global warming (Seneviratne et al., 2021). Thus, predicting heat extremes several months ahead is crucial given their impacts on socio-economic and environmental systems.

In this context, state-of-the-art dynamical seasonal prediction systems (SPSs) show low skills in predicting European heat extremes on seasonal timescale, especially in central and northern Europe (Prodhomme et al., 2022). However, recent studies have shown that our skills in predicting extratropical climate can be largely improved by subsampling the dynamical SPS ensemble with statistical post-processing techniques (Dobrynin et al., 2022).

This study assesses if the seasonal prediction skill of summer extreme temperatures in Europe in the state-of-the-art dynamical SPSs can be improved through subsampling. Specifically, we use a multi-model ensemble (MME) of SPSs contributing to the Copernicus Climate Change Service (C3S), analysing di hindcast period 1993—2016. The MME is subsampled by retaining a subset of members that predict the phase of the North Atlantic Oscillation (NAO) and the Eastern Atlantic (EA), typically linked to summer extreme temperatures in Europe. The subsampling relies on spring predictors of the weather regimes and thus allows us to retain only those ensemble members with a reasonable representation of summer heat extreme teleconnections.

Results show that by retaining only those ensemble members that accurately represent the NAO phase, it not only enhances the seasonal prediction skills for the summer European climate but also leads to improved predictions of summer extreme temperatures, especially in central and northern Europe. Differently, selecting only those ensemble members that accurately represent the EA phase does not improve either the predictions of summer European climate or the predictions of summer extreme temperatures. This can be explained by the fact that the C3S SPSs exhibits deficiencies in accurately representing the summer low-frequency atmospheric variability.

Bibliography

Dobrynin, M., and Coauthors, 2018: Improved Teleconnection-Based Dynamical Seasonal Predictions of Boreal Winter. Geophysical Research Letters, 45 (8), 3605—3614, https://doi.org/10.1002/2018GL07720

Prodhomme, C., S. Materia, C. Ardilouze, R. H. White, L. Batté, V. Guemas, G. Fragkoulidis, and J. Garcìa-Serrano, 2022: Seasonal prediction of European summer heatwaves. Climate Dynamics, 58 (7), 2149—2166, https://doi.org/10.1007/s00382-021-05828-3

Seneviratne, S., and Coauthors, 2021: Weather and Climate Extreme Events in a Changing Climate, chap. 11, 1513—1766. Cambridge University Press, https://doi.org/10.1017/9781009157896.013

How to cite: Famooss Paolini, L., Ruggieri, P., Pascale, S., Brattich, E., and Di Sabatino, S.: Hybrid statistical-dynamical seasonal prediction of summer extreme temperatures over Europe, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9690, https://doi.org/10.5194/egusphere-egu24-9690, 2024.

EGU24-9905 | ECS | Orals | CL4.3

Optimization-based driver detection and prediction of seasonal heat extremes 

Ronan McAdam, César Peláez Rodríguez, Felicitas Hansen, Jorge Pérez Aracil, Antonello Squintu, Leone Cavicchia, Eduardo Zorita, Sancho Saldez-Sanz, and Enrico Scoccimarro

As a consequence of limited reliability of dynamical forecast systems, particularly over Europe, efforts in recent years have turned to exploiting the power of Machine Learning methods to extract information on drivers of extreme temperature from observations and reanalysis. Meanwhile, the diverse impacts of extreme heat have driven development of new indicators which take into account nightime temperatures and humidity. In the H2020 CLimate INTelligence (CLINT) project, a feature selection framework is being developed to find the combination of drivers which provides optimal seasonal forecast skill of European summer heatwave indicators. Here, we present the methodology, its application to a range of heatwave indicators and forecast skill compared to existing dynamical systems. First, a range of (reduced-dimensionality) drivers are defined, including k-means clusters of variables known to impact European summer (e.g. precipitation, sea ice content), and more complex indices like the NAO and weather regimes. Then, these drivers are used to train machine learning based prediction models, of varying complexity, to predict seasonal indicators of heatwave occurrence and intensity. A crucial and novel step in our framework is the use of the Coral Reef Optimisation algorithm, used to select the variables and their corresponding lag times and time periods which provide optimal forecast skill. To maximise training data, both ERA5 reanalysis and a 2000-year paleo-simulation are used; the representation of heatwaves and atmospheric conditions are validated with respect to ERA5. We present comparisons of forecast skill to the dynamical Copernicus Climate Change Service seasonal forecasts systems. The differences in timing, predictability and drivers of daytime and nighttime heatwaves across Europe are highlighted. Lastly, we discuss how the framework can easily be adapted to other extremes and timescales.



How to cite: McAdam, R., Peláez Rodríguez, C., Hansen, F., Pérez Aracil, J., Squintu, A., Cavicchia, L., Zorita, E., Saldez-Sanz, S., and Scoccimarro, E.: Optimization-based driver detection and prediction of seasonal heat extremes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9905, https://doi.org/10.5194/egusphere-egu24-9905, 2024.

EGU24-10539 | ECS | Orals | CL4.3

Exploring multiyear-to-decadal North Atlantic sea level predictability using machine learning and analog methods 

Qinxue Gu, Liwei Jia, Liping Zhang, Thomas Delworth, Xiaosong Yang, Fanrong Zeng, and Shouwei Li

Long-term sea level rise and multiyear-to-decadal sea level variations pose substantial risks of flooding and erosion in coastal communities. The North Atlantic Ocean and the U.S. East Coast are hotspots for sea level changes under current and future climates. Here, we employ a machine learning technique, a self-organizing map (SOM)-based framework, to systematically characterize the North Atlantic sea level variability, assess sea level predictability, and generate sea level predictions on multiyear-to-decadal timescales. Specifically, we classify 5000-year North Atlantic sea level anomalies from the Seamless System for Prediction and EArth System Research (SPEAR) model control simulations into generalized patterns using SOM. Preferred transitions among these patterns are further identified, revealing long-term predictability on multiyear-to-decadal timescales related to shifts in Atlantic meridional overturning circulation (AMOC) phases. By combining the SOM framework with “analog” techniques based on the simulations and observational/reanalysis data, we demonstrate prediction skill of large-scale sea level patterns comparable to that from initialized hindcasts. Moreover, additional source of short-term predictability is identified after the exclusion of low-frequency AMOC signals, which arises from the wind-driven North Atlantic tripole mode triggered by the North Atlantic Oscillation. This study highlights the potential of machine learning methods to assess sources of predictability and to enable efficient, long-term climate prediction.

How to cite: Gu, Q., Jia, L., Zhang, L., Delworth, T., Yang, X., Zeng, F., and Li, S.: Exploring multiyear-to-decadal North Atlantic sea level predictability using machine learning and analog methods, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10539, https://doi.org/10.5194/egusphere-egu24-10539, 2024.

The inter-annual to multi-decadal variability of recurrent, synoptic-scale atmospheric circulation patterns in the Northern Hemisphere extratropics, as represented by the Jenkinson-Collison classification scheme, is explored in reanalysis data spanning the entire 20th century, and in global climate model (GCM) data from the historical, AMIP and DCPP experiments conducted within the framework of CMIP6. The aim of these efforts is to assess the effect of coupled vs. uncoupled and initialised vs. non-initialized GCM simulations in reproducing the observed low-frequency variability of the aforementioned circulation patterns.

Results reveal that the observed annual counts of typical recurrent weather patterns, such as cyclonic or anticyclonic conditions and also situations of pronounced advection, exhibit significant oscillations on multiple time-scales ranging between several years and several decades. The period of these oscillations, however, is subject to large regional variations. This is in line with earlier studies suggesting that the extratropical atmospheric circulation’s low frequency variability is essentially unforced, except in the Pacific-North American sector where the forced variability is enhanced due to ENSO teleconnections. Neither the periods obtained from historical nor those obtained from AMIP experiments align with observations. Likewise, not even the periods obtained from different runs of the same GCM and experiment correspond to each other. Thus, in an non-initialized model setup, ocean-atmosphere coupling or the lack thereof essentially leads to the same results. Whether initialization and/or augmenting the ensemble size can improve these findings, will also be discussed.

Acknowledgement: This work is part of project Impetus4Change, which has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement No 101081555.

How to cite: Brands, S., Cimadevilla, E., and Fernández, J.: Low-frequency variability of synoptic-scale atmospheric circulation patterns in the Northern Hemisphere extratropics and associated hindcast skill of decadal forecasting systems, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10551, https://doi.org/10.5194/egusphere-egu24-10551, 2024.

EGU24-10574 | Orals | CL4.3 | Highlight

Will 2024 be the first year above 1.5 C? 

Nick Dunstone, Doug Smith, Adam Scaife, Leon Hermanson, Andrew Colman, and Chris Folland

Global mean surface temperature is the key metric by which our warming climate is monitored and for which international climate policy is set. At the end of each year the Met Office makes a global mean temperature forecast for the coming year. Following on from the new record 2023, we predict a high probability of another record year in 2024 and a 35% chance of exceeding 1.5 C above pre-industrial. Whilst a one-year temporary exceedance of 1.5 C would not constitute a breech of the Paris Agreement target, our forecast highlights how close we are now to breeching this target. We show that our 2024 forecast can be largely explained by the combination of the continuing warming trend of +0.2 C/decade and the lagged warming affect of a strong tropical Pacific El Nino event. We further highlight 2023 was significantly warmer than forecast and that much of this warming signal came from the southern hemisphere and requires further understanding.

How to cite: Dunstone, N., Smith, D., Scaife, A., Hermanson, L., Colman, A., and Folland, C.: Will 2024 be the first year above 1.5 C?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10574, https://doi.org/10.5194/egusphere-egu24-10574, 2024.

EGU24-11485 | ECS | Orals | CL4.3

Summer drought predictability in the Mediterranean region in seasonal forecasts 

Giada Cerato, Katinka Bellomo, and Jost von Hardenberg

The Mediterranean region has been identified as an important climate change hotspot, over the 21st century both air temperature and its extremes are projected to rise at a rate surpassing that of the global average and a significant decrease of average summer precipitation is projected, particularly for the western Mediterranean. On average, Mediterranean droughts have become more frequent and intense in recent years and are expected to become more widespread in many regions. These prolonged dry spells pose a substantial threat to agriculture and impact several socio-economic sectors. In this context, long-range weather forecasting has emerged as a promising tool for seasonal drought risk assessment. However, the interpretation of the forecasting products is not always straightforward due to their inherent probabilistic nature. Therefore, a rigorous evaluation process is needed to determine the extent to which these forecasts provide a fruitful advantage over much simpler forecasting systems, such as those based on climatology. 

In this study, we use the latest version of ECMWF’s seasonal prediction system (SEAS5) to understand its skill in predicting summer droughts. The Standardized Precipitation Evapotranspiration Index (SPEI) aggregated over different lead times is employed to mark below-normal dryness conditions in August. We use a comprehensive set of evaluation metrics to gain insight into the accuracy, systematic biases, association, discrimination and sharpness of the forecast system. Our findings reveal that up to 3 months lead time, seasonal forecasts show stronger association and discrimination skills than the climatological forecast, especially in the Southern Mediterranean, although the prediction quality in terms of accuracy and sharpness is limited. On the other hand, extending the forecast range up to 6 months lead time dramatically reduces its predictability skill, with the system mostly underperforming elementary climatological predictions. 

This approach is then extended to examine the full ensemble of seasonal forecasting systems provided by the Copernicus Climate Change Service (C3S) to test their skill in predicting droughts. Our findings can help an informed use of seasonal forecasts of droughts and the development of related climate services.

How to cite: Cerato, G., Bellomo, K., and von Hardenberg, J.: Summer drought predictability in the Mediterranean region in seasonal forecasts, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11485, https://doi.org/10.5194/egusphere-egu24-11485, 2024.

EGU24-11930 | ECS | Posters on site | CL4.3

A global empirical system for probabilistic seasonal climate prediction based on generative AI and CMIP6 models  

Lluís Palma, Alejandro Peraza, Amanda Duarte, David Civantos, Stefano Materia, Arijit Nandi, Jesús Peña-Izquierdo, Mihnea Tufis, Gonzalo Vilella, Laia Romero, Albert Soret, and Markus Donat

Reliable probabilistic information at the seasonal time scale is essential across various societal sectors, such as agriculture, energy, or water management. Current applications of seasonal predictions rely on General Circulation Models (GCMs) that represent dynamical processes in the atmosphere, land surface, and ocean while capturing their linear and nonlinear interactions. However, GCMs come with an inherent high computational cost. In an operational setup, they are typically run once a month and at a lower temporal and spatial resolution than the ones needed for regional applications. Moreover, GCMs suffer from significant drifts and biases and can miss relevant teleconnections, resulting in low skill for particular regions or seasons. 

In this context, the use of generative AI methods that can model complex nonlinear relationships can be a viable alternative for producing probabilistic predictions with low computational demand. Such models have already demonstrated their effectiveness in different domains, i.e. computer vision, natural language processing, and weather prediction. However, although requiring less computational power, these techniques still rely on big datasets in order to be efficiently trained. Under this scenario, and with sufficiently high-quality global observational datasets spanning at most 70 years, the research trend has evolved into training these models using climate model output. 

In this work, we build upon the work presented by Pan et al., 2022, which introduced a conditional Variational Autoencoder (cVAE) to predict global temperature and precipitation fields for the October to March season starting from July initial conditions. We adopt several pre-processing changes to account for different biases and trends across the CMIP6 models. Additionally, we explore different architecture modifications to improve the model's performance and stability. We study the benefits of our model in predicting three-month anomalies on top of the climate change trend. Finally, we compare our results with a state-of-the-art GCM (SEAS5) and a simple empirical system based on the linear regression of classical seasonal indices based on Eden et al., 2015.

 

Pan, Baoxiang, Gemma J. Anderson, André Goncalves, Donald D. Lucas, Céline J.W. Bonfils, and Jiwoo Lee. 'Improving Seasonal Forecast Using Probabilistic Deep Learning'. Journal of Advances in Modeling Earth Systems 14, no. 3 (1 March 2022). https://doi.org/10.1029/2021MS002766.


Eden, J. M., G. J. van Oldenborgh, E. Hawkins, and E. B. Suckling. 'A Global Empirical System for Probabilistic Seasonal Climate Prediction'. Geoscientific Model Development 8, no. 12 (11 December 2015): 3947–73. https://doi.org/10.5194/gmd-8-3947-2015.

How to cite: Palma, L., Peraza, A., Duarte, A., Civantos, D., Materia, S., Nandi, A., Peña-Izquierdo, J., Tufis, M., Vilella, G., Romero, L., Soret, A., and Donat, M.: A global empirical system for probabilistic seasonal climate prediction based on generative AI and CMIP6 models , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11930, https://doi.org/10.5194/egusphere-egu24-11930, 2024.

EGU24-12969 | ECS | Orals | CL4.3

How unusual is the recent decade-long pause in Arctic summer sea ice retreat? 

Patricia DeRepentigny, François Massonnet, Roberto Bilbao, and Stefano Materia

The Earth has warmed significantly over the past 40 years, and the fastest rate of warming has occurred in and around the Arctic. The warming of northern high latitudes at a rate of almost four times the global average (Rantanen et al., 2022), known as Arctic amplification, is associated with sea ice loss, glacier retreat, permafrost degradation, and expansion of the melting season. Since the mid-2000s, summer sea ice has exhibited a rapid decline, reaching record minima in September sea ice area in 2007 and 2012. However, after the early 2010s, the downward trend of minimum sea ice area appears to decelerate (Swart et al., 2015; Baxter et al., 2019). This apparent slowdown and the preceding acceleration in the rate of sea ice loss are puzzling in light of the steadily increasing rate of greenhouse gas emissions of about 4.5 ppm yr−1 over the past decade (Friedlingstein et al., 2023) that provides a constant climate forcing. Recent studies suggest that low-frequency internal climate variability may have been as important as anthropogenic influences on observed Arctic sea ice decline over the past four decades (Dörr et al., 2023; Karami et al., 2023). Here, we investigate how unusual this decade-long pause in Arctic summer sea ice decline is within the context of internal climate variability. To do so, we first assess how rare this is deceleration of Arctic sea ice loss is by comparing it to trends in CMIP6 historical simulations. We also use simulations from the Decadal Climate Prediction Project (DCPP) contribution to CMIP6 to determine if initializing decadal prediction systems from estimates of the observed climate state substantially improves their performance in predicting the slowdown in Arctic sea ice loss over the past decade. As the DCPP does not specify the data or the methods to be used to initialize forecasts or how to generate ensembles of initial conditions, we also assess how different formulations affect the skill of the forecasts by analyzing differences between models. This work provides an opportunity to attribute this pause in Arctic sea ice retreat to interannual internal variability or radiative external forcings, something that observation analysis alone cannot achieve.

How to cite: DeRepentigny, P., Massonnet, F., Bilbao, R., and Materia, S.: How unusual is the recent decade-long pause in Arctic summer sea ice retreat?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12969, https://doi.org/10.5194/egusphere-egu24-12969, 2024.

EGU24-14341 | Posters on site | CL4.3

Compound Heat and Dry Events Influenced by the Pacific–Japan Pattern over Taiwan in Summer 

Szu-Ying Lin, Wan-Ling Tseng, Yi-Chi Wang, and MinHui Lo

Compound dry and hot events, characterized by elevated temperatures and reduced precipitation, pose interconnected challenges to human social economics, necessitating comprehensive strategies for mitigation and adaptation. This study focuses on the Pacific-Japan (PJ) pattern, a significant climate variability influencing summer climates in East Asia. While previous research has explored its impact on Japan and Korea, our investigation delves into its effects on Taiwan, a mountainous subtropical island with a population of approximately 24 million. Utilizing long-term temperature and rainfall data, along with reanalysis dynamic downscaling datasets, we examine the interannual impacts of the PJ pattern on summer temperature and compound heat and dry events. Our findings reveal a significant temperature increase during the positive phase of the PJ pattern, characterized by anticyclonic anomalous circulation over Taiwan. Additionally, both the Standardized Precipitation Index and soil water exhibit a decline during this phase, reflecting meteorological and hydrological drought conditions. A robust negative correlation (-0.7) between drought indices and temperature emphasizes the compound effect of heat and dry events during the PJ positive phase. This study enhances the understanding of the PJ pattern as a climate driver, describing its role in hot and dry summers over Taiwan. The insights gained, when integrated into seasonal prediction and early warning systems, can aid vulnerable sectors in preparing for potential heat and dry stress hazards.

How to cite: Lin, S.-Y., Tseng, W.-L., Wang, Y.-C., and Lo, M.: Compound Heat and Dry Events Influenced by the Pacific–Japan Pattern over Taiwan in Summer, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14341, https://doi.org/10.5194/egusphere-egu24-14341, 2024.

EGU24-14379 | Posters on site | CL4.3

Linkage between Temperature and Heatwaves in Summer Taiwan to the Pacific Meridional Mode 

Chieh-Ting Tsai, Wan-Ling Tseng, and Yi-Chi Wang

Over the past century, Taiwan has gradually recognized the hazards posed by extreme heat events (EHT), prompting the development of mid-term adaptation strategies to address challenges in the coming decades. However, our understanding of decadal-scale temperature variations remains insufficient, requiring further research into influencing factors. Our study reveals the crucial role of the Pacific Meridional Mode (PMM) in modulating decadal-scale variations in summer temperatures in Taiwan. During the positive phase of PMM, warm sea surface temperature anomalies trigger an eastward-moving wave train extending into East Asia. This leads to the development of high-pressure circulations near Southeast Asia and Taiwan, enhancing the temperature increase. This mechanism has been reproduced in experiments using the Taiwan Earth System Model. Moreover, our study utilizes the calendar day 90th percentile of maximum temperature (CTX) as the threshold for extreme high-temperature events (EHT), while also employing the heatwaves magnitude scale (HWMS) as the criterion for defining heatwaves. During the positive phase of PMM, the frequency and duration of EHT increase, with variations observed across different regions. The overall intensity of heatwave events also strengthens, primarily due to extended durations. Notably, in a single city, this results in exposure of up to 800,000 person-days to EHT, presenting a tenfold increase compared to the annual effect observed in the long-term warming trend. These findings on the decadal-scale relationship between summer temperatures in Taiwan and PMM contribute to a deeper understanding of EHT and heatwaves events impacts, providing more nuanced insights for future regional strategies in mitigating heatwave disasters.

How to cite: Tsai, C.-T., Tseng, W.-L., and Wang, Y.-C.: Linkage between Temperature and Heatwaves in Summer Taiwan to the Pacific Meridional Mode, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14379, https://doi.org/10.5194/egusphere-egu24-14379, 2024.

EGU24-14688 | ECS | Orals | CL4.3

Exploring ML-based decadal predictions of the German Bight storm surge climate 

Daniel Krieger, Sebastian Brune, Johanna Baehr, and Ralf Weisse

Storm surges and elevated water levels regularly challenge coastal protection and inland water management along the low-lying coastline of the German Bight. Skillful seasonal-to-decadal (S2D) predictions of the local storm surge climate would be beneficial to stakeholders and decision makers in the region. While storm activity has recently been shown to be skillfully predictable on a decadal timescale with a global earth system model, surge modelling usually requires very fine spatial and temporal resolutions that are not yet present in current earth system models. We therefore propose an alternative approach to generating S2D predictions of the storm surge climate by training a neural network on observed water levels and large-scale atmospheric patterns, and apply the neural network to the available model output of a S2D prediction system. We show that the neural-network-based translation from large-scale atmospheric fields to local water levels at the coast works sufficiently well, and that several windows of predictability for the German Bight surge climate emerge on the S2D scale.

How to cite: Krieger, D., Brune, S., Baehr, J., and Weisse, R.: Exploring ML-based decadal predictions of the German Bight storm surge climate, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14688, https://doi.org/10.5194/egusphere-egu24-14688, 2024.

Atlantic meridional overturning circulation (AMOC) is one of the mechanisms for climate predictability and one of the properties that decadal climate predictions are attempting to predict. The starting point for AMOC decadal predictions is sensitive to the underlying data assimilation and/or initialization procedure. This means that different choices during the data assimilation procedure (e.g., assimilation method, assimilation window, data sources, resolution, nudging terms and strength, full field vs anomaly initialization/assimilation, etc) can result in a different mean and even variability of reconstructed ocean circulation. How coherent the AMOC initial states should be among the CMIP-like decadal prediction experiments? How good in general should the initial AMOC be for decadal predictions? And do initialization issues of the ocean circulation influence the prediction skill of other variables that are of interest for application studies? These are the questions that we were attempting to address in our study, where we analyzed twelve decadal prediction systems from the World Meteorological Organization Lead Centre for Annual-to-Decadal Climate Prediction project. We identify that the AMOC initialization influences the quality of predictions of the subpolar gyre (SPG). When predictions show a large initial error in their AMOC, they usually have low skill for predicting the internal variability of the SPG five years after the initialization.

How to cite: Polkova, I. and the Co-Authors: Initialization shock in the ocean circulation reduces skill in decadal predictions of the North Atlantic subpolar gyre, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15358, https://doi.org/10.5194/egusphere-egu24-15358, 2024.

EGU24-15476 | Posters on site | CL4.3

Statistics of sudden stratospheric warmings using a large model ensemble 

Sarah Ineson, Nick Dunstone, Adam Scaife, Martin Andrews, Julia Lockwood, and Bo Pang

Using a large ensemble of initialised retrospective forecasts (hindcasts) from a seasonal prediction system, we explore various statistics relating to sudden stratospheric warmings (SSWs). Observations show that SSWs occur at a similar frequency during both El Niño and La Niña northern hemisphere winters. This is contrary to expectation, as the stronger stratospheric polar vortex associated with La Niña years might be expected to result in fewer of these extreme breakdowns. We show that this similar frequency may have occurred by chance due to the limited sample of years in the observational record. We also show that in these hindcasts, winters with two SSWs, a rare event in the observational record, on average have an increased surface impact. Multiple SSW events occur at a lower rate than expected if events were independent but somewhat surprisingly, our analysis also indicates a risk, albeit small, of winters with three or more SSWs, as yet an unseen event.

How to cite: Ineson, S., Dunstone, N., Scaife, A., Andrews, M., Lockwood, J., and Pang, B.: Statistics of sudden stratospheric warmings using a large model ensemble, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15476, https://doi.org/10.5194/egusphere-egu24-15476, 2024.

EGU24-15709 | ECS | Orals | CL4.3

Predicting Atlantic and Benguela Niño events with deep learning  

Marie-Lou Bachelery, Julien Brajard, Massimiliano Patacchiola, and Noel Keenlyside

Extreme Atlantic and Benguela Niño events continue to significantly impact the tropical Atlantic region, with far-reaching consequences for African climate and ecosystems. Despite attempts to forecast these events using traditional seasonal forecasting systems, success remains low, reinforcing the growing idea that these events are unpredictable. To overcome the limitations of dynamical prediction systems, we introduce a deep learning-based statistical prediction model for Atlantic and Benguela Niño events. Our convolutional neural network (CNN) model, trained on 90 years of reanalysis data incorporating surface and 100m-averaged temperature variables, demonstrates the capability to forecast the Atlantic and Benguela Niño indices with lead times of up to 3-4 months. Notably, the CNN model excels in forecasting peak-season events with remarkable accuracy extending up to 5 months ahead. Gradient sensitivity analysis reveals the ability of the CNN model to exploit known physical precursors, particularly the connection to equatorial dynamics and the South Atlantic Anticyclone, for accurate predictions of Benguela Niño events. This study challenges the perception of the Tropical Atlantic as inherently unpredictable, underscoring the potential of deep learning to enhance our understanding and forecasting of critical climate events. 

How to cite: Bachelery, M.-L., Brajard, J., Patacchiola, M., and Keenlyside, N.: Predicting Atlantic and Benguela Niño events with deep learning , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15709, https://doi.org/10.5194/egusphere-egu24-15709, 2024.

EGU24-15974 | ECS | Posters virtual | CL4.3

Recalibrating DWD’s operational climate predictions: towards a user-oriented seamless climate service 

Alexander Pasternack, Birgit Mannig, Andreas Paxian, Amelie Hoff, Klaus Pankatz, Philip Lorenz, and Barbara Früh

The German Meteorological Service's (Deutscher Wetterdienst DWD) climate predictions website  (www.dwd.de/climatepredictions) offers a centralized platform for accessing post-processed climate predictions, including subseasonal forecasts from ECMWF's IFS and seasonal and decadal predictions from the German climate prediction system. The website design was developed in collaboration with various sectors to ensure uniformity across all time frames, and users can view maps, tables, and time series of ensemble mean and probabilistic predictions in combination with their skill. The available data covers weekly, 3-month, 1-year, and 5-year temperature means, precipitation sums and soil moisture for the world, Europe, Germany, and particular German regions. To achieve high spatial resolution, the DWD used the statistical downscaling method EPISODES. Moreover, within the BMBF project KIMoDIs (AI-based monitoring, data management and information system for coupled forecasting and early warning of low groundwater levels and salinisation) the DWD provides climate prediction data of further hydrological variables (e.g. relative humidity) with corresponding prediction skill on a regional scale.

However, all predictions on these time scales can suffer from inherent systematic errors, which can impact their usefulness. To address these issues, the recalibration method DeFoReSt was applied to decadal predictions, using a combination of 3rd order polynomials in lead and start time, along with a boosting model selection approach. This approach addresses lead-time dependent systematic errors, such as drift, as well as inaccuracies in representing long-term changes and variability.

This study highlights the improved accuracy of the recalibration approach on decadal predictions due to an increased polynomial order compared to the original approach, and its different impact on global and regional scales. It also explores the feasibility of transferring this approach to predictions with shorter time horizons of the provided variables.

How to cite: Pasternack, A., Mannig, B., Paxian, A., Hoff, A., Pankatz, K., Lorenz, P., and Früh, B.: Recalibrating DWD’s operational climate predictions: towards a user-oriented seamless climate service, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15974, https://doi.org/10.5194/egusphere-egu24-15974, 2024.

EGU24-16366 | ECS | Orals | CL4.3

Decadal predictions outperform projections in forecasting winter precipitation over the Mediterranean region 

Dario Nicolì, Silvio Gualdi, and Panos Athanasiadis

The Mediterranean region is highly sensitive to climate change, having experienced an intense warming and drying trend in recent decades, primarily due to the increased concentrations of anthropogenic greenhouse gases. In the context of decision-making processes, there is a growing interest in understanding the near-term climate evolution of this region.

In this study, we explore the climatic fluctuations of the Mediterranean region in the near-term range (up to 10 years ahead) using two different products: projections and decadal predictions. The former are century-scale climate change simulations initialized from arbitrary model states to which were applied anthropogenic and natural forcings. A major limitation of climate projections is their limited information regarding the current state of the Earth’s climate system. Decadal climate predictions, obtained by constraining the initial conditions of an ensemble of model simulations through a best estimate of the observed climate state, provide a better understanding of the next-decade climate and thus represent an invaluable tool in assisting climate adaptation.

Using retrospective forecasts from eight decadal prediction systems contributing to the CMIP6 Decadal Climate Prediction Project (CMIP6 DCPP) and the corresponding ensemble of non-initialized projections, we compare the capabilities of the state-of-the-art climate models in predicting future climate changes of the Mediterranean region for some key quantities so as to assess the added value of initialization. 

Beyond the contribution of external forcings, the role of internal variability is also investigated since part of the detected predictability arises from internal climate variability patterns affecting the Mediterranean. The observed North Atlantic Oscillation, the dominant climate variability pattern in the Euro-Atlantic domain, as well as its  impact on wintertime precipitation over Europe are well reproduced by decadal predictions, especially over the Mediterranean, outperforming projections. We also apply a sub-sampling method to enhance the respective signal-to-noise ratio and consequently improve precipitation skill over the Mediterranean.

How to cite: Nicolì, D., Gualdi, S., and Athanasiadis, P.: Decadal predictions outperform projections in forecasting winter precipitation over the Mediterranean region, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16366, https://doi.org/10.5194/egusphere-egu24-16366, 2024.

EGU24-16985 | Posters on site | CL4.3

Investigating signals in summer seasonal forecasts over the North Atlantic/European region 

Julia Lockwood, Nick Dunstone, Kristina Fröhlich, Ramón Fuentes Franco, Anna Maidens, Adam Scaife, Doug Smith, and Hazel Thornton

The current generation of seasonal forecast models struggle to skilfully predict dynamical circulation over the North Atlantic and European region in boreal summer.  Using two different state-of-the-art seasonal prediction systems, we show that tropical rainfall anomalies drive a circulation signal in the North Atlantic/Europe via the propagation of Rossby waves.  The wave, however, is shifted eastwards compared to observations, so the signal does not contribute positively to model skill.  Reasons for the eastward shift of the Rossby wave are investigated, as well as other drivers of the signal in this region.  Despite the errors in the waves, the fact that seasonal forecast models do predict dynamical signals over the North Atlantic/Europe signifies seasonal predictability over this region beyond the climate change trend, and understaning the cause of the errors could lead to skilful predictions.

How to cite: Lockwood, J., Dunstone, N., Fröhlich, K., Fuentes Franco, R., Maidens, A., Scaife, A., Smith, D., and Thornton, H.: Investigating signals in summer seasonal forecasts over the North Atlantic/European region, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16985, https://doi.org/10.5194/egusphere-egu24-16985, 2024.

EGU24-17418 | Posters on site | CL4.3

Strengthening seasonal forecasting in the Middle East & North Africa (MENA) through the WISER Programme. 

Stefan Lines, Nicholas Savage, Rebecca Parfitt, Andrew Colman, Alex Chamberlain-Clay, Luke Norris, Heidi Howard, and Helen Ticehurst

In this presentation, we introduce the WISER MENA projects SeaFOAM (Seasonal Forecasting Across MENA) and SeaSCAPE (Seasonal Co-Production and Application in MENA). These projects explore both the improvement to the regional-level seasonal forecast in the MENA region, as well as how to tailor the information in ways useful to a range of climate information stakeholders. SeaFOAM works alongside Maroc Meteo, Morocco's National Meteorological and Hydrological Service (NMHS) and the Long Range Forecasting node of the Northern Africa WMO Regional Climate Centre (RCC), to develop a framework for objective seasonal forecasting. This approach will blend techniques such as bias correction via local linear regression and canonical correlation analysis (CCA), with skill-assessed sub-selected models, to improve forecasting accuracy. Multiple drivers of rainfall variability, including the North Atlantic Oscillation (NAO) and Mediterranean Oscillation (MO), are investigated for their calibration potential. SeaSCAPE works with the WMO and various partners across MENA to understand the use of seasonal information in multiple sectors, exploring existing gaps and needs. Through stakeholder engagement workshops, training and bespoke support for the Arab Climate Outlook Forum (ArabCOF), SeaSCAPE operates collaboratively to tailor regional and national-level climate information to improve accessibility and usability of climate information on seasonal timescales.

How to cite: Lines, S., Savage, N., Parfitt, R., Colman, A., Chamberlain-Clay, A., Norris, L., Howard, H., and Ticehurst, H.: Strengthening seasonal forecasting in the Middle East & North Africa (MENA) through the WISER Programme., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17418, https://doi.org/10.5194/egusphere-egu24-17418, 2024.

EGU24-17585 | Orals | CL4.3

Skill of wind resource forecasts on the decadal time scale 

Kai Lochbihler, Ana Lopez, and Gil Lizcano

Accurate forecasts of the natural resources of renewable energy production have become not only a valuable but a crucial tool for managing the associated risks of specific events, such as wind droughts. Wind energy, alongside with solar power, now provide a substantial part to the renewable energy share of the global energy production and growth in this sector will most likely further increase. The naturally given fluctuations of wind resources, however, pose a challenge for maintaining a stable energy supply, which, at the end of the chain, can have an impact on the energy market prices.
Operational short-term forecasting products for the wind energy sector (multiple days) are already commonly available and seasonal to sub seasonal forecasting solutions (multiple months) can provide valuable skill and are gaining in popularity. On the other side of the spectrum, typically on a time scale of multiple decades, we find risk assessment based on climate change projections. In between the long and short term time scales, however, there is a gap that still needs to be filled to achieve seamless prediction of risks that are relevant for the energy sector: decadal predictions.

Here, we present the results of an evaluation study of a multi-model decadal prediction ensemble (DCPP) for a selection of wind development regions in Europe. The evaluation is based on multiple decades long hindcasts and carried out with a focus on the skill of predicting specific event types of wind resource availability in a probabilistic context, alongside with basic deterministic skill measures. We further investigate specific event constellations and their large-scale drivers that, in combination, can provide windows of opportunity with enhanced predictive skill. We conclude with a discussion on how this hybrid approach can be used to potentially increase not only forecast skill but also the trust of the end user.

How to cite: Lochbihler, K., Lopez, A., and Lizcano, G.: Skill of wind resource forecasts on the decadal time scale, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17585, https://doi.org/10.5194/egusphere-egu24-17585, 2024.

EGU24-19229 | ECS | Orals | CL4.3

Comparing the seasonal predictability of Tropical Pacific variability in EC-Earth3 at two different horizontal resolutions 

Aude Carreric, Pablo Ortega, Vladimir Lapin, and Francisco Doblas-Reyes

Seasonal prediction is a field of research attracting growing interest beyond the scientific community due to its strong potential to guide decision-making in many sectors (e.g. agriculture and food security, health, energy production, water management, disaster risk reduction) in the face of the pressing dangers of climate change.

Among the various techniques being considered to improve the predictive skill of seasonal prediction systems, increasing the horizontal resolution of GCMs is a promising avenue. There are several indications that higher resolution versions of the current generation of climate models might improve key air-sea teleconnections, decreasing common biases of global models and improving the skill to predict certain regions at seasonal scales, e.g. in tropical sea surface temperature.

In this study, we analyze the differences in the predictive skill of two different seasonal prediction systems, based on the same climate model EC-Earth3 and initialized in the same way but using two different horizontal resolutions. The standard (SR) and high resolution (HR) configurations are based on an atmospheric component, IFS, of ~100 km and ~40 km of resolution respectively and on an ocean component, NEMO3.6, of ~100 km and ~25 km respectively. We focus in particular on the Tropical Pacific region where statistically significant improvements are found in HR with respect to SR for predicting ENSO and its associated climate teleconnections. We explore some processes that can explain these differences, such as the simulation of the tropical ocean mean state and atmospheric teleconnections between the Atlantic and Pacific tropical oceans. 

A weaker mean-state bias in the HR configuration, with less westward extension of ENSO-related SST anomalies, leads to better skill in ENSO regions, which can also be linked to better localization of the atmospheric teleconnection with the equatorial Atlantic Ocean. It remains to be assessed if similar improvements are consistently identified for HR versions in other forecast systems, which would prompt their routine use in seasonal climate prediction.

How to cite: Carreric, A., Ortega, P., Lapin, V., and Doblas-Reyes, F.: Comparing the seasonal predictability of Tropical Pacific variability in EC-Earth3 at two different horizontal resolutions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19229, https://doi.org/10.5194/egusphere-egu24-19229, 2024.

EGU24-19251 | Orals | CL4.3 | Highlight

The opportunities and challenges of near-term climate prediction 

Hazel Thornton

Accurate forecasts of the climate of the coming season and years are highly desired by many sectors of society. The skill of near-term climate prediction in winter in the North Atlantic and European region has improved over the last decade associated with larger ensembles, improving models and boosting of the prediction signal using intelligent post processing. International collaboration has improved the availability of forecasts and promoted the uptake of forecasts by different sectors. However, significant challenges remain, including summer prediction, understanding the risk of extremes within a season, multi-seasonal extremes and how best to post process the forecasts to aid decision making. This talk will summarise recent near-term climate prediction research activities at the UK Met Office and will detail our experience of providing such forecasts to the energy and water sectors.  

How to cite: Thornton, H.: The opportunities and challenges of near-term climate prediction, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19251, https://doi.org/10.5194/egusphere-egu24-19251, 2024.

This study focuses on applying machine learning techniques to bias-correct the seasonal temperature forecasts provided by the Copernicus Climate Change Service (C3S) models. Specifically, we employ bias correction on forecasts from five major models: UK Meteorological Office (UKMO), Euro-Mediterranean Center on Climate Change (CMCC), Deutscher Wetterdienst (DWD), Environment and Climate Change Canada (ECCC), and Meteo-France. Our primary objective is to assess the performance of our bias correction model in comparison to the original forecast datasets. We utilise temperature-based indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI) to evaluate the effectiveness of the bias-corrected seasonal forecasts. These indices served as valuable metrics to gauge the predictive capability of the models, especially in forecasting natural cascading hazards such as wildfires, droughts, and floods. The study involved an in-depth analysis of the bias-corrected forecasts, and the derived indices were crucial in understanding the models' ability to predict temperature-related extreme events. The results of this research contribute valuable information for decision-making and planning across various sectors, including disaster risk management and environmental protection. Through a comprehensive evaluation of machine learning-based bias correction techniques, we enhance the accuracy and applicability of seasonal temperature forecasts, thereby improving preparedness and resilience to climate-related challenges. 

How to cite: Mbuvha, R. and Nikraftar, Z.: Machine Learning Approaches to Improve Accuracy in Extreme Seasonal Temperature Forecasts: A Multi-Model Assessment , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19297, https://doi.org/10.5194/egusphere-egu24-19297, 2024.

EGU24-19359 | ECS | Posters on site | CL4.3

Seasonal forecast of the late boreal winter temperature based on solar forcing and QBO 

Mikhail Vokhmianin, Antti Salminen, Kalevi Mursula, and Timo Asikainen

The ground temperature variability in the Northern Hemisphere winter is greatly influenced by the state of the polar vortex. When the vortex collapses during sudden stratospheric warmings (SSWs), rapid changes in stratospheric circulations propagate downward to the troposphere in the subsequent weeks. The ground effect following SSWs is typically manifested as the negative phase of the North Atlantic Oscillation. Our findings reveal a higher frequency of cold temperature anomalies in the Northern part of Eurasia during winters with SSWs, and conversely, warm anomalies in winters with a strong and stable vortex. This behavior is particularly evident when temperature anomalies are categorized into three equal subgroups, or terciles. Recently, we developed a statistical model that successfully predicts SSW occurrences with an 86% accuracy rate. The model utilizes the stratospheric Quasi-Biennial Oscillation (QBO) phase and two parameters associated with solar activity: the geomagnetic aa-index as a proxy for energetic particle precipitations and solar irradiance. In this study, we explore the model's potential to provide a seasonal forecast for ground temperatures. We assess the probabilities of regional temperature anomalies falling into the lowest or highest terciles based on the predicted weak or strong vortex state. Additionally, we demonstrate that the QBO phase further enhances the forecast quality. As the model provides SSW predictions as early as preceding August, our results carry significant societal relevance as well, e.g., for the energy sector, which is highly dependent on prevailing weather conditions.

How to cite: Vokhmianin, M., Salminen, A., Mursula, K., and Asikainen, T.: Seasonal forecast of the late boreal winter temperature based on solar forcing and QBO, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19359, https://doi.org/10.5194/egusphere-egu24-19359, 2024.

EGU24-33 | ECS | Posters on site | AS1.5

Improved Diurnal Cycle in GFDL Earth System Models with Non-Equilibrium Convection 

Bosong Zhang, Leo Donner, Ming Zhao, and Zhihong Tan

Most global climate models with convective parameterization have trouble in simulating the observed diurnal cycle of convection. Maximum precipitation usually happens too early during local summertime, especially over land. Observational analyses indicate that deep convection over land cannot keep pace with rapid variations in convective available potential energy (CAPE), which is largely controlled by boundary layer forcing. In this study, a new convective closure in which shallow and deep convection interact strongly, out of equilibrium, is implemented in atmosphere-only and ocean-atmosphere coupled models developed at the NOAA Geophysical Fluid Dynamics Laboratory (GFDL). The diurnal cycles of convection in both simulations are significantly improved without altering their mean states. These improvements in the diurnal cycle of these climate models are consistent with those obtained by Peter Bechtold and colleagues in the ECMWF Integrated Forecasting System. The new closure shifts maximum precipitation over land later by about three hours. Compared to satellite observations, the diurnal phase biases are reduced by half. Shallow convection to some extent equilibrates rapid changes in the boundary layer at sub-diurnal time scales. Future model improvement will focus on the remaining biases, which may be further reduced by including stochastic entrainment and cold pools.

How to cite: Zhang, B., Donner, L., Zhao, M., and Tan, Z.: Improved Diurnal Cycle in GFDL Earth System Models with Non-Equilibrium Convection, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-33, https://doi.org/10.5194/egusphere-egu24-33, 2024.

EGU24-1875 | Orals | AS1.5

Is the fate of Mesoscale Convective Systems written from the start? 

Caroline Muller, Sophie Abramian, Camille Risi, Remy Roca, and Thomas Fiolleau

Mesoscale Convective Systems (MCSs) that become large or have long lifespans contribute disproportionately to extreme rainfall. Gaining a better understanding of the factors that determine whether a system will become large could improve our understanding of extreme weather phenomena. The recent emergence of high-resolution global simulations from the DYAMOND project, coupled with a storm tracking algorithm called TOOCAN, provides a groundbreaking opportunity to study the factors controlling the maximum area of MCSs. In this study we use machine learning algorithms to predict the maximum area of convective systems based on their early development stages and initial environmental conditions. The results reveal that the initial evolution of the system anticipates its maximum area. Factors such as the presence of ice in the system's environment, proximity to surrounding systems, intensity of vertical velocity at 500 hPa, and the migration distance, have been identified as significant factors in improving the accuracy of the prediction. Using a linear model, we investigate the relative role of the environment and of the system itself, in the growth of the system. 

How to cite: Muller, C., Abramian, S., Risi, C., Roca, R., and Fiolleau, T.: Is the fate of Mesoscale Convective Systems written from the start?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1875, https://doi.org/10.5194/egusphere-egu24-1875, 2024.

Future changes in tropical convection will be closely tied to changes in the underlying sea surface temperature (SST) pattern. To understand the convective response to warming in a coupled atmosphere-ocean system, we perform a series of idealized, 20-year radiative-convective equilibrium experiments with a 2D cloud-resolving model coupled to a 25-m slab ocean. The domain length is that of the tropical Pacific basin, and different climates are achieved by varying the parameterized ocean heat transport (q-flux). The simulations are characterized by two distinct regimes of  convection-SST coupling: an oscillatory regime that occurs when the mean SST is near that of the present-day tropical Pacific (27-30 °C), and a non-oscillatory regime at warmer temperatures (>36 °C).

The oscillatory regime is defined by internal, 3°C oscillations in mean SST driven by variations in low cloudiness. During the warming phase of the cycle, SSTs are homogeneous, deep convection occurs in two regions, and low clouds are sparse. During the cooling phase, there are well-defined warm and cold pools, deep convection aggregates into a single region, and expansive low cloud decks act to decrease the mean SST.  

In the warmer, non-oscillating regime, distinct warm and cold pools still form, but convection is no longer limited to the warmest SSTs. Rather, convection develops over cooler SSTs and is then advected to the warm pool by the mean flow. The expansion of deep convection to cooler SSTs impedes low cloud formation over the cold pool and inhibits the low cloud-driven oscillations in mean SST. Changes in sub-cloud buoyancy explain the expansion of the convectively unstable region.

Both regimes (oscillatory and non-oscillatory) can be achieved for the same q-flux depending on initial conditions. Intermediate SSTs (30-36 °C) are unstable on long timescales and eventually revert to one regime or the other. While certain aspects of this behavior are likely sensitive to simulation design, our broader set of experiments suggests potential shifts in convection-SST coupling as the climate warms.

How to cite: Sokol, A., Munteanu, V., and Hartmann, D.: Internal variability, multiple equilibria, and convection-SST coupling in a cloud-resolving model with an interactive ocean, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3218, https://doi.org/10.5194/egusphere-egu24-3218, 2024.

EGU24-3972 | ECS | Posters on site | AS1.5

Modelling the formation of an extreme Australian pyro-convection event and its sensitivities 

Jason Müller, Fabian Senf, and Ina Tegen

During the Australian fire season 2019/2020, an unprecedented amount of smoke aerosol was not only released, but also transported upwards and injected into the tropopause region by so-called pyro-cumulonimbus clouds (pyroCb). The resulting lower stratospheric aerosol loads in early 2020 were comparable to those of the largest volcanic eruptions of the twentieth century. PyroCbs have been identified as the main pathway for biomass burning aerosol into the stratosphere. To study the phenomenon of PyroCbs, simulations of the so-called Australian New Year Super Outbreak are performed with the numerical weather model ICON. Simulations were run in a nested, limited area mode setup, with the smallest domain reaching down to a horizontal grid spacing of 500 m. Within the domain, an idealised fire perturbation was applied for which an additional constant surface sensible heat and water vapour flux was introduced to represent the thermodynamical impacts of the fire. Simulations with this setup were successful in producing fire-induced deep convection with subsequent smoke injection into the lower stratosphere. Preliminary sensitivity experiments show a high sensitivity of the PyroCb properties to initial and boundary conditions. We can show, that especially water vapour emissions, which would originate from evaporating surface water as well as from combustion of organic materials, have a decisive, enhancing impact on the pyro-convection. Moreover, besides the fire intensity, the plume characteristics and smoke injection heights are also closely linked to the background meteorology, in particular. In the long term, the goal is to incorporate the effects of extreme biomass burning emission into large scale climate simulations by taking into account PyroCb activity. However, this will require a very deep understanding of wildfire triggered convection and PyroCb dynamics.  

How to cite: Müller, J., Senf, F., and Tegen, I.: Modelling the formation of an extreme Australian pyro-convection event and its sensitivities, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3972, https://doi.org/10.5194/egusphere-egu24-3972, 2024.

EGU24-4139 | ECS | Posters on site | AS1.5

The Unreasonable Efficiency of Total Rain Evaporation Removal in Triggering Convective Self-Aggregation 

Yi-Ling Hwong and Caroline Muller

The elimination of rain evaporation in the planetary boundary layer (PBL) has been found to lead to convective self-aggregation (CSA) even without radiative feedback (frequently referred to as “moisture memory aggregation”), but the precise mechanisms underlying this phenomenon remain unclear. We conducted cloud-resolving simulations with two domain sizes (L = 128 and 256 km; Δx = 1 and 4 km) with homogenised radiation and progressively reduced rain evaporation in the PBL by multiplying it with a factor 𝛼 = [1.0, 0.8, 0.6, 0.4, 0.2, 0]. Surprisingly, self aggregation only occurred when rain evaporation was almost completely removed (𝛼 ≈ 0). Similar to conventional radiatively-driven aggregation (RDA), a shallow circulation that leads to an upgradient moist static energy transport is present, but in this case it is the additional convective heating resulting from the reduction of evaporative cooling in the moist patch that triggers this circulation, thereafter a dry subsidence intrusion into the PBL in the dry patch takes over and intensifies aggregation. Hence, this type of aggregation should be more appropriately referred to as “convectively-driven aggregation” (CDA). Contrary to RDA, in CDA temperature and moisture anomalies oppose each other in their buoyancy effects, hence explaining the need for near-zero 𝛼 values: only when rain evaporation is almost completely removed can the additional heating trigger aggregation. Lastly, we found radiative cooling and not cold pools to be the leading cause of the domain size dependence of CDA. Runs with similar amounts of cold pools aggregate in the large but not small domain due to stronger radiative cooling rates and concomitant broadening of the range of precipitable water in the larger domain. 

How to cite: Hwong, Y.-L. and Muller, C.: The Unreasonable Efficiency of Total Rain Evaporation Removal in Triggering Convective Self-Aggregation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4139, https://doi.org/10.5194/egusphere-egu24-4139, 2024.

EGU24-5759 | ECS | Posters on site | AS1.5

Climatology, characteristics and forcing mechanisms of warm-season cold-frontal convection in Europe 

George Pacey, Stephan Pfahl, Lisa Schielicke, and Kathrin Wapler

Convection frequently initiates in proximity to cold fronts during the European warm-season and can also be associated with hazards such as flooding, rain, and hail. Despite this, the frequency and underlying processes that drive such events are not well-understood. To understand the typical nature, frequency and forcing mechanisms of convection depending on the region relative to the front, automatic front detection methods, a convective cell detection and tracking dataset (KONRAD), and lightning data are combined between 2007–2016.

The climatology shows that convective cells are most frequent in Germany marginally ahead of the surface front. Furthermore, the 700 hPa frontal line marks the minimum frequency of convection and a shift in regime between cells with a strong diurnal cycle on the cold-side of the 700 hPa front and a weakened diurnal cycle on the warm-side of the 700 hPa front. The results are consistent for lightning data on a sub-European domain. Given cell detection ahead of the surface front, cells are up to 3 times more likely to be associated with a mesocyclone compared to non-cold-frontal cells in Germany. Cells with 55 dBZ cores are over 1.5 times more likely.

To unravel the complex relationships between different predictor variables and the probability of convection a logistic regression model is developed. Feature importance techniques are utilised to understand which variables carry the most importance depending on the region relative to the front. We find solar heating carries more importance towards the model’s predictive power behind the 700 hPa front than ahead of the 700 hPa front. The opposite is true for the elevation term, which acts as a proxy for the influence of orography on convective initiation. By giving the model information on the number of surrounding grid points associated with convection, a proxy for cell interactions, the most skill is added near the surface front.

These results are an important step towards a deeper understanding of the underlying processes that drive cold-frontal convection and improved forecasting.

How to cite: Pacey, G., Pfahl, S., Schielicke, L., and Wapler, K.: Climatology, characteristics and forcing mechanisms of warm-season cold-frontal convection in Europe, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5759, https://doi.org/10.5194/egusphere-egu24-5759, 2024.

Seeley and Wordsworth (2021) showed that in small-domain cloud-resolving simulations the temporal pattern of precipitation transforms in extremely hot climates (≥ 320 K) from quasi-steady to organized episodic deluges, with outbursts of heavy rain alternating with several dry days. They proposed a mechanism for this transition involving increased water vapor greenhouse effect and solar radiation absorption leading to net lower-tropospheric radiative heating. This heating inhibits lower-tropospheric convection and decouples the boundary layer from the upper troposphere during the dry phase, allowing lower-tropospheric moist static energy to build until it discharges, resulting in a deluge. We perform cloud-resolving simulations in polar night and show that the same transition occurs, implying that some revision of their mechanism is necessary. We perform further tests to show that episodic deluges can occur even if the lower-tropospheric radiative heating rate is negative, as long as the magnitude of the upper-tropospheric radiative cooling is about twice as large. We find that in the episodic deluge regime the period can be predicted from the time for radiation and reevaporation to cool the lower atmosphere.

How to cite: Song, X., Abbot, D., and Yang, J.: Critical role of vertical radiative cooling contrast in triggering episodic deluges in small-domain hothouse climates, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5781, https://doi.org/10.5194/egusphere-egu24-5781, 2024.

EGU24-5959 | ECS | Posters on site | AS1.5

Numerical diffusion and turbulent mixing in convective self-aggregation 

Lorenzo Silvestri, Miriam Saraceni, and Paolina Bongioannini Cerlini

Spontaneous aggregation of deep convection is a common feature of idealized numerical simulations of the tropical atmosphere in Radiative-Convective Equilibrium (RCE). However, at coarse grid resolution where deep convection is not fully resolved, the occurrence of this phenomenon is highly sensitive to subgrid-scale processes. This study investigates the role of mixing and entrainment, provided by either the turbulence model or the implicit numerical dissipation, in this phenomenon. The results of two different models, WRF and SAM, have been analysed and compared using different configurations by varying the turbulence models, initial conditions, and horizontal spatial resolution. At a coarse grid resolution of 3 km, the occurrence of Convective Self-Aggregation (CSA) is prevented in models with low numerical diffusivity due to the removal of turbulent mixing, while it is preserved in models with high numerical diffusivity. When refining the horizontal grid resolution to 1 km, which reduces the implicit numerical dissipation, CSA can only be achieved by increasing explicit turbulent mixing. Even with a small amount of shallow clouds, CSA was found to occur in this case. Therefore, this study suggests that the sensitivity of CSA to horizontal grid resolution is not primarily due to the corresponding decrease in shallow clouds. It has been found that the amplitude of initial humidity perturbations introduced by convection in the free troposphere is regulated by turbulent mixing and dissipation at small scales. The size and strength of humidity perturbations in the free troposphere that can destabilize the RCE state increase with greater dissipation at small scales.

How to cite: Silvestri, L., Saraceni, M., and Bongioannini Cerlini, P.: Numerical diffusion and turbulent mixing in convective self-aggregation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5959, https://doi.org/10.5194/egusphere-egu24-5959, 2024.

EGU24-8367 | ECS | Posters on site | AS1.5

Improving and Assessing Organized Convection Parameterization in the Unified Model 

Zhixiao Zhang, Hannah Christensen, Mark Muetzelfeldt, Tim Woollings, Bob Plant, Alison Stirling, Michael Whitall, Mitchell Moncrieff, and Chih-Chieh Chen

Improving weather and climate prediction cannot avoid accurately representing organized convection, as its convective and stratiform components distinctly reshape large-scale circulations via redistributing momentum and heat. For latent heating, the stratiform heating in organized convection shifts to higher altitudes compared to convective regions, presenting a significant challenge for representation in models across scales. The Multiscale Coherent Structural Parameterization (MCSP), introduced by Moncrieff et al. (2017), offers a promising solution by generating the top-heavy profile from convective heating in slantwise layer overturning scenarios. As part of the MCS: PRIME project, the PRIME-MCSP implementation by Zhang et al. (submitted, 2024) couples MCSP with the CoMorph-A convection scheme in the UK Met Office Unified Model with the following improvements: 1) CoMorph permits unstable air to rise from any height, diverging from the conventional CAPE trigger for deep convection, thereby enhancing continuity and facilitating storm tracking. 2) We activate MCSP selectively for deep mixed-phase clouds, recognizing the limited ability of shallow clouds to produce a stratiform component. 3) We configure the global model runs to include both a fixed convective-stratiform heating fraction and a fraction proportional to cloud top temperature.

MCS tracks in ensembles of weather runs show that PRIME-MCSP suppresses cloud deepening and reduces precipitation areas by dampening low-level updrafts. 20-year climate simulations show that PRIME-MCSP improves the precipitation seasonal cycle over the Indian Ocean, while increasing the warm-season wet bias over the Western Pacific. Additionally, PRIME-MCSP intensifies the Madden Julian Oscillation (MJO). The model run using a variable convective-stratiform fraction more accurately represents the MJO frequency and aligns better with reanalysis. Future plans focus on the stochastic representation of stratiform effects, steered by insights from data assimilation increments.

How to cite: Zhang, Z., Christensen, H., Muetzelfeldt, M., Woollings, T., Plant, B., Stirling, A., Whitall, M., Moncrieff, M., and Chen, C.-C.: Improving and Assessing Organized Convection Parameterization in the Unified Model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8367, https://doi.org/10.5194/egusphere-egu24-8367, 2024.

EGU24-8856 | ECS | Orals | AS1.5

Storm intensification driven by soil moisture gradients in global hotspot regions 

Emma Barton, Cornelia Klein, Christopher Taylor, John Marsham, Douglas Parker, Ben Maybee, Zhe Feng, and L. Ruby Leung

Organised thunderstorm clusters known as Mesoscale Convective Systems (MCSs) can bring high impact hazards such as flash floods, lighting and destructive winds. It is crucial for the forecasting and mitigation of these hazards to understand the processes that influence the characteristics of storms and thereby contribute to extreme events. Soil moisture is known to influence the initiation of MCSs in several regions of the world, but the influence of soil moisture on the later stages of MCS lifecycles is less well understood. Work in West Africa has revealed that dry soil moisture structures on scales > 200 km can increase the scale and longevity of propagating, mature afternoon MCSs, but this has not been investigated for other regions. In the current work we simultaneously analyse seven global MCS hotspot regions where storms may be sensitive to soil moisture, the US Great Plains, China, India, West Africa, Australia, South Africa and South America, to gain a more global perspective of the impact of soil moisture conditions on mature MCS characteristics. Using a combination of global datasets, storm tracks, satellite data, reanalysis data and CMIP6 simulations, we reveal that large-scale soil moisture gradients (100s of km) can intensify storms by driving favourable shear conditions through the strengthening of low-level atmospheric temperature gradients. By separating storms by soil moisture conditions, we show an increase in precipitation feature area and rainfall production on days with favourable gradients compared to days with unfavourable gradients. This is a newly identified mechanism through which soil moisture can influence storm hazards globally, which has implications for the forecasting and future projection of extreme events under climate change.

How to cite: Barton, E., Klein, C., Taylor, C., Marsham, J., Parker, D., Maybee, B., Feng, Z., and Leung, L. R.: Storm intensification driven by soil moisture gradients in global hotspot regions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8856, https://doi.org/10.5194/egusphere-egu24-8856, 2024.

EGU24-9561 | Posters on site | AS1.5

Representing land-ocean heterogeneity via convective adjustment timescale 

Andrea Polesello, Bidyut Bikash Goswami, and Caroline Muller

Representing land-ocean heterogeneity via convective
adjustment timescale
Bidyut Goswami1 , Andrea Polesello1 , Caroline Muller1 .
1Department of Earth Science, Institute of Science and Technology Austria, Klosterneuburg, Austria
January 2024


Abstract

The time needed by deep convection to bring the atmosphere back to equilibrium
is called convective adjustment timescale or simply adjustment timescale, typically
denoted by τ . In the Community Atmospheric Model version 6 (CAM6), convection
is parameterized through the Zhang-McFarlan scheme [1], where CAPE undergoes
an exponential consumption, of which τ is the time constant. τ is a tunable pa-
rameter in CAM6 and it has a default value of 1 hour, worldwide, on both ocean
and land. Albeit, there is no justified reason why one adjustment timescale value
should work over land and ocean both. Continental and oceanic convection is dif-
ferent in terms of the vigor of updraft and hence can have different durations.[2, 3]
So it is logical to investigate the prescription of two different convective adjustment
timescales for land (τL ) and ocean (τL ). To understand the impact of representing
land-ocean heterogeneity via τ , we investigated CAM climate simulations for two
different convective adjustment timescales for land and ocean in contrast to having
one value globally.
Following a comparative analysis of 5-year-long climate simulations, we find
τO =4hr and τL =1hr to yield the best results. In particular, we obtain a better
description of the Madden-Julian Oscillation (MJO). Although these τ values were
chosen empirically and require further tuning, the conclusion of our finding remains
the same, which is, to use two different τ values for land and ocean.
References
[1] G. Zhang and N. A. McFarlane, “Sensitivity of climate simulations to the parameterization of
cumulus convection in the canadian climate centre general circulation model,” Atmosphere-
Ocean, vol. 33, no. 3, pp. 407–446, 1995.
[2] C. Lucas, E. J. Zipser, and M. A. Lemone, “Vertical Velocity in Oceanic Convection off
Tropical Australia,” Journal of the Atmospheric Sciences, vol. 51, pp. 3183–3193, 11 1994.
[3] R. Roca, T. Fiolleau, and D. Bouniol, “A Simple Model of the Life Cycle of Mesoscale
Convective Systems Cloud Shield in the Tropics,” Journal of Climate, vol. 30, pp. 4283–
4298, 6 2017.

How to cite: Polesello, A., Goswami, B. B., and Muller, C.: Representing land-ocean heterogeneity via convective adjustment timescale, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9561, https://doi.org/10.5194/egusphere-egu24-9561, 2024.

EGU24-10474 | ECS | Posters on site | AS1.5

Predator-prey characteristics of the rapid shallow-to-deep transition of atmospheric convection 

Cristian-Valer Vraciu, Julien Savre, and Maxime Colin

Within a diurnal cycle, the transition from shallow to deep convection takes several hours, despite having large environmental instability at the onset of shallow convection. During this period, the cloud environment remains rather steady, while the convection exhibits a rapid development. Properly predicting the timing of this rapid shallow-to-deep transition within a diurnal cycle is still a major shortcoming of weather and climate models that employ the so-called mass-flux parameterization of atmospheric convection, as they typically predict the onset of deep convection too early, not allowing for a gradual convective deepening. In this work, it is argued that the problem of correctly representing the diurnal cycle of deep convection comes from the fundamental assumptions of the mass-flux formulation, in which it is considered that the clouds, represented by steady-state plumes, only interact with a spatially homogeneous environment. However, in the rapid shallow-to-deep transition, the convection still requires several hours to deepen, even if the environment remains steady, so some interactions must be missing. Here, a conceptual model for cloud development is introduced, in which a cloud is formed due to the sum of water transport from the boundary layer by multiple updrafts during its life-time, allowing for cloud-cloud interactions. This process captures local preconditioning, in which the clouds themself provide favorable conditions for the development of subsequent updrafts. It is also argued that the cold pools act as a reinforcement of this process, organizing the updrafts, and thus, allowing for a greater degree of local preconditioning. Based on this new conceptual model, it is argued that the shallow-to-deep transition can be seen as a predator-prey problem, in which the cloud population at the cloud base acts as prey, while the surface precipitation rate acts as predators. This simple predator-prey model is then tested against an idealized large-eddy simulation, showing that indeed, the rapid shallow-to-deep transition of atmospheric convection exhibits predator-prey characteristics. Moreover, it is shown how easily the simple predator-prey model can be implemented in current mass-flux schemes, leading to improved representation of deep convection within a diurnal cycle. Overall, this suggests that better representing the spatial organisation of clouds can lead to improvements in the timing of cloud and precipitation properties, thanks to a better convective memory.

How to cite: Vraciu, C.-V., Savre, J., and Colin, M.: Predator-prey characteristics of the rapid shallow-to-deep transition of atmospheric convection, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10474, https://doi.org/10.5194/egusphere-egu24-10474, 2024.

EGU24-10894 | Posters on site | AS1.5

Organization of convection over Amazonia and its impact on transport 

Thibaut Dauhut, Héléna Gonthier, Bastien Viala, and Guido Haytzmann

Deep convection over Amazonia can manifest in various forms, from scattered convective cells to mesoscale organizations like squall lines and cloud clusters. This diversity significantly influences vertical convective transport, impacting not only large-scale circulation but also the poorly understood cycle of gases and aerosols emitted by the forest. Monitoring convective systems over Amazonia during the CAFE-Brazil field campaign (Dec 2022-Jan 2023) involved the HALO aircraft and the ATTO-Campina ground site, employing meteorological, aerosol, and chemical measurements.

On January 18, a 500-km wide mesoscale system dissipated, giving rise to new convective cells initially disorganized and later organized into a large squall line. This event was measured by ATTO-Campina and HALO during the local afternoon. To understand the processes driving organizational changes and their impact on transport, 24-hour simulations with the Meso-NH model were conducted over an 800-km wide domain, ranging from horizontal resolutions of 1600 m down to 200 m, ultimately resulting in large-eddy simulations.

The simulations revealed a strong resolution sensitivity in mesoscale convective organization, with a distinct emergence of squall lines at the finest resolutions only. Surprisingly, at fine resolution, organized convection exhibited larger transport due to increased updraft size, rather than intensity. Cloud cluster organization exhibited a delayed onset compared to convective cell organization, aligning with expectations. Ongoing investigations are currently focusing on gravity waves and cold pools to better understand their impact on convective organization.

How to cite: Dauhut, T., Gonthier, H., Viala, B., and Haytzmann, G.: Organization of convection over Amazonia and its impact on transport, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10894, https://doi.org/10.5194/egusphere-egu24-10894, 2024.

Satellite infrared (IR) cloud imagery has proven valuable in the identification of Pyrocumulonimbus (pyroCb) clouds. The substantial brightness temperature difference observed between warm shortwave IR wavelengths (~4 μm) and window IR wavelengths (~11 μm) has served as a reliable marker for detecting daytime pyroCb. However, this indicator becomes ineffective during nocturnal hours when the enhanced brightness temperature at 4 μm is solely a daytime phenomenon, arising from PyroCb microphysics that increase solar reflectivity of clouds. We have developed a machine learning model designed to detect pyroCb events during nighttime using IR channels from the Advanced Baseline Imager (ABI) aboard GOES-16. The model leverages the distinctive characteristics of daytime IR channels as its training data. We applied the trained model to five intense pyroCb events in western North America during August 2017. Furthermore, we have employed an established cloud-tracking tool known as Tracking and Object-Based Analysis of Clouds (tobac) to analyze the evolution of the clouds plumes and infer their lifetimes. Our research aims to extend this case study on a global scale, with the objective of creating a comprehensive database for the lifetimes of pyroCb events. Such a database will enhance our understanding of pyroCb dynamics, which is helpful for investigating the radiative implications and the potential impact on stratospheric chemistry.

How to cite: Liu, F. and da Silva, A.: Detecting diurnal cycle and lifetime of pyrocumulonimbus using GOES-16 infrared data with a machine learning model , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12581, https://doi.org/10.5194/egusphere-egu24-12581, 2024.

EGU24-13870 | ECS | Posters on site | AS1.5

Investigating Deep Convective Cores Combining CloudSat Observations and Model Simulations  

Zhuocan Xu, Pavlos Kollias, Alessandro Battaglia, Bernat Puigdomènech Treserras, and Peter Marinescu

The launch of the joint ESA JAXA Earth Cloud Aerosol and Radiation Explorer (EarthCARE) mission (May, 2024) marks the beginning of a new era of spaceborne radar measurements that target atmospheric convection. In addition to the EarthCARE mission that features the first Cloud Profiling Radar (CPR) with Doppler capability, NASA’s Investigation of Convective Updrafts (INCUS) and Atmosphere Observing System (AOS) missions aim to provide unique observations of convective dynamics. Prior to this upcoming decade of the study of atmospheric convection from space, the CloudSat CPR collected remarkable data of convective cores over a period of 15 years. Despite its high frequency that results in significant attenuation and multiple scattering effects, the 94-GHz CloudSat CPR offers a relatively small footprint (compared to the TRMM/GPM radar footprint of 5 km) and collocated radar-radiometer (passive) brightness temperatures (Tb). Here, we propose a refined deep convective core (DCC) identification scheme by first selecting the CPR profiles with continuous echoes between below 2 and above 10 km. The 10-dBZ echo top height is also required to exceed 10 km and located within 2 km from cloud top. Additionally, profiles with stratiform precipitation flags in the CloudSat products are not included in the analysis.

We investigated the CloudSat observations from 2006 to 2019 globally and also with a focus over 4 convective basins where model simulations are performed by the NASA’s INCUS science team. The four deep convection basins are Amazon, Congo, Philippines, and Western Pacific, which represent a decent spectrum of atmospheric environments. It is found that the DCCs over the Congo basin are featured with larger size and likely more intensified updrafts, while the Western Pacific is characterized with finer-scale cores. The analysis shows that the DCCs with size below 5 km predominate, implying the narrow cores can be under detected by the large-footprint radars such as GPM. The distinct depressions of 94-GHz Tb due to the presence of high-density ice particles lend complementary information on DCC classifications. In addition, multiple scattering can be a confounding factor in interpretating the CPR measurements within deep convective clouds. Our preliminary calculations suggest the impact of multiple scattering becomes significant at ~2.5 km from radar cloud top on average and is subject to the DCC updraft intensity. Moreover, profiles of 94-GHz radar reflectivity and Tb are forward calculated from the high-resolution model simulation outputs to understand the constraints that such observations can afford on key measures such as convective mass fluxes.

How to cite: Xu, Z., Kollias, P., Battaglia, A., Treserras, B. P., and Marinescu, P.: Investigating Deep Convective Cores Combining CloudSat Observations and Model Simulations , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13870, https://doi.org/10.5194/egusphere-egu24-13870, 2024.

EGU24-14373 | Orals | AS1.5

Why can nighttime convection occur despite strong convective inhibition? 

Yi-Hung Kuo, Zhihong Tan, Ming Zhao, and J. David Neelin

Over continental plains, precipitation tends to peak in the late afternoon or during nighttime. The accurate simulation of the land precipitation diurnal cycle in GCMs has been a long-standing challenge. Nighttime surface cooling tends to yield a stable layer with large convective inhibition (CIN). However, CIN arises from traditional parcel considerations—measuring the inhibition for an infinitesimal parcel. Here, we argue that the CIN layer is less effective in inhibiting convection than previously thought for convective entities of typical horizontal cloud size.

A time-dependent process model for anelastic convective entities (ACE) is formulated to consistently include dynamic entrainment/detrainment as well as a representation of nonhydrostatic perturbation pressure. Spatially nonlocal effects mediated by the pressure field imply that horizontal feature size becomes a factor in the vertical conditional instability problem. ACE simulations using nighttime GoAmazon soundings with strong surface inversion demonstrate that the vertically nonlocal pressure response and its interaction with the surface boundary condition make the CIN layer ineffective for convective features of substantial horizontal size. Within the convective column, buoyancy of different signs offset each other via the nonlocal interaction over vertical scales comparable to the typical horizontal scale. Furthermore, the interaction with the surface tends to downweight the effectiveness of negative buoyancy contributions at low levels. This implies that a much smaller vertical velocity perturbation (or more generally, nonlocal buoyancy forcing from neighboring disturbances) can tunnel through the CIN layer. The same effect yields smaller magnitude for the mass flux above the CIN layer compared with steady plume models. 

A related implication of including spatially nonlocal interactions is that the vertical acceleration due to deep-convective buoyancy tends to extend above the level of neutral buoyancy (LNB). This results in cloud top much higher than the LNB, exhibiting the convective cold-top feature previously noted in observations. Results here point to revision for convective parameterizations. 

How to cite: Kuo, Y.-H., Tan, Z., Zhao, M., and Neelin, J. D.: Why can nighttime convection occur despite strong convective inhibition?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14373, https://doi.org/10.5194/egusphere-egu24-14373, 2024.

EGU24-14599 | Posters on site | AS1.5

Using Deep Learning for Convection Parameterization 

Guang Zhang, Yilun Han, and Yong Wang

Data-driven approaches using machine learning to parameterizing model physical processes in Earth System Models have been actively explored in recent years. Deep-learning-based convection parameterization is one such example. While significant progress has been made in emulating convection using neural networks (NN), serious roadblocks remain, including generalization of the NN-based scheme trained on model data from current climate to future climate and integration instability when it is implemented into the model for long-term integrations. This study uses a deep residual convolutional network to emulate convection simulated by a superparameterized global climate model (GCM). The NN uses the current environmental state variables and advection tendencies, as well as the history of convection to predict the GCM grid-scale temperature and moisture tendencies, cloud liquid and ice water contents from moist physics processes. Independent offline tests show that the NN-based scheme has extremely high prediction accuracy for all output variables considered. In addition, the scheme trained on data in the current climate generalizes well to a warmer climate with +4K sea surface temperature in an offline test, with high prediction accuracy as well. Further tests on different aspects of the NN architecture are performed to understand what factors are responsible for its generalization ability to a warmer climate. We are also able to perform multi-year integrations, without encountering any integration instability, when the scheme is implemented into the NCAR CAM5. The details will be presented at the meeting.

How to cite: Zhang, G., Han, Y., and Wang, Y.: Using Deep Learning for Convection Parameterization, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14599, https://doi.org/10.5194/egusphere-egu24-14599, 2024.

EGU24-14717 | Orals | AS1.5

Intensification of daily tropical precipitation extremes from more organized convection 

Jiawei Bao, Bjorn Stevens, Lukas Kluft, and Caroline Muller

Tropical precipitation extremes and their changes with surface warming are investigated using global storm resolving simulations and high-resolution observations. The simulations demonstrate that the spatial organization of convection at mesoscale, a process that cannot be physically represented by conventional global climate models, is important for the variations of tropical daily precipitation extremes (total accumulations over a day). In both the simulations and observations, daily precipitation extremes increase in a more organized state, in association with larger, but less frequent, storms. Repeating the simulations for a warmer climate results in a robust increase in monthly-mean daily precipitation extremes. Higher precipitation percentiles have a greater sensitivity to convective organization, which is predicted to increase with warming. Without changes in organization, the strongest daily precipitation extremes over the tropical oceans increase at a rate close to Clausius-Clapeyron (CC) scaling. Thus, in a future warmer state with increased organization, the strongest daily precipitation extremes over oceans increase at a faster rate than CC scaling. Moreover, as the precipitation distribution becomes more uneven with increased organization, the tropics may not only face heavier precipitation extremes, but experience more extensive drying.

How to cite: Bao, J., Stevens, B., Kluft, L., and Muller, C.: Intensification of daily tropical precipitation extremes from more organized convection, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14717, https://doi.org/10.5194/egusphere-egu24-14717, 2024.

EGU24-16757 | Orals | AS1.5

Increase of a precipitation “brake” to stronger storms in kilometer-scale global warming simulations 

Maximilien Bolot, Olivier Pauluis, Lucas Harris, Kai Cheng, Timothy Merlis, Spencer Clark, Alex Kaltenbaugh, Linjiong Zhou, and Stephan Fueglistaler

As the atmosphere gets warmer, it is expected to hold more water vapor, thereby fueling stronger storms. At the same time, the condensation of this vapor increases the combined load of liquid water and ice aloft, forcing convection to do more work to lift water to the level where it precipitates. This takes away from the generation of kinetic energy, thereby creating a “brake” on atmospheric motions. The evolution of this precipitation “brake” with warming determines the magnitude of future storm intensification, with important societal implications. The new generation of kilometer-scale climate models is capable of projecting this evolution. In this presentation, we show how the NOAA/GFDL X-SHiELD experimental global storm-resolving model can be used to estimate the total mechanical work done by convection and the work done to lift water which is then subsequently dissipated by friction during precipitation. The statistics are computed in year-long simulations of the present climate and of a 4K warmer climate.

We find that the ratio of kinetic energy generation vs work spent to lift water is respectively 30% vs 70% of the total mechanical work done by convection on global average, with a relative stability across regions and in the present vs future climate.

Moving beyond regional averages, when we organize the space by decreasing values of dissipation, we find that the ratio of work spent to lift water to total mechanical work strongly increases in the most convective percentiles, that is, most of the work done by convection is used to lift water in the extremes, showing that water loading strongly opposes kinetic energy generation. We also find that the total work done by convection, the work spent to lift water and the precipitation-induced dissipation all increase similarly with warming in the most convective percentiles. This suggests that, as the Earth warms, the updrafts tend to “kill” themselves in situ from increased water loading instead of generating a response at larger scale.

How to cite: Bolot, M., Pauluis, O., Harris, L., Cheng, K., Merlis, T., Clark, S., Kaltenbaugh, A., Zhou, L., and Fueglistaler, S.: Increase of a precipitation “brake” to stronger storms in kilometer-scale global warming simulations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16757, https://doi.org/10.5194/egusphere-egu24-16757, 2024.

EGU24-16951 | ECS | Posters on site | AS1.5

Mapping km-scale global extreme rainfall onto mesoscale convective systems lifecycle, frequency and dynamics  

Benjamin Fildier, Maxime Carenso, Rémy Roca, and Thomas Fiolleau

Mesoscale convective systems are the building block of tropical precipitation, as more than 40% of global precipitation and more than 80% of extreme rainrates are produced by these organized systems. However, when investigating the sensitivity of global rain extremes, the behavior and morphology of organized storm systems are typically ignored and corresponding dynamics are instead interpreted using the textbook framework of a convecting parcel. Indeed, despite rich observational and case studies describing the internal dynamics and structures of MCSs, no conceptual framework exist to this day to bridge the gap between global hydrologic sensitivity and MCS behavior.

This work introduces new approaches to link extreme precipitation rates in the tropics to the occurrence, internal dynamics and lifecycle of individual MCSs. Individual storms are idenditifed based on by the Lagrangian tracking algorithm TOOCAN which tracks storm anvils over their lifecycle, and which has been applied to satellite observations and to global storm resolving models in the DYAMOND experiment. We first use this rich dataset to develop a numerical interface that maps the occurrence of extreme precipitation rate onto the MCS cloud shield. We then introduce a novel conceptual framework to decompose the sensitivity of precipitation extremes to the change in storm occurrence and change in internal dynamics within this cloud shield. 

Results are threefold. We demonstrate a robust phasing in the timing of global extreme rainrates within the storm lifecycle, robustly occurring at 25-30% of the storm's lifetime for the models and regions analyzed. The analytical decomposition confirms that in a given climate state, variability in the heaviest rainrates across regions mostly occur through changes in MCS frequency, rather than changes in their efficiency at producing rain. We finally argue that the sensitivity of extremes to climate state may occur through both a change in occurrence and a change in internal MCS dynamics.

How to cite: Fildier, B., Carenso, M., Roca, R., and Fiolleau, T.: Mapping km-scale global extreme rainfall onto mesoscale convective systems lifecycle, frequency and dynamics , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16951, https://doi.org/10.5194/egusphere-egu24-16951, 2024.

EGU24-17683 | ECS | Orals | AS1.5

Characteristics of precipitating convection and moisture-convection relationships in global km-scale simulations 

Tobias Becker, Daisuke Takasuka, and Jiawei Bao

In this study, we compare convection characteristics in three models that are at the forefront of global km-scale modelling, the ICON model developed by the Max Planck Institute for Meteorology (MPI-M) and German Weather Service (DWD), the IFS developed by the European Centre for Medium-Range Weather Forecasts (ECMWF), and the NICAM model developed by the University of Tokyo, the Japan Agency for Marine-Earth Science and Technology (JAMSTEC) and the National Institute for Environmental Studies (NIES). For IFS and ICON, we analyse 1-year coupled simulations at 4.4 and 5 km resolution, respectively, which stem from Cycle 3 of the H2020 Next Generation Earth Modelling Systems (nextGEMS) project. For NICAM, we analyse a 1-year AMIP-type simulation at 3.5 km resolution. Convection schemes have been switched off in ICON and NICAM, while in the IFS the deep convection scheme’s cloud base mass flux is strongly reduced. 

Modelling convection at km-scale resolutions is both exciting and challenging because some important processes are already resolved at these scales (e.g., deep convection) but other important processes remain under-resolved (e.g., mixing of grid-scale updrafts with their environment). Thus, we analyse in this study what common issues exist in ICON, IFS and NICAM with respect to the convection characteristics in the tropics, in what respects all models do well and where there are substantial inter-model differences.

Specifically, we analyse local convection characteristics and show that compared to satellite observations, the models tend to overestimate precipitation intensity (NICAM and ICON), while they underestimate precipitation cell size and precipitation duration. We study mesoscale organisation by using different organisation metrics and show that the models tend to underestimate organisation, even though they all consistently show that when organisation is enhanced, heavy precipitation is enhanced as well. We also investigate moisture-convection relationships and show that the models generally do not moisten enough during a convective event compared to ERA5 reanalysis data. Consistently, the sensitivity of lower-tropospheric moisture variations to the life cycle of deep convection over ocean looks too weak in ICON and IFS.

Finally, we look at land-ocean differences of the convection characteristics and show that while all models capture the diurnal cycle of precipitation over ocean well, there are some substantial differences over land, even though biases are not consistent between the models. Over coastal regions of the Maritime Continent, ICON has too strong mean precipitation and a too strong diurnal cycle, whereas IFS overall underestimates both, connected to a too weak propagation of convection onto the ocean during nighttime, potentially connected to too weak cold pools. Meanwhile, NICAM has more realistic convection characteristics in these coastal regions.

How to cite: Becker, T., Takasuka, D., and Bao, J.: Characteristics of precipitating convection and moisture-convection relationships in global km-scale simulations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17683, https://doi.org/10.5194/egusphere-egu24-17683, 2024.

EGU24-20249 | ECS | Orals | AS1.5

Convective precipitation extremes may not increase beyond the Clausius-Clapeyron expectation 

Nicolas Da Silva and Jan Haerter

Flash floods arising from short-duration precipitation extremes are costly for the population, and their frequency and intensity could increase with global warming (Fowler et al., 2021). Understanding the mechanisms leading to extreme precipitation is thus essential. A common hypothesis for precipitation extremes is that they scale with temperature according to the thermodynamic Clausius-Clapeyron (CC) law. However, increases in short-duration precipitation extremes beyond the CC expectation (or super-CC) were reported in multiple regions. The super-CC scaling is currently understood as the combination of two effects: (1) an invigoration of convective precipitation through convective cloud feedbacks; (2) a statistical effect resulting from a shift in rain type, from light stratiform to heavier convective-type precipitation, with increasing temperatures.

This work revisits these hypotheses by identifying convective precipitation at an unprecedented high resolution (5 km spatially and 10 min temporally). For this, we employ the EUropean Cooperation for LIghtning Detection (EUCLID) lightning dataset to define convective precipitation and combine it with weather station data from the German weather service (Deutscher Wetterdienst, DWD). We show that while (total) extreme precipitation increases with a super-CC rate, the scaling of both convective and stratiform-type precipitation extremes is in accordance with the CC law. We thus conclude that the super-CC rate is explained by the statistical shift in rain type alone and refute any mechanistic origin. 

Mesoscale Convective Systems (MCSs), which dominate extreme precipitation events in Europe (Da Silva & Haerter, 2023), are known to contain both a convective and stratiform region (Houze, 1997). By tracking MCSs over Germany, we show that MCS extreme precipitation also features a super-CC rate, which we relate to a dramatic increase in their convective fraction for dew point temperatures exceeding 14 degrees Celsius. 

References:

Da Silva, N. A., & Haerter, J. O. (2023). The precipitation characteristics of mesoscale convective systems over Europe. Journal of Geophysical Research: Atmospheres, 128, e2023JD039045. https://doi.org/10.1029/2023JD039045

Fowler, H.J., Lenderink, G., Prein, A.F. et al. Anthropogenic intensification of short-duration rainfall extremes. Nat Rev Earth Environ 2, 107–122 (2021). https://doi.org/10.1038/s43017-020-00128-6

Houze, R. A. Stratiform precipitation in regions of convection: A meteorological paradox? Bulletin of the American Meteorological Society 78, 2179 – 2196 (1997). https://doi.org/10.1175/1520-0477(1997)078<2179:SPIROC>2.0.CO;2

How to cite: Da Silva, N. and Haerter, J.: Convective precipitation extremes may not increase beyond the Clausius-Clapeyron expectation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20249, https://doi.org/10.5194/egusphere-egu24-20249, 2024.

EGU24-20512 | ECS | Posters on site | AS1.5

Formation of thermal vortex rings 

Paweł Jędrejko and Jun-Ichi Yano

Geophysical convection is usually characterized by Reynolds number in the range typical for turbulent flow. Despite that, it displays features of organization.  
Thermal vortex rings are considered candidates for the basic elements of that order (Yano 2023, ch. 16).
In this work, the process of their formation from a spherical buoyancy anomaly is studied numerically. The buoyancy distribution is assumed to be uniform with a discontinuity at the interface.
The rising anomaly experiences a collapse at the bottom, and initially spherical shape is transformed into a torus. Neglecting diffusive processes, the system is uniquely defined by the vortex sheet coincident with the interface. For that reason, its evolution is considered on the grounds of vorticity dynamics with Lagrangian approach.
The vortex sheet is intensified by buoyancy and further subjected to Kelvin-Helmholtz instability. This starts in high wavenumbers increasing the effective thickness by purely advective mechanism. A similar instability is then launched in lower wavenumbers, and the phenomenon repeats hierarchically. As a result, the energy is transferred from small to large scales. The same mechanism also drives the interfacial mixing by applying stretching and folding repetitively. This makes it a good starting point for further studies on the entrainment rate and order emerging out of chaos. 

How to cite: Jędrejko, P. and Yano, J.-I.: Formation of thermal vortex rings, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20512, https://doi.org/10.5194/egusphere-egu24-20512, 2024.

EGU24-328 | ECS | Orals | AS1.6

Object-Based Analyses of Mesoscale Convective Systems and Embedded Storms over the Indian Monsoon Zone Using Datasets from Satellite, Radar and Model Simulations                

Manisha Tupsoundare, Sachin Deshpande, Zhe Feng, Subrata kumar Das, Medha Deshpande, and Harshad Hanmante

Mesoscale convective systems (MCSs), the largest type of deep convective storms are formed when convection aggregates and grows upscale, forming a distinct mesoscale circulation through the interaction of multiple storms. Thus, storms play an important role in MCS organization. Due to their large size, longer duration, and larger precipitation, MCSs cause high-impact extreme weather events like lightning, damaging hail, gusty winds, and flooding. During the Indian summer monsoon (June-September), synoptic-scale weather systems move across the monsoon zone (MZ), causing MCSs to form frequently. MCSs often produce widespread and heavy rain throughout the MZ. Hence, studies on structure and evolution of MCSs highlighting the organization of convection are needed for an improved understanding of MCS.

In this study, we used an object-based cloud-tracking method (Feng et al., 2018) to identify and track MCSs and embedded storms in remote sensing observations and numerical simulations. The work is divided into three parts. In the first part, we tracked MCSs over the monsoon zone using geostationary satellite infrared brightness temperature (IRTb) and GPM IMERG precipitation from June-September, 2014 to 2019 and examined various aspects of observed MCSs (n=2092) such as spatial coverage, diurnal cycle, rainfall amount, and land-ocean contrast. The majority of MCSs are positioned in the monsoon trough's southeast-northwest stretch and account for more than 60% of total precipitation. For MCSs with short and long lifespans, there was a clear land-ocean divide and varied lifecycle trends. Oceanic MCSs last longer, are deeper, and provide more rainfall over a larger area than land-based MCSs.

In the second part of the study, we explored embedded storm structures for those MCSs that exist within the radar domain (n=65) by applying a storm classification algorithm to the S-band Doppler radar observations during June-September 2015. We observed that an MCS contains many precipitation features, especially during early stages of development when multiple convective clusters begin to amalgamate. Furthermore, we investigated the co-evolution of numerous storm parameters (e.g., areas of convective/stratiform precipitation, convective core length, and top heights) as a function of MCS lifetime. Distinct vertical structures are observed for the convective, stratiform, and anvil components of MCSs.

In the third part of this work, we examine the ability of a convection-permitting Weather Research Forecast (WRF) model in simulating MCSs and their characteristics (initiation, size, intensity, lifetime, propagation) during June-September 2015. A similar cloud-tracking algorithm is applied to WRF-simulated data (reflectivity, IRTb, and precipitation) to identify and track MCS in the simulation. Although the model underestimated the number of observed MCSs, the composite evolution and frequency distribution of convective area, precipitation amount, MCS propagation speed produces reasonable agreement with observations but underestimate stratiform areas. Consistent with observations, the simulated MCS properties showed a gradual increase from convective initiation to around the first half of the MCS lifetime. We observed that an MCS contains multiple precipitation features, particularly during the initial development stage when multiple convective clusters begin to aggregate. More details on observed MCSs and embedded storm structures, as well as their representation in simulation, will be presented.

How to cite: Tupsoundare, M., Deshpande, S., Feng, Z., Das, S. K., Deshpande, M., and Hanmante, H.: Object-Based Analyses of Mesoscale Convective Systems and Embedded Storms over the Indian Monsoon Zone Using Datasets from Satellite, Radar and Model Simulations               , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-328, https://doi.org/10.5194/egusphere-egu24-328, 2024.

EGU24-1300 | Posters on site | AS1.6

Ensemble Sensitivity-Based Subsetting for Convection: Progress Toward Operational Use 

Brian Ancell and Austin Coleman

Ensemble sensitivity is a statistical tool applied within an ensemble that reveals the atmospheric flow features (e.g. position of a jet streak, or magnitude of a low-level moisture plume) at early forecast times that are related to a chosen forecast response later in the forecast window.  The response function is chosen to diagnose high-impact forecast features such as maximum updraft helicty over a specified area, or number of grid points of simulated reflectivity exceeding 40 dBZ in a chosen region.  Since ensemble sensitivity highlights the features early in a forecast important to the prediction of high-impact features later in the forecast, a subset of members with the smallest errors in sensitive regions can be chosen that might improve probabilistic forecasts of the response relative to the full ensemble. Similar to ensemble data assimilation, this process incorporates observational information to beneficially update forecast distributions.  The subsetting procedure can be done quickly once an ensemble has been run, and sensitivity-based subsets can typically be generated well before the next extended forecast can be run within a cycling storm-scale data assimilation and forecasting system. In turn, the subsetting procedure, if shown to improve forecasts, could be a unique and useful operational forecasting tool.

 

Ensemble sensitivity-based subsetting has been tested within the Texas Tech University operational ensemble system in both an idealized framework and in more operational settings in real time during several years of the National Oceanic and Atmospheric Administration (NOAA) Hazardous Weather Testbed (HWT).  Response functions that diagnose severe convective hazards, such as updraft helicity, hail size, and simulated reflectivity have been tested to gain an understanding of both the general capability of the technique and the perception of forecasters regarding its value in a real-time forecasting environment.  Here we discuss this effort and its associated results, the technique’s current status, and future plans toward ultimate operational implementation.

How to cite: Ancell, B. and Coleman, A.: Ensemble Sensitivity-Based Subsetting for Convection: Progress Toward Operational Use, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1300, https://doi.org/10.5194/egusphere-egu24-1300, 2024.

EGU24-1675 | Orals | AS1.6 | Highlight

ML for weather prediction at Météo-France : current status and future plans 

Laure Raynaud, Clément Brochet, and Gabriel Moldovan

Applications of Machine Learning (ML) in the different stages of weather forecasting have considerably developed recently. Such progress is likely to change the landscape and offer new perspectives to speed up and improve forecast performances, at different spatio-temporal scales. In this context, Météo-France engaged more actively in this new area of research, with the objective to further explore the capabilities and opportunities of ML for operational forecasting. Major ongoing projects include ML to significantly enhance the size of convective-scale ensemble forecasts, high-resolution statistical downscaling and the development of data-driven kilometre-scale forecasting systems. Early results will be presented and our short-term roadmap will be discussed.

How to cite: Raynaud, L., Brochet, C., and Moldovan, G.: ML for weather prediction at Météo-France : current status and future plans, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1675, https://doi.org/10.5194/egusphere-egu24-1675, 2024.

EGU24-1936 | ECS | Orals | AS1.6

Mesoscale Convective Systems across Australia 

Ewan Short and Todd Lane

A major aspiration of operational and research meteorology is to relate the average behaviour of convective-scale flows to the more predictable, larger-scale flows in which they occur. This goal is difficult, partly because convective flows often self-organize at mesoscales, with the dynamics of such mesoscale convective systems (MCSs) distinct from those at convective and synoptic scales. In this study we use a tracking algorithm to detect MCSs in Australian operational radar data, revealing regional, seasonal and sub-seasonal, i.e. synoptic, differences in organizational characteristics. Restricting to MCS observations with nominally two-dimensional mean system-relative flows, spatio-temporal organizational differences are generally well explained by theoretical ideas regarding the breakdown of two-dimensional overturning flows. Theoretically, breakdown is characterised by a single non-dimensional convective Richardson number R, which provides the ratio of thermodynamic potential energy to inflow kinetic energy. Specifically, 76% of MCS relative trailing-stratiform, up-shear tilted observations, nominally associated with primarily non-overturning system-relative flows, occur when R>5, whereas 72% of relative leading-stratiform, down-shear tilted observations, nominally indicating primarily overturning system-relative flows, occur when R<5. Spatiotemporal variations in observed organizational characteristics are broadly consistent with spatiotemporal variations in median R. These results likely have implications for convective parametrisation, and operational convective permitting model testing and development.

How to cite: Short, E. and Lane, T.: Mesoscale Convective Systems across Australia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1936, https://doi.org/10.5194/egusphere-egu24-1936, 2024.

The Advanced Geostationary Radiation Imager (AGRI) onboard the FY-4A geostationary satellite provides high spatiotemporal resolution visible reflectance data since March 12th, 2018. Data assimilation experiments under the framework of observing system simulation experiment have shown great potential of these data to improve the forecasting skills of numerical weather prediction (NWP) models. To effectively assimilate the AGRI data, it is important to address the quality the observations. In this study, the FY-4A/AGRI channel 2 (0.55 μm - 0.75 μm) reflectance was evaluated by the equivalents derived from the short-term model forecasts of the China Meteorological Administration Mesoscale Model (CMA-MESO) using the Radiative Transfer for TOVS (RTTOV, v 12.3). It is shown that the observation minus background (O – B) statistics could be used to reveal the abrupt changes related to the measurement calibration processes. In addition, O - B statistics are negatively biased. Potential causes include measurement errors, the unresolved processes, forward-operator errors, etc. The relative mean biases of O-B computed for cloud-free and cloudy pixels were used to correct the systematic differences for cloudy and clear pixels separately. Results indicate that the bias correction method could effectively reduce the biases and standard deviations of O-B. In addition, an ensemble forecast has advantages over a deterministic forecast in correcting the biases in FY-4A/AGRI visible reflectance data. The finding suggests an effective method to monitor the performance of FY-4A/AGRI visible measurements and to correct the biases in the observations. 

How to cite: Zhou, Y., Liu, Y., Zeng, Y., and Han, W.: Evaluation of FY-4A/AGRI visible reflectance using the equivalents derived from the forecasts of CMA-MESO using RTTOV, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2793, https://doi.org/10.5194/egusphere-egu24-2793, 2024.

The historic 22-26 May 2015 flood event in Texas and Oklahoma was caused by anomalous clustered mesoscale convective systems (MCSs) that produced record-breaking rainfall and $3 billion of damage in the region. A month-long regional convection-permitting simulation is conducted to reconstruct multiple clustered MCSs that lead to this flood event. We further use the pseudo global warming approach to examine how a similar event may unfold in a warmer climate and the driving physical factors for the changes. Tracking of MCSs in observations and simulations shows that the historical simulation reproduces the salient characteristics of the observed MCSs. In a warmer climate under a high-emission (SSP5-8.5) scenario, the Southern Great Plains is projected to experience a near surface warming of 4-6 K, accompanied by enhanced moisture transport by the strengthened Great Plains low-level jet. A warmer and moister lower troposphere leads to 36-59% larger convective available potential energy, supporting wider and more intense convective updrafts and rainfall production. Consistently, MCSs have wider convective areas and stronger rainfall intensities, producing 50% larger rain volumes during the mature stage. Extreme (99.5%) MCS rainfall frequency and amount will increase by threefold (Fig. 1). However, MCS stratiform rain area decreases as a result of elevated stratiform cloud bases that lead to stronger sublimation and evaporation of precipitation in response to warming, resulting in reduced weak-to-moderate surface precipitation. Results suggest that global warming greatly increases precipitation intensity of clustered MCS events under strong synoptic influence, with much higher potential to produce serious floods without additional climate adaptation.

Figure 1. (a) Frequency distribution of MCS grid-point hourly rain rates, and (b) normalized cumulative distribution of rainfall amount by hourly rain rates. The region of the data included is show in the inset.

How to cite: Feng, Z., Chen, X., and Leung, R.: How Might the May 2015 Flood in the U.S. Southern Great Plains Induced by Clustered MCSs Unfold in the Future?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3210, https://doi.org/10.5194/egusphere-egu24-3210, 2024.

This study examines the urban impacts associated with a developed city belt on generating an afternoon heavy rainfall event over a coastal developing city that is 70–100 km downwind from the city belt over the Yangtze River Delta region. Observational analyses show pronounced urban heat island (UHI) effects along the upstream city belt prior to convection initiation (CI). A series of cloud-permitting model simulations with the finest grid spacing of 1 km are performed to examine the impacts of urbanization on CI and the subsequent heavy rainfall event. Results reveal the generation of warm anomalies and low-level convergence in the planetary boundary layer along the upstream city belt, thereby inducing upward motion for CI. The southwesterly flows of the monsoonal warm-moist air, enhanced by the UHI effects along the city belt, allow the development of convective cells along the belt. Some of the cells merge during their downstream propagation, promoting to the ultimate generation of the distinct heavy rainfall centers in favor of local convective clusters over the coastal city where atmospheric columns are more moist and potentially unstable under the influences of sea breezes. Sensitivity simulations show small contribution of the downstream city but more influences from the upstream city belt on the heavy rainfall event. The above findings help elucidate how the UHI effects could assist the CI in a weak-gradient environment, and explain why urbanization can contribute to increased downwind mean and extreme precipitation under the influences of favorable regional forcing conditions. These findings have been published in Monthly Weather Review.

How to cite: Jiang, X., Zhang, D.-L., and Luo, Y.: Influences of urbanization on an afternoon heavy rainfall event over the Yangtze River Delta region in East China, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3714, https://doi.org/10.5194/egusphere-egu24-3714, 2024.

The impact of urbanization and the sensitivity of urban canopy parameters (UCPs) on a typical summer rainfall event in Hangzhou, China, is investigated using three groups of ensemble experiments. In this case, urbanization leads to higher temperatures, lower mixing ratios, lower wind speeds before precipitation, and more precipitation in and around the urban area. Both the thermal and dynamical effects of urbanization contribute to an increase in temperature and precipitation, with thermal effects contributing 71.2% and 63.8% to the temperature and precipitation increase, respectively, while the thermal and dynamical impacts cause the opposite changes to the mixing ratio and wind speed. Compared to the other three meteorological elements, the model has the largest uncertainty in the simulation of precipitation, which includes the sensitivity of the different parameterization schemes to the simulation of precipitation in urban areas, and the uncertainty brought by the urban effect on precipitation is not confined within the city but extends to the surrounding areas as well. Temperature and mixing ratio are more sensitive to thermal-related UCPs, while the wind speed is mainly affected by the structural parameters. These variations, however, are sometimes contradictory to precipitation changes, which further adds to the complexity of precipitation simulation.

How to cite: Wu, M., Dong, M., Chen, F., and Yang, X.: Impacts of Urbanization and Its Parameters on Thermal and Dynamic Fields in Hangzhou: A Sensitivity Study Using the Weather Research and Forecasting Urban Model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3735, https://doi.org/10.5194/egusphere-egu24-3735, 2024.

EGU24-4082 | ECS | Orals | AS1.6

Assessing the influence of observations on the analysis in ensemble-based data assimilation systems 

Guannan Hu, Sarah Dance, Alison Fowler, David Simonin, and Joanne Waller

Convection-permitting numerical weather prediction (NWP) is crucial for forecasting high-impact weather events such as heavy precipitation, storms, floods, wind gusts and fog. The assimilation of observations plays a significant role in improving the forecasting skill of these weather events. To make better use of existing observations and guide the design of future observation networks, accurately assessing the influence of assimilated observations is essential. The degrees of freedom for signal (DFS) has long been used to assess the influence of observations on the analysis. While various methods exist for calculating the DFS in variational data assimilation (DA) systems, calculating the DFS in ensemble-based DA systems (e.g., the ensemble transform Kalman filter) is a largely unexplored area. Since ensemble-based DA systems are becoming increasingly dominant for convection-permitting NWP, practical implementation of the DFS in such DA systems is needed. Unlike in variational DA systems, the background error covariance matrix is not static in ensemble-based DA methods. Consequently, the DFS calculated at each assimilation step measures the observation influence for a certain background error covariance matrix. This means that the DFS estimates are flow dependent. In addition, domain localisation of observations is often used in ensemble-based DA systems (e.g., local ensemble transform Kalman filter). This implies that the DFS should be calculated locally. In this work, we propose novel approaches for calculating the DFS in ensemble-based DA systems and investigate existing approaches applicable to such systems. We establish their consistency under idealised conditions and discuss their differences in practical applications. To validate our theoretical findings, we conduct simple numerical experiments using JEDI (Joint Effort for Data assimilation Integration) developed by JCSDA (Joint Center for Satellite Data Assimilation).  Our results provide useful information for assessing the influence of observations in ensemble-based DA systems. This work is financially supported by the Met Office and is fully in line with the Met Office’s strategy and its ongoing development of the next generation data assimilation and observation processing system.

How to cite: Hu, G., Dance, S., Fowler, A., Simonin, D., and Waller, J.: Assessing the influence of observations on the analysis in ensemble-based data assimilation systems, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4082, https://doi.org/10.5194/egusphere-egu24-4082, 2024.

Frequent air pollution episodes pose severe health and environmental challenges in Tehran, Iran. Despite recent efforts, pollutant levels often exceed WHO-based national standards. This study addresses the pressing need for accurate air quality prediction by leveraging advanced satellite data and machine learning techniques. Our methodology integrates Sentinel-5P satellite data with optical depth remote sensing information. We systematically evaluated five machine learning algorithms to identify the most effective approach for AQI prediction. This study aims to advance air quality prediction in Tehran by integrating Sentinel-5P satellite data with machine learning algorithms. We examined the efficacy of various algorithms, including Decision Tree, K-Nearest Neighbors, Random Forest, Support Vector Machine, and Logistic Regression, in correlating air pollutant levels with the Air Quality Index (AQI). The selection criteria focused on algorithmic efficiency and accuracy in handling diverse environmental datasets. The Random Forest algorithm, utilizing Sentinel-5P and optical depth data, achieved a remarkable accuracy of 74% in predicting AQI. Further enhancement was observed by incorporating climatic data, COVID-19 status, and environmental parameters; the model achieved a significant predictive accuracy of up to 75.6%. These findings underscore the critical impact of nitrogen dioxide, ozone, and aerosol optical depth on Tehran's AQI, with notable variations observed post-COVID-19 restrictions. The increase in AQI following the lifting of COVID-19 restrictions suggests a significant correlation between human activity and air quality. These insights can inform targeted environmental policies in Tehran. We demonstrate the potential of integrating satellite data with machine learning to predict AQI accurately. Our approach offers a scalable model for urban air quality management with implications for environmental policy and public health initiatives.

How to cite: Kafi, A. M., Hosseinipoor, M., Zare Shahne, M., and Jamaat, A.: Integrating Sentinel-5P Satellite Data and Machine Learning Algorithms for Air Quality Index Prediction in Tehran: A Comprehensive Study on Factors Influencing Air Quality, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4506, https://doi.org/10.5194/egusphere-egu24-4506, 2024.

EGU24-5305 | ECS | Posters on site | AS1.6

Testing Hybrid-3DEnVar in the convective scale NWP model AROME-Austria 

Kaushambi Jyoti, Martin Weissmann, Philipp Griewank, and Florian Meier
We test a Hybrid 3-Dimensional Ensemble Variational (Hybrid-3DEnVar) Data Assimilation (DA) method in the limited-area NWP model AROME over Austria at 2.5km horizontal resolution, with a flow-dependent error covariance matrix sampled from a 50-member ensemble.
Rapidly evolving highly non-linear convective-scale processes and the unique orography of the Austrian Alps intensify the complexities of estimating model error correlations. While the climatological error covariance matrix can not well represent the non-linear error growth of convective-scale weather, these errors can be incorporated into the assimilation using the ensemble-based error covariance matrix. We explore 11 weighted combinations of climatological and sampled covariance matrices, ranging from a purely climatological (weight of 0) to a purely ensemble-based (weight of 1) B-matrix, with incremental weight adjustments to the ensemble by 10 percent increments. The pure climatological configuration (3-dimensional variational data assimilation, 3DVar) is the operational DA scheme of GeoSphere Austria and serves as a comparative benchmark for our experiments. Multiple distinct summertime convective weather scenarios with a special focus on local convection were tested, while cold and warm fronts also influenced some of these cases. Aircraft wind and temperature observations are split into assimilated and non-assimilated parts so that the latter serves as validation for the analysis.
The resulting analysis from the Hybrid-3DEnVar configuration outperforms the operational 3DVAR of GeoSphere Austria, indicating a substantial leap forward in forecast accuracy of convective scale weather within Austria’s complex terrain. However, the optimal weight to the ensemble-based covariances for the optimal analysis strongly depends on the weather phenomenon investigated.
Keywords: AROME-Austria, Hybrid-3DEnVar, a 50-member ensemble, convective scale, and non-linear error growth.
 
 
 

How to cite: Jyoti, K., Weissmann, M., Griewank, P., and Meier, F.: Testing Hybrid-3DEnVar in the convective scale NWP model AROME-Austria, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5305, https://doi.org/10.5194/egusphere-egu24-5305, 2024.

EGU24-5399 | ECS | Posters on site | AS1.6

Characteristics of Warm‐Season Mesoscale Convective Systems Over the Yangtze–Huaihe River Basin (YHR): Comparison Between Radar and Satellite 

Yutong Lu, Jianping Tang, Xin Xu, Ying Tang, and Juan Fang

Mesoscale convective systems (MCSs) are crucial in modifying the water cycle and frequently induce high-impact weather events over eastern China. Radar and Climate Prediction Center (CPC)-4 km satellite-derived infrared cloud top temperature (Tb) data were used to thoroughly analyze the long-term climatology of MCSs over eastern China, particularly in the Yangtze–Huaihe River Basin (YHR) in the warm season from 2013 to 2018. For the first time, we contrasted the effects of data set selection and threshold setting on research outcomes. The large-scale environments of MCSs initiation were also investigated using the latest global reanalysis data ERA5. It is found that striction of thresholds, including duration, reflectivity/Tb, area, and linearity, would lead to a greater proportion of early-morning MCSs. Satellite-identified MCSs differed from radar-derived ones, exhibiting afternoon diurnal peaks, faster movement speeds, longer travel distances, and expansive impact areas. The center of MCS and related precipitation shifted northward from Pre-Meiyu to Post-Meiyu seasons, contributing to up to 20% of total rainfall, with most MCSs moving along eastward trajectories. MCSs typically had the most substantial impact in the Meiyu season because of the most prolonged duration, largest convective core area, and strongest precipitation intensity. Warm-season MCSs initiated ahead of midlevel troughs and were related to strong anomalous low-level convergence and midlevel upward. The circulation anomalies were the strongest in the Pre-Meiyu season among the three subseasons, with most moisture sourced from the southwest.

How to cite: Lu, Y., Tang, J., Xu, X., Tang, Y., and Fang, J.: Characteristics of Warm‐Season Mesoscale Convective Systems Over the Yangtze–Huaihe River Basin (YHR): Comparison Between Radar and Satellite, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5399, https://doi.org/10.5194/egusphere-egu24-5399, 2024.

Using the FengYun (FY) satellite products and hourly rain gauge data, the east-moveing regional rainfall events (RREs) with long duration and large areas originated in the northeastern Tibetan Plateau (TP) were identified. Our findings reveal that 70% of heavy and long-duration(≥6h) RREs originating in the northeastern TP have the potential to move a thousand kilometers eastward during the warm-season. We noted distinct differences in the speed and spatial location of rainfall for the two types of eastward-moving RREs under investigation.. For the long-distance eastward-moving RREs, three local enhancements of precipitation centers, corresponding to the center moving out of 105°E, 110°E and 115°E are evident. In contrast, for the short-distance eastward-moving RREs, the precipitation centers mainly reach the second topographical terrace without further eastward moving. The evolution of mid-level trough and upper troposphere warm anomalies are closely related to the eastward-moving RREs. With the eastward movements of middle troposphere trough, coupled with the synergistic effects of the convergence and a change in wind orientation at the lower level, and the divergence at the upper-level, collectively contribute to the long-distance eastward moving RREs. The short-distance eastward moving RREs, influenced by the ridge of western Pacific subtropical high over North China and the low-level anomalous anticyclone, remains west of 110°E. This study offers an in-depth understanding of how upstream precipitation events influences the downstream rainfall.

How to cite: Chen, H. and He, M.: The characteristics of eastward-moving regional rainfall events originating in the northeastern of Tibetan plateau, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5848, https://doi.org/10.5194/egusphere-egu24-5848, 2024.

EGU24-5880 | Orals | AS1.6

Multiday mesoscale soil moisture persistence and atmospheric predictability – an illustration from the Sahel 

Christopher Taylor, Cornelia Klein, and Bethan Harris

The hydro-climate of the Sahel is dominated by organised Mesoscale Convective Systems (MCSs), which typically bring intense rain every few days during the West African Monsoon season. MCSs leave a swath of wet soil often hundreds of kilometres across, which in turn create strong spatial patterns of surface fluxes of heat and water back into the atmosphere. Previous studies have shown that soil moisture patterns exert a strong control on the initiation and propagation of MCSs, significantly enhancing the predictability of convection on scales of 10 – 100s km. Here, we use satellite observations to examine how this strong, locally negative, soil moisture-precipitation feedback evolves and impacts rainfall patterns over a series of storms.

We track the response of the surface and atmosphere to over 5,000 MCS events from the period 2004-2020, using a combination of satellite-derived products (Land Surface Temperature; LST, soil moisture, Vegetation Optical Depth, rainfall, cloud-top temperature). Initial anomalies in LST and soil moisture weaken rapidly in the 3-4 days after the MCS, particularly in climatologically wetter regions. However, a statistically significant memory of the original MCS event still remains in surface anomalies out to 20 days. In terms of rainfall, we see a strong suppression of convection in the first 48 hours after the MCS in areas which initially received heavy rain. There is also some evidence of enhanced MCS activity around the edges of the original swath in the first 4 days. The persistence over several days of mesoscale rainfall patterns anti-correlated with the original MCS point to an important role for surface-atmosphere feedbacks. Synoptic forcing cannot explain the finer scale rainfall response, whilst post-MCS cold pool effects are too short-lived. On longer time scales (5-20 days) in climatologically drier areas, we also find a weak but statistically significant enhancement of rainfall around the original initiation zone.

These results have important implications for rainfall forecasting on scales of tens to several hundred kilometres. Pre-existing soil moisture heterogeneity provides strong predictability of where future convection will occur under favourable synoptic conditions. This provides skill out to 2-4 days, but strongly depends on regional rainfall frequencies. Because new MCSs create new soil moisture patterns, the combination of storms every few days and a strong negative land feedback at the mesoscale actively degrades longer term predictability within the rainy season, effectively limiting intra-seasonal to seasonal forecast skill for severe weather.

How to cite: Taylor, C., Klein, C., and Harris, B.: Multiday mesoscale soil moisture persistence and atmospheric predictability – an illustration from the Sahel, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5880, https://doi.org/10.5194/egusphere-egu24-5880, 2024.

EGU24-6302 | Orals | AS1.6

Relationships between growing cloudy updrafts, deep convection initiation, and orographic flow 

James Marquis, Adam Varble, Zhe Feng, Enoch Jo, and William Gustafson

Shallow cumulus cloud fields often organize and deepen within regions of mesoscale ascent associated with orographic flows. However, there is significant uncertainty in the relative roles of mesoscale and cloud-scale factors ultimately controlling the location of orographic deep convection initiation (DCI). These factors include spatial heterogeneity of the magnitude of mesoscale vertical mass flux associated with orographic convergence, near-cloud convective ingredients (e.g. CAPE, CIN, LFC, and shear), and entrainment effects. More fundamentally, it is not well understood how these factors influence the initial width and strength of low-level cloudy updrafts, which are increasingly cited as important governors of their ultimate depth potential. Thus, it is important to better understand these relationships for increased predictability of DCI.

 

Numerous DCI events observed along the Sierras de Córdoba range during the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) project were modeled by the U.S. Department of Energy’s LES ARM Symbiotic Simulation and Observation (LASSO) team. In this study, we examine the connection between low-level cloudy updrafts, DCI, and the ascent associated with the mesoscale orographic circulation using LES with 100-m and 500-m grid spacing across multiple days. We hypothesize that the width and strength of low-level cloudy updrafts and the probability of DCI events along the ridge are proportional to the width, strength, and depth of the local orographic convergence. To test this, we examine correlations between the width and depth of developing cloudy updrafts and the: i) 3D structure of the evolving orographic ascent, and ii) convective meteorological ingredients (e.g., convective available potential energy, convective inhibition, level of free convection, moisture, etc.).

 

Preliminary results indicate that DCI events do not always occur in regions of the strongest or widest orographic ascent along the mountain range. Further, the strength and width of low-level cloudy updrafts that precede DCI are only weakly correlated with most orographic ascent metrics. Overall, the apparent relative roles of mesoscale ascent and convective sounding parameters governing DCI varied significantly across case days. Near-cloud relative humidity located near and just above the level of free convection steadily increased with time during each afternoon, likely owing to orographic vertical moisture flux and/or cloud detrainment. Thus, in addition to highly varied roles of the background conditions, the fate of individual growing cloudy updrafts may further depend on complex cloud-scale factors, such as entrainment and microphysical processes.

How to cite: Marquis, J., Varble, A., Feng, Z., Jo, E., and Gustafson, W.: Relationships between growing cloudy updrafts, deep convection initiation, and orographic flow, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6302, https://doi.org/10.5194/egusphere-egu24-6302, 2024.

  Recently, the increase in convective storms that develop rapidly within a short period of time and on a very small area causes severe damage to property and human life. Thus, it is important to understand the characteristics of convective activities and to provide the information about severity of the developing storms.  In order to address these issues, object-based analysis of convective systems is essential to provide severity information on convective precipitation systems including their life-cycle from initiation to dissipation. 
  In this study, we analyzed the developing stage of convective storms by using the statistics of storms detected by the Fuzzy Logic Algorithm for Storm Tracking (FAST). The Column Maximum (CMAX) was used to provide the information on detection and severity of storms. A convective storm was defined as a CMAX values above 35dBZ and small convective cells with an area less than 20km2 were filtered out. The identified storm was tracked on a fuzzy basis using storm speed and its morphological characteristics. Within the detected storm area, we analyzed the characteristics of the storm by averaging variables such as reflectivity (ZH), echo top height corresponding to ZH, rainfall rate at 1.5km altitude, VIL (Vertical Integrated Liquid) contents, etc.
  This study aims to provide quantitative information on severity of individual storms by using these radar variables and storm characteristics. We calculated and modified the threshold values of each predictor for determining the severity of the convective storms. Furthermore, we plan to analyze the intensity and frequency of severe precipitation storms in associated with the occurrence or absence of lightning event during their life cycle.

Key words : Weather Radar, convective storms, Radar parameter, storm severity

※ This research was supported by the "Development of radar based severe weather monitoring technology (KMA2021-03121)" of "Development of integrated application technology for Korea weather radar" project funded by the Weather 

How to cite: Kang, E., Kwon, S., and Lee, S.: Analysis of convective storm characteristics to classify the storm severity information using weather radar variables, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6900, https://doi.org/10.5194/egusphere-egu24-6900, 2024.

EGU24-8069 | ECS | Posters on site | AS1.6

Towards the assimilation of dual-polarization radar data 

Tatsiana Bardachova, Maryam Ramezani Ziarani, and Tijana Janjic

The forecast accuracy of numerical weather prediction models is strongly determined by the precision of the initial conditions, especially for storm and convective-scale weather prediction. Since radars allow to capture the internal structure and important microphysical and dynamical processes in convective systems, they are crucial instrument for improvement of weather forecasts on these scales. Dual-polarization radar, in contrast to a prevalent single-polarization radar, also provides information on the types and sizes of hydrometeor particles. As a result, polarimetric radar data (PRD) proves to be a valuable data source for data assimilation. However, direct assimilation of PRD is not used in current operational non-hydrostatic convection-permitting numerical models. This is associated with several difficulties, such as model error estimation, which require further study.

The current focus of our study is to directly assimilate PRD in an idealized setup. For that purpose, observation system simulation experiments (OSSEs) were performed that simulate the development of a long-lived supercell using the ICON model with two-moment microphysics scheme. In OSSE, the Kilometer-scale Ensemble Data Assimilation (KENDA) system, which comprises the Local Ensemble Transform Kalman Filter (LETKF) was used. The new polarimetric radar forward operator EMVORADO-POL developed at Deutscher Wetterdienst (DWD) was incorporated in the setup. The first steps towards the direct assimilation of differential reflectivity, in addition to non-polarimetric variables, have been implemented and will be presented. Proper thresholds and model equivalents of polarimetric data were examined. Results were compared to reference experiments assimilating non-polarimetric variables such as reflectivity and radial velocity.

How to cite: Bardachova, T., Ramezani Ziarani, M., and Janjic, T.: Towards the assimilation of dual-polarization radar data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8069, https://doi.org/10.5194/egusphere-egu24-8069, 2024.

EGU24-8739 | ECS | Posters on site | AS1.6

Trends in Warm Season Mesoscale Convective Systems OverAsia in 2001–2020 

Yuanjing Guo, Qiang Fu, L. Ruby Leung, Ying Na, and Riyu Lu

Mesoscale convective systems (MCSs) frequently occur over Asia during the warm season, often producing intense precipitation with associated socioeconomic impacts. Here we reveal significant trends in MCS occurrence frequency and related precipitation in Asia during the warm season (March–September) in 2001–2020, using a tracking method that combines cloud and precipitation criteria with high-resolution satellite data from the Global Precipitation Measurement mission. To examine whether there are differences between MCSs of different scales, both meso-α scales (MαCSs) and meso-β scales (MβCSs), with horizontal scales of 200–2,000 km and 20–200 km, are tracked. The distribution pattern of frequency and related precipitation of both MαCSs and MβCSs are quite similar and manifest positive trends over East Asia (EA) and Northeast Asia, and negative trend over Southeast Asia (SEA). The MCS precipitation trend contributes significantly to total precipitation trend, with MαCSs contributing the most. Our analysis indicates the trend in lower-tropospheric water vapor flux convergence has a similar spatial pattern to the MCS frequency and related precipitation trend. Based on an atmospheric moisture flux decomposition analysis, the water vapor flux convergence trend can largely be explained by the change in horizontal wind convergence, while the specific humidity trend driven largely by temperature change plays a minor role. The trend in wind convergence in EA and SEA is possibly related to the evident trend in the lower-tropospheric anticyclone over the western North Pacific and SEA, which might be due to the relatively stronger warming in the Indian Ocean during the past two decades.

How to cite: Guo, Y., Fu, Q., Leung, L. R., Na, Y., and Lu, R.: Trends in Warm Season Mesoscale Convective Systems OverAsia in 2001–2020, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8739, https://doi.org/10.5194/egusphere-egu24-8739, 2024.

EGU24-8810 | ECS | Posters on site | AS1.6

Investigating the life-cycle of convective clouds from 4D observational data 

Sarah Brüning and Holger Tost

Convective clouds play a crucial role for understanding the Earth’s climate. Current advancements of remote sensing instruments allow us to obtain valuable information on the spatio-temporal dynamics of convective clouds on multiple scales. Nevertheless, a continuous coverage of high-resolved 4D observational data to investigate the 3D properties of rapidly developing convective clouds is generally not available.

In this study, we leverage 4D radar reflectivities (in dBZ) derived from the extrapolation of passive and active remote sensing sensors with machine learning to close this gap. Using data with a spatial resolution of 3 km and a temporal resolution of 15 minutes, we receive a continuous perspective on the evolution of the cloud vertical column along the different stages of the cloud life-cycle. For this purpose, we apply an object-based algorithm to detect the centroid of convective cores and their anvil at each time step. Based on these centroids, we extract the 3D cloud field and track the horizontal and vertical movement through space and time. Afterwards, we filter all tracks using the vertical extension and maximum reflectivity of the associated cloud field to exclude erroneous features.

Here, we present an evaluation of the algorithm and its ability to investigate the 4D spatio-temporal properties of convective clouds. We set out to compare convective systems of different sizes over both oceans and continents to analyze the impact of varying environmental conditions on the cloud vertical motions along the cloud life-cycle.

How to cite: Brüning, S. and Tost, H.: Investigating the life-cycle of convective clouds from 4D observational data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8810, https://doi.org/10.5194/egusphere-egu24-8810, 2024.

EGU24-8868 | ECS | Posters on site | AS1.6

Situation-Dependent Localization for All-Sky Satellite Observations 

Tobias Necker, Takumi Honda, Philipp Griewank, Takemasa Miyoshi, and Martin Weissmann

This study aims to improve the localization and assimilation of satellite observations in the visible and infrared spectral ranges to enhance predictions of clouds and convective processes. Understanding correlation structures between satellite observations and atmospheric state variables is crucial for successful data assimilation. We focus on examining vertical ensemble-based correlations from Himawari-8 channels (VIS0.64 or IR7.35) and tackle the challenge of vertical observation-space localization. Traditional distance-based localization methods are often suboptimal due to the multi-layered origin of observed radiation. We present empirical optimal localization (EOL) functions derived from a 1000-member ensemble convective-scale simulation to address this issue. Our research highlights the need for channel-specific and variable-specific localization strategies, emphasized by our analysis of two summer case studies that exhibit substantial situational variability in correlation structures, especially in the visible spectral range. Further, we explore various predictors for formulating dynamic, situation-specific vertical localization strategies, offering insights into their effectiveness and potential for advancing convective-scale satellite data assimilation.

How to cite: Necker, T., Honda, T., Griewank, P., Miyoshi, T., and Weissmann, M.: Situation-Dependent Localization for All-Sky Satellite Observations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8868, https://doi.org/10.5194/egusphere-egu24-8868, 2024.

EGU24-9241 | ECS | Orals | AS1.6

Designing a Global Weather Station Network 

Stavros Keppas, Haris Balis, Ioannis Dravilas, and John Pagonis

Designing a weather station network is a demanding, multi-objective optimisation problem and usually constrained to local geographies. In this study, the authors deviate from typical approaches that focus the design of weather station networks on a small or country-wide area and present a method that is applicable on a global scale.

Prior art suggests that weather networks should exhibit high density, often at 1-3km or finer resolution, especially when deployed over complex topographies and urban landscapes. High station density is usually required to support research on urban micrometeorology, agricultural applications and to capture intricate meteorological mesoscale phenomena such as convective precipitation and sea breeze. High density is also required due to the persistence of temperature inversions at near-surface layers is significantly influenced by topography, leading to prolonged periods of temperature inversion.

In this novel approach, the authors suggest the design of a global weather network distributed over millions of hexagons covering the entire world. The number of weather stations per hexagon is determined by the topology (e.g. maximum elevation difference, aspects, water formations, etc.) and the land use (urban coverage, green areas, etc.) of the covered area.

The method is materialized via an open-source software tool (available on GitHub) which utilizes freely available elevation data (Copernicus DEM) and land use data (OpenStreetMap) and is capable of preparing the global weather station network in reasonable computation time (~24 hours on a 16-core CPU).

Finally, the authors present their findings, discuss the effect of various hexagon sizes and suggest that the design of a global weather station network is viable and computationally feasible.

How to cite: Keppas, S., Balis, H., Dravilas, I., and Pagonis, J.: Designing a Global Weather Station Network, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9241, https://doi.org/10.5194/egusphere-egu24-9241, 2024.

EGU24-9266 | ECS | Orals | AS1.6

Impact of aerosol and microphysical uncertainty on the evolution of a severe hailstorm 

Patrick Kuntze, Annette Miltenberger, Corinna Hoose, Michael Kunz, and Lena Frey

Forecasting high impact weather events is a major challenge for numerical weather prediction. Initial condition uncertainty plays an important role but so do uncertainties arising from the representation of subgrid-scale processes, e.g. cloud microphysics. Here, we investigate the impact of cloud microphysical parameter uncertainties on the forecast of a selected severe convective storm over South-Eastern Germany in 2019, which is generally referred to as the Munich hailstorm (Wilhelm et al., 2020).
The storm is simulated using the ICON model (2-moment cloud microphysics, 1 km grid-spacing) with perturbed microphysical parameters related to graupel and hail formation. Combinations of parameter perturbations are chosen according to a Latin hyper cube design and one-at-a-time parameter perturbations for the smallest and largest parameter values. Important impacts on surface (hail) precipitation are found for parameters pertaining to (i) CCN and INP activation, (ii) diffusional growth of ice, and (iii) the mass-diameter and mass-fall velocity relations for graupel. The behavior of graupel particles are thereby controlled by their density.
The one-at-a-time parameter perturbation simulations are used to track microphysical process rates. By closing the hydrometeor mass budgets we explore changes in precipitation formation pathways (based on the approach by Barrett and Hoose, 2023) arising from perturbations of the most impactful parameters. Preliminary results show a strong influence of graupel density on the hail particle size distribution as well as total precipitation, but less so on surface hail amount.
The analysis allows us to draw conclusions about the most impactful cloud microphysical parameters for hail forecast uncertainty as well as the underlying mechanisms.

How to cite: Kuntze, P., Miltenberger, A., Hoose, C., Kunz, M., and Frey, L.: Impact of aerosol and microphysical uncertainty on the evolution of a severe hailstorm, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9266, https://doi.org/10.5194/egusphere-egu24-9266, 2024.

EGU24-11456 | ECS | Orals | AS1.6

Thunderstorm and Hail Frequencies in South America and Australia Based on Overshooting Tops 

Jannick Fischer, Michael Kunz, and Kristopher Bedka

Convective storms over South America and Australia are among the most intense worldwide (e.g., Zipser 2006). However, they are less researched compared to US and Europe. This study analyses the thunderstorm climatology over South America and Australia based on over 20 years of overshooting cloud top (OT) satellite detections (Khlopenkov et al. 2021). These OTs serve as robust, horizontally homogeneous indicators of strong updrafts and hence intense thunderstorms. Furthermore, we focus on the frequency of severe storms and hail by using ERA5 Reanalysis data to exclude OTs in unfavorable environments (e.g., Punge et al. 2023).
The resulting climatologies of intense thunderstorms and hail are largely consistent with existing literature, showing strong thunderstorm activity in tropical regions but more severe (e.g., hail-producing) storms in south-central South America and southeast Australia. Some notable details will also be discussed, such as the discrepancy with observational hotspots near the coast in South America and a surprisingly strong signal over northwest Australia. Furthermore, regarding a climate change signal, preliminary analysis indicates no significant trend for South America. However, the multi-year variations are strongly linked to the El Ninjo-Southern Oscillation (ENSO).

How to cite: Fischer, J., Kunz, M., and Bedka, K.: Thunderstorm and Hail Frequencies in South America and Australia Based on Overshooting Tops, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11456, https://doi.org/10.5194/egusphere-egu24-11456, 2024.

EGU24-12158 | Orals | AS1.6

Severe Storms Research at ESSL 

Pieter Groenemeijer, Francesco Battaglioli, Tomáš Púčik, Alois Holzer, and Mateusz Taszarek

Convective storms are an important weather hazard in Europe as shown by the high number of severe wind gusts, large hail, tornadoes, and flash floods recorded each year in the European Severe Weather Database. A recent innovation to the ESWD was the introduction of the new tornado International Fujita scale for rating tornado and wind intensity from damage. In 2023, no fewer than 62182 new reports were entered, and reinsurer Munich Re estimated severe thunderstorms to account for the majority of weather-related losses in Europe in 2023 with a total damage of € 10 billion.

At the core of ESSL’s mission is conducting and facilitating research on severe weather at a European level. Over the years, the organisation has grown with support from its members which include most of Europe’s weather services and commercial sector partners. In addition to research ESSL is active in the area of forecaster training and the evaluation of novel forecasting and nowcasting applications at the ESSL Testbed.

The recorded multi-year trends of severe weather apparent in the ESWD are often dominated by non-meteorological factors, but for large hail indications are strong that its frequency is changing, illustrated by the new hailstone size record of 19 cm diameter in northern Italy in July 2023. ESSL’s recent models of large hail climatology across Europe and the world support these trends. A key challenge for the research community is to develop methods to estimate trends from ever higher-resolution reanalyses and climate models. This is not straightforward as even the highest resolution models do not resolve tornadoes or microbursts, let alone hailstones, and already show biases at coarse scales.

The mentioned work modelling severe weather has given new insights into which environmental characteristics are important to severe weather occurrence. For hail, we additionally studied the conditions under which individual hailstorms in 2021, 2022, and 2023 that were particularly severe. We show the importance of the vertical distribution of buoyancy and wind in a storm-centred reference framework, defined using radar-observed storm motion.

High vertical wind shear above the boundary layer and high CAPE above the -10 °C isotherm for hail, and a combination of vertical vorticity and strong streamwise vorticity for tornadoes. ESSL is collaborating with ECMWF to develop forecast tools based on these concepts. That said, many questions remain, for example regarding the pre-convective environment and mountain ranges, and with the developing storms. For instance, an important concentration of severe weather is evident surrounding the Alps. To address related questions, ESSL has taken the initiative for a multi-year multi-national field campaign in central Europe called TIM (Thunderstorm Intensification from Mountains to plains), in which it will collaborate with a large number of research institutes.

How to cite: Groenemeijer, P., Battaglioli, F., Púčik, T., Holzer, A., and Taszarek, M.: Severe Storms Research at ESSL, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12158, https://doi.org/10.5194/egusphere-egu24-12158, 2024.

EGU24-12221 | ECS | Posters on site | AS1.6

Preparing AROME assimilation experiments for cloud-affected satellite observations 

Sandy Chkeir, Martin Weissmann, Philipp Griewank, Florian Meier, and Adhithiyan Neduncheran

Despite the abundance of satellite observations, their assimilation in all-sky scenarios remains difficult, which hinders the use of high-resolution information in forecast models. In this work, we focus on the direct assimilation of satellite radiances (visible 0.6 μm, infrared IR 6.2 and 7.3 μm of the Seviri instrument) under all-sky conditions into the convection-permitting Numerical Weather Prediction (NWP) model AROME, which is in operation at Geosphere Austria, the Austrian weather service. Our research aims to exploit the potential of assimilating visible and IR channels under all-sky conditions making use of convection-permitting weather models that can explicitly resolve deep convection. In particular, we are looking at the use of the Radiative Transfer for TOVS (RTTOV), as an observational operator, to generate synthetic images for each channel. We endeavor to optimize the operator settings for running simulations within the convective-scale AROME model. Our first experiment focuses on testing IR synthetic images generated under all-sky conditions during a summer period (August) over Austria. 

How to cite: Chkeir, S., Weissmann, M., Griewank, P., Meier, F., and Neduncheran, A.: Preparing AROME assimilation experiments for cloud-affected satellite observations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12221, https://doi.org/10.5194/egusphere-egu24-12221, 2024.

EGU24-12544 | Orals | AS1.6

Testing ensemble-based estimates of potential observation impact 

Philipp Griewank, Tobias Necker, and Martin Weissmann

While ensemble methods to estimate the impact of assimilated observations on forecast error have been widely used (known as EFSO), similar methods to estimate the benefit of potential observations not assimilated have received less attention. For this presentation we use a toymodel to illustrate these methods and highlight their strengths and weaknesses. We show that these methods work well over a wide range of lead times and for different types of observations, but only when the localization used in ensemble data assimilation to mitigate sampling errors is accounted for. While previous studies struggled to achieve quantitative results because they treated the localization inconsistently, we found three methods to overcome this limitation. 

How to cite: Griewank, P., Necker, T., and Weissmann, M.: Testing ensemble-based estimates of potential observation impact, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12544, https://doi.org/10.5194/egusphere-egu24-12544, 2024.

EGU24-13519 | ECS | Posters on site | AS1.6

Impacts of an upper tropospheric cold low on the extreme precipitation in Henan Province, China in July 2021 

Liangliang Li, Wenshou Tian, Jian Li, Jinlong Huang, Rui Wang, and Jiali Luo

From 19 to 21 July 2021, Henan province of China experienced an extreme precipitation event that caused massive flooding and great loss of lives. This event is thus far the second heaviest precipitation event observed by rain gauges in this region. Based on the ERA5 reanalysis data, the ECMWF operational global ensemble forecasts and numerical simulations using the ARW-WRF model, impacts of an upper tropospheric cold low (UTCL) on the extreme precipitation are examined. It is found that due to the influence of the persistent intrusion of stratospheric high potential vorticity (PV) air, a long-lived UTCL was detached from the upper level flow a week prior to the extreme precipitation event. The UTCL then moved westward, reaching the Yellow Sea and the East China Sea and maintaining there until the precipitation event ended. During this event, a broad northeast-southwest oriented area of ascending motion associated with the UTCL could be observed in front of the UTCL and strong ascending motions developed in the upper troposphere above Henan province. Analysis of the ECMWF operational global ensemble forecasts reveals that the amount of precipitation over Henan is positively correlated with the UTCL intensity. The UTCL impact on the extreme precipitation and the underlying mechanisms are further investigated based on results of numerical experiments. The control experiment reasonably reproduces the UTCL location as well as the distribution and evolution of the extreme precipitation. When the UTCL intensity is reduced in the initial condition using the piecewise PV inversion for sensitivity experiment, the upper tropospheric divergence reduces correspondingly and the dynamical ascending motion weakens in the second precipitation stage. As a result, the amount and intensity of precipitation both decrease. When the UTCL is completely removed from the initial condition, the sensitivity experiment indicates that the upper tropospheric divergence and dynamical ascending motion further weaken, resulting in a large decrease in precipitation intensity during the whole precipitation period. These findings highlight that the occurrence of the long-lived UTCL is a crucial factor that affects the intensity of the extreme precipitation event.

How to cite: Li, L., Tian, W., Li, J., Huang, J., Wang, R., and Luo, J.: Impacts of an upper tropospheric cold low on the extreme precipitation in Henan Province, China in July 2021, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13519, https://doi.org/10.5194/egusphere-egu24-13519, 2024.

EGU24-13906 | ECS | Orals | AS1.6

Multi-scale interaction and predictability of moist convection and tropical cyclones  

Masashi Minamide and Derek Posselt

Predicting tropical cyclone intensity changes, especially the onset of rapid intensification, has been a more challenging topic than tropical cyclone tracking because of its chaotic nature in multi-scale physical process with significant contributions from convective-scale phenomena. Before intensification onset, tropical cyclones experience precession process, in which tilted vortices rotate counterclockwise around the center of circulation, and develop an axisymmetric structure. The forecast uncertainty in precession process limits the predictability of early-stage development and intensification of TCs.

In this study, we have explored the contribution of moist convective activity to the predictability and variability of TC intensification onset through the precession process. Our recent investigation in Minamide and Posselt (2022) proposed a Lagrangian-based approach to identify the potential signals of individual convective occurrence. Using the technique, we conducted sensitivity experiments to control specific convective activities within the inner-core of early-stage TCs with convection-permitting Weather Research and Forecasting model (WRF-ARW). The results indicate that the spatiotemporal variability of convective activity even governs whether early-stage vortex completes precession and initiates RI, indicating the importance of accurately constraining convective activity in the severe weather event predictions. Given the strong nonlinearity of the onset process of RI, the advancement of our understanding of the uncertainty sources will provide an insight about the observation network that may effectively constrain the TC forecasting.

How to cite: Minamide, M. and Posselt, D.: Multi-scale interaction and predictability of moist convection and tropical cyclones , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13906, https://doi.org/10.5194/egusphere-egu24-13906, 2024.

Atmospheric Rivers (ARs) transport vast amounts of water vapor from the tropics to mid-latitudes, resulting in sustained, heavy precipitation that explains about 50 % of mid-latitude annual mean rainfall. AR events over the Western US have shown particularly high societal impact, where orographic and soil conditions make communities vulnerable to floods and mudslides. Climate modelling approaches for capturing extreme precipitation and water runoff on land are both strongly constrained by the horizontal resolution that is currently deployed, typically on the order of 100 km. Such grid spacing neither allows for explicitly resolving key processes associated with extreme precipitation like atmospheric convection, nor complex terrain that controls water runoff. However, recent advances in computational capabilities and model development at the Geophysical Fluid Dynamics Laboratory (GFDL) at a finer horizontal resolution of 50 and 25 km have shown promising perspectives for simulating important characteristics of ARs and their associated mean and extreme precipitation. In addition, advances in GFDL land model hydrology now allow for investigating climate model capabilities in predicting precipitation induced flood hazard precursors like excessive runoff and streamflow in a physically coupled, orography-aware atmosphere-land framework.

Here, we make use of the high resolution GFDL coupled atmosphere-land model by running hindcast experiments for a handful of high impact AR events over the Western US. We evaluate the model’s predictive skill in AR associated precipitation by running ensemble forecasts on weather time scales, which we evaluate against observations and reanalysis. We attribute the found biases in terms of dynamical and thermodynamic drivers, revealing current model constraints. Accounting for the biases found in precipitation, we turn to the land hydrology and evaluate catchment associated hydrological characteristics, which we compare to satellite derived and in-situ observations.

How to cite: Prange, M., Zhao, M., and Shevliakova, E.: Evaluating historic atmospheric river associated extreme rainfall and its flooding potentials based on a high-resolution climate model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13964, https://doi.org/10.5194/egusphere-egu24-13964, 2024.

EGU24-15473 | ECS | Posters on site | AS1.6

The assimilation of surface observations in mountainous terrain in the WRFDA system 

Giorgio Doglioni, Stefano Serafin, Martin Weissmann, Gianluca Ferrari, and Dino Zardi

Assimilating surface observations in convective scale data assimilation (DA) systems is not straightforward, since these observations may be affected by small-scale effects not represented in the model, and the model itself might not be able to accurately represent the features of the atmosphere close to the surface. These issues are particularly evident in mountainous terrain. In variational DA systems, such as the Weather Research and Forecasting, Data Assimilation (WRFDA) suite, the available background error (BE) models produce BE variances and covariances that vary smoothly over long distances. Therefore, for instance, assimilating a valley-floor surface observation typically leads to large analysis increments even at nearby mountain tops, which are physically unwarranted and cause high levels of gravity-wave noise. 

Such problems can be partially mitigated in WRFDA by modeling the BE using the Alpha Control Variable Transform (Alpha CVT). 

Like other BE models in WRFDA, this technique derives BE statistics from an ensemble of differences between forecasts with different initial and identical valid times (NMC method), and it makes use of a control variable transform (CVT). Differently from other BE models in WRFDA, it computes analysis increments as a linear combination of the NMC ensemble members.

In this work we consider simulations with a grid spacing of 3.5 km over a domain encompassing the European Alps. We first use pseudo-observation tests to show how different BE specifications in WRFDA affect the assimilation of surface observations of temperature, specific humidity, pressure and horizontal winds components in complex terrain.

We then present real-case assimilation experiments with a limited set of surface observations. Considering the consistency between the variances of innovations and the assigned observation and background errors, we demonstrate the positive impact of the Alpha CVT.

How to cite: Doglioni, G., Serafin, S., Weissmann, M., Ferrari, G., and Zardi, D.: The assimilation of surface observations in mountainous terrain in the WRFDA system, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15473, https://doi.org/10.5194/egusphere-egu24-15473, 2024.

EGU24-15891 | ECS | Posters on site | AS1.6

Optimizing Kilometer-Scale Climate Modeling: Refining Cloud Microphysics Using Machine Learning and Satellite Correlation 

Hannah Marie Eichholz, Jan Kretzschmar, Josefine Umlauft, and Johannes Quaas

The modeling of the Earth Climate System has undergone outstanding advances to the point of resolving atmospheric and oceanic processes on kilometer-scale, thanks to the development of high-performance computing systems. In the preparation phase of the global kilometre-resolution coupled ICON climate model, there's a critical need to fine-tune cloud microphysical parameters. Our approach involves investigating the optimal calibration of these parameters using machine learning techniques.

Our initial focus involves calibrating the autoconversion scaling parameter by correlating it with satellite-derived top-of-atmosphere and bottom-of-atmosphere radiation fluxes. This calibration process entails conducting limited area simulations specifically within the North Atlantic and South Pacific region using ICON. Through these simulations, various adjustments to cloud microphysical parameters are made, aiming to assess their potential impacts on radiation flux output.

How to cite: Eichholz, H. M., Kretzschmar, J., Umlauft, J., and Quaas, J.: Optimizing Kilometer-Scale Climate Modeling: Refining Cloud Microphysics Using Machine Learning and Satellite Correlation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15891, https://doi.org/10.5194/egusphere-egu24-15891, 2024.

EGU24-17689 | ECS | Posters on site | AS1.6

Impact of microphysical perturbations on convective precipitation predictability 

Beata Czajka, Christian Barthlott, and Corinna Hoose

The predictability of deep moist convection is subject to large uncertainties, mainly due to inaccurate initial and boundary data, incomplete description of physical processes, or uncertainties in microphysical parameterizations. In this study we present results from a large 108-member ensemble focussing solely on the perturbation of microphysical uncertainties. We perturb the cloud condensation nuclei concentrations, the ice nucleating particle concentrations, the graupel sedimentation velocity as well as the width of the cloud droplet size distribution, all of which are not well constrained by observations. The model simulations are conducted with a convection-permitting configuration of the ICON model using a double-moment microphysics scheme. Results from four convective episodes during the Swabian MOSES field campaigns conducted in the summers of 2021 and 2023 show a large spread in convective precipitation in Germany. Based on convection-related parameters and microphysical process rates, the sensitivities of convection initiation, cloud and precipitation formation to the microphysical uncertainties are discussed. An important finding is e.g. the large sensitivity of hail formation on all analysed days. These results demonstrate the benefits of using an aerosol-aware double-moment microphysics scheme and that the use of microphysical uncertainties for ensemble modelling strategies can produce a sufficiently large ensemble spread for convective-scale predictability.

How to cite: Czajka, B., Barthlott, C., and Hoose, C.: Impact of microphysical perturbations on convective precipitation predictability, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17689, https://doi.org/10.5194/egusphere-egu24-17689, 2024.

EGU24-17871 | ECS | Posters on site | AS1.6

Accurate Representation of Dual-Polarized Radar Parameters with Data Assimilation 

Ji-Won Lee, Ki-Hong Min, and GyuWon Lee

To enhance the accuracy of heavy rainfall prediction, the assimilation of radar data (DA) is crucial. Single-polarized radar variables, such as reflectivity and Doppler velocity, offer insights into raindrop quantity and speed. Dual-polarization (dual-pol) radar variables, including differential reflectivity (ZDR), specific differential phase (KDP), and co-polar correlation coefficient (ρhv), provide additional details about hydrometeor phase, size, and liquid water content. Assimilating dual-pol radar variables into a Numerical Weather Prediction (NWP) model can enhance the accuracy of predicting both large-scale and rapidly developing mesoscale precipitation events. Therefore, the development and application of an accurate radar observation operator for DA, considering the microphysical information of an NWP model with dual-pol radar data, is necessary.

In this study, we developed a dual-pol radar operator based on microphysical variables such as the mixing ratio and total number concentration of hydrometeors. The enhanced method can accurately replicate the characteristics of dual-pol radar variables in the melting layer, improve the underestimation of hydrometeors mixing ratio for liquid and ice particles. Enhancing the estimation of hydrometeor increments further refines the prediction of mesoscale precipitation effects. This study aims to demonstrate improvements in microphysical processes and enhanced accuracy in rainfall predictions through dual-pol radar DA.

※ This work was supported by the National Research Foundation (NRF) grant funded by the Korea government (MSIT)(No. 2021R1A4A1032646, 2022R1A6A3A13073165) and the Korea Meteorological Administration Research and Development Program under Grant RS-2023-00237740.

How to cite: Lee, J.-W., Min, K.-H., and Lee, G.: Accurate Representation of Dual-Polarized Radar Parameters with Data Assimilation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17871, https://doi.org/10.5194/egusphere-egu24-17871, 2024.

EGU24-18115 | ECS | Posters on site | AS1.6

Influence of data assimilation on tropical waves 

Yvonne Ruckstuhl, Tijana Janjic, Hyunju Jung, Peter Knippertz, and Robert Redl

Precipitation forecasts in the tropics are poor due to large model and initial condition errors, leaving ample room for improvement. In particular, it has been hypothesized that the coupling of tropical waves and convection offers a source of predictability, suggesting that capturing these waves accurately in the model and in initial conditions could lead to improved precipitation forecasts. In this work, we investigate whether standard data assimilation (DA) algorithms like the Ensemble Kalman Filter (EnKF) are fundamentally capable of recovering tropical waves and thereby provide initial conditions that lead to skillful precipitation forecasts. To capture the essence of tropical dynamics without contamination of land-sea contrasts, sea-surface temperature gradients and influences from the extra-tropics, we use a tropical aqua channel configuration at 13km grid-spacing with the ICON numerical weather prediction model. Further, to isolate the role of the initial conditions provided by DA, we assume a perfect model. In our setup, Kelvin waves dominate over other wave types and primarily modulate precipitation.  In addition, there is evidence of a Madden-Julian-Oscillation (MJO)-like feature that appears coupled to large-scale convective activity. We show that when sufficient wind observations are assimilated, the DA can reduce the errors in the representation of the Kelvin waves sufficiently to provide accurate precipitation forecasts up to several weeks. Surprisingly, even the MJO-like rainfall event, which starts after a forecast lead time of 10 days, is captured by the forecast ensemble. Further, we find that accurate initial conditions for humidity are important to slow down error growth for all model variables. Like emphasized by several other studies, we conclude that wind observations are by far the most important input to achieve skillful tropical forecasts. A secondary challenge is to improve initial conditions of humidity by developing DA algorithms to account for non-Gaussian error statistics. Investigating the role of model error in DA and the resulting forecasts is left for future work. 

How to cite: Ruckstuhl, Y., Janjic, T., Jung, H., Knippertz, P., and Redl, R.: Influence of data assimilation on tropical waves, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18115, https://doi.org/10.5194/egusphere-egu24-18115, 2024.

EGU24-19982 | ECS | Orals | AS1.6

Assessing the Influence of the Shillong Plateau Topography on Thunderstorm Activities in North-east India 

Rajesh Kumar Sahu, Hylke E Beck, and Bhishma Tyagi

Northeast India (NEI) experiences frequent thunderstorms during the pre-monsoon season, which can be catastrophic, resulting in loss of life and damage to infrastructure and property. The Shillong Plateau (SP) has been identified as a key factor in triggering these thunderstorms over NEI. Our study focuses on monitoring changes in thermodynamic indicators over NEI to assess the impact of the SP on the initiation and propagation of thunderstorms. The results demonstrate a significant increase in thermodynamic index values across NEI when the SP topography is elevated, indicating an increase in thunderstorm activity. Conversely, when the SP topography is reduced, there is a decrease in these indicators, corresponding with lower thunderstorm intensity. Notably, a lower SP topography is associated with increased precipitation, whereas a higher SP topography is linked to decreased precipitation. These findings underscore the crucial role of SP topography in influencing pre-monsoon thunderstorms over NEI, which has implications for understanding and predicting regional weather patterns.

Keywords: Thunderstorms; Thermodynamic Indices; Topography; Shillong Plateau; WRF

How to cite: Sahu, R. K., Beck, H. E., and Tyagi, B.: Assessing the Influence of the Shillong Plateau Topography on Thunderstorm Activities in North-east India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19982, https://doi.org/10.5194/egusphere-egu24-19982, 2024.

EGU24-20211 | ECS | Posters on site | AS1.6

Does higher temperature accentuate convective cell clustering within European MCSs? 

Nicolas A. Da Silva and Jan O. Haerter

Mesoscale Convective Systems (MCSs) are clusters of thunderstorms composed of narrow and heavy convective-type precipitation adjacent with wider and lighter stratiform-type precipitation. MCSs are the largest contributor of extreme precipitation events over Europe (Da Silva & Haerter, 2023). 

While convective and stratiform-type precipitation contributions within MCSs are each expected to increase according to the Clausius-Clapeyron law (~7%°C-1), their statistical superimposition is shown to increase at a faster rate due to increased MCS convective fraction with temperature (Da Silva & Haerter, submitted). 

 

For better prediction of floods induced by MCSs, it is also important to characterize the relationship between temperature and the spatio-temporal clustering of convective cells within MCSs. For that purpose, we use the high resolution EUropean Cooperation for LIghtning Detection (EUCLID) lightning dataset and combine it with MCS tracking data (derived from the RADOLAN radar precipitation dataset; Bartels et al., 2004) over Germany. Identifying convective cells through lightning records, we measure the degree of convection clustering using an organization index which we adapt to the MCS geometry. In this process, we use a Monte Carlo method to estimate the reference random distribution of nearest neighbor distances of convective centroids. 

 

We associate our organization index with surface dew-point temperatures from neighboring weather stations from the German Weather Service (Deutscher Wetterdienst, DWD). We select the temperature upstream of the MCS tracks, as a proxy of the moisture source involved in the formation of MCS precipitation. Idealized simulations suggest that both the mean and the spatial variability of surface temperature could be relevant for convective aggregation (Pendergrass, 2020; Shamekh et al., 2020). Our study considers both and also investigates the potential role of other triggers for convective aggregation such as convective cold pools (Haerter, 2019) or the diurnal cycle (Haerter et al., 2020).




References:

 

Bartels, H. et al. Projekt RADOLAN Routineverfahren zur Online-Aneichung der Radarniederschlagsdaten mit Hilfe von automatischen Bodenniederschlagsstationen (Ombrometer) (2004).

https://www.dwd.de/DE/leistungen/radolan/radolan_info/abschlussbericht_pdf.pdf?__blob=publicationFile&v=2

 

Da Silva, N. A., & Haerter, J. O. (2023). The precipitation characteristics of mesoscale convective systems over Europe. Journal of Geophysical Research: Atmospheres, 128, e2023JD039045. https://doi.org/10.1029/2023JD039045

 

Da Silva, N. A, & Haerter J. O.. Non super-Clausius-Clapeyron scaling of convective precipitation extremes, 08 January 2024, PREPRINT (Version 1) available at Research Square

https://doi.org/10.21203/rs.3.rs-3777860/v1

 

Haerter, J. O. (2019). Convective self-aggregation as a cold pool-driven critical phenomenon. Geophysical Research Letters, 46, 4017–4028. https://doi.org/10.1029/2018GL081817

 

Haerter, J.O., Meyer, B. & Nissen, S.B. Diurnal self-aggregation (2020). npj Clim Atmos Sci 3, 30. https://doi.org/10.1038/s41612-020-00132-z


Pendergrass, A. G. (2020). Changing degree of convective organization as a mechanism for dynamic changes in extreme precipitation. Current climate change reports, 6, 47-54.

 

Shamekh, S., C. Muller, J. Duvel, and F. D’Andrea (2020), How Do Ocean Warm Anomalies Favor the Aggregation of Deep Convective Clouds?. J. Atmos. Sci., 77, 3733–3745, https://doi.org/10.1175/JAS-D-18-0369.1.

How to cite: Da Silva, N. A. and Haerter, J. O.: Does higher temperature accentuate convective cell clustering within European MCSs?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20211, https://doi.org/10.5194/egusphere-egu24-20211, 2024.

EGU24-20250 | Posters on site | AS1.6

Probabilistic nowcasting of severe storms in Africa: workflow and online tools for monitoring 

Cornelia Klein, Seonaid Anderson, Steven Cole, Christopher Taylor, Steven Wells, Gemma Nash, and Abdoulahat Diop

Mesoscale convective systems (MCSs) dominate rainfall and its extremes in most parts of West Africa, frequently producing flash floods that result in major damage and loss of life. As West African storms are already intensifying, these effects are expected to become more frequent and severe under climate-change and rapid urban expansion. To help mitigate these impacts, the NFLICS (Nowcasting FLood Impacts of Convective storms in the Sahel) project has co-developed a prototype nowcasting system with West African meteorological services based on conditioned climatologies of organised convection as seen from the Meteosat Second Generation (MSG) satellites since 2004.  Data on historical convective activity, conditioned on the present location and timing of observed convection, are used to produce probabilistic forecasts of convective activity out to six hours ahead. Verification against the convective activity analysis and the 24-hour raingauge accumulations over Dakar suggests that these probabilistic nowcasts provide useful information on the occurrence of convective activity. The highest skill (compared to nowcasts based solely on climatology) is obtained when the probability of convection is estimated over spatial scales between 100 and 200km, depending on the forecast lead-time considered. Furthermore, recent advances have included incorporation of land surface temperature anomalies to modify nowcast probabilities – this recognises that MCS evolution favour drier land. We present the workflow of this nowcasting system and discuss our current understanding of the land surface effects that play a role for storm development and prediction. The developed nowcasting system is crucially computationally inexpensive to run operationally and achieves skill in the absence of rainfall radar, as is the case over most of Africa. Operational trials over the 2020 and 2021 rainy seasons, and during intensive nowcasting testbeds with researchers and forecasters, has shown the utility of these new nowcast products to support Impact-based Forecasting, and are currently being extended for use during a testbed with meteorological services in southern Africa in 2024.

Latest West Africa nowcasts alongside pan-African cloud and surface state imagery are publicy accessible on https://eip.ceh.ac.uk/hydrology/sub-saharan-africa/nowcasting

How to cite: Klein, C., Anderson, S., Cole, S., Taylor, C., Wells, S., Nash, G., and Diop, A.: Probabilistic nowcasting of severe storms in Africa: workflow and online tools for monitoring, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20250, https://doi.org/10.5194/egusphere-egu24-20250, 2024.

EGU24-981 | ECS | Posters on site | AS1.7

Low clouds over the subtropical Indian Ocean in the Mascarene High environment and sub-seasonal circulation associations with the Indian summer monsoon 

Gokul Tamilselvam, Ramesh Vellore, Ayantika Dey Choudhury, Divya Viswanath, Krishnan Raghavan, and Reji Mariya Joy Kooran

This study investigates anomalous low cloud fractions (LCFs) in the Mascarene High(MH) environment of subtropical Indian Ocean (SIO) during June-September, and their sub-seasonal (10-90 day) circulation changes in the SIO and associated variations of the Indian summer monsoon (ISM) using observations and ERA5 circulation products based on 1999-2014 period. Periods of anomalous excess and deficits in LCFs in the SIO clearly reveal different sub-seasonal circulation attributes across the equator with precursor signals to the strength of ISM. Anomalous circulation composites from the excess LCF periods shows mean sea level pressure (MSLP) enhancements of about 2 hPa in the MH region in correspondence with increasing areal extent and intensifications in LCFs, and a net increase in low-level southerly momentum between MH and monsoon trough (MT) environments. The MSLP reinforcements in the MH are clearly demonstrated to emerge from the strength of cloud-top radiative cooling and associated winds and mass adjustments. The 10-20 [30 -50] day modes of the circulation in the SIO further elucidates zonally propagating [quasi-stationary] manifestations on MH reinforcements. There is an increase in meridional transport of moisture fluxes, by about 7 times relative to deficit LCF periods, channelled through a
conduit region (15-30°S, 60-90°E) juxtaposing the cross-equatorial circulation (CEC) from both western and eastern sides of the Indian Ocean. This occurs in tandem with a zone of moisture flux convergence in the ISM region advancing poleward towards the climatological MT region - implying that excess LCF periods portend the likelihood of stronger ISM. Deficit LCF periods, on the contrary, show a mirrored scenario of the above with a net northerly low-level wind anomalies between MH and MT, pressure deficits in the MH region, and also portend the likelihood of weaker ISM. Low clouds
in the SIO are not only instrumental for MH stability, but also essential for circulation and moisture support across the equator and the signals for the strength of ISM on sub-seasonal scale.

How to cite: Tamilselvam, G., Vellore, R., Choudhury, A. D., Viswanath, D., Raghavan, K., and Kooran, R. M. J.: Low clouds over the subtropical Indian Ocean in the Mascarene High environment and sub-seasonal circulation associations with the Indian summer monsoon, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-981, https://doi.org/10.5194/egusphere-egu24-981, 2024.

EGU24-1219 | Orals | AS1.7

Mesoscale Convective Systems in DYAMOND Models: A Feature Tracking Intercomparison. 

Zhe Feng, Ruby Leung, Andreas Prein, Thomas Fiolleau, William Jones, Zachary Moon, Ben Maybee, Fengfei Song, Jinyan Song, Kelly Núñez Ocasio, Cornelia Klein, Adam Varble, Remy Roca, and Puxi Li

The DYAMOND project (Stevens et al. 2019) provides an intercomparison framework for state-of-the-art global convection-permitting models with km-scale horizontal grid spacing that can directly simulate convective storms. We recently assessed the fidelity of the convective storms simulated by DYAMOND models using a novel feature tracking technique (Feng et al. 2023) and found a surprisingly large inter-model spread in the simulated frequency of ordinary deep convection and mesoscale convective systems (MCSs), as well as their associated precipitation. Recent works also showed that different feature tracking algorithms have significant impacts on estimating MCS characteristics including frequency, size, lifetime and precipitation (Prein et al. 2023). To further investigate how feature tracking methods affect the evaluation of global MCS simulations and our understanding of convective organization in observations and DYAMOND simulations, we are organizing a new international initiative called MCSMIP (MCS tracking Method Intercomparison Project). Preliminary results from several different feature trackers show that DYAMOND models generally underestimate observed MCS precipitation amount and their contribution to total precipitation in the tropics (Fig. 1), and the simulated MCS precipitation is too intense. However, some models have notable differences in MCS frequency and characteristics among the trackers. Potential paths towards more process-oriented model diagnostics to better understand the differences in simulated MCS and precipitation characteristics will be discussed.

Figure 1. (a) Observed MCS contribution to total precipitation during DYAMOND Phase II, (b) model relative mean difference (%) from observations in the tropics. Each group of bars in (b) is from a feature tracker: PyFLEXTRKR, MOAAP, TOOCAN, tobac, TAMS, and simpleTrack, and each bar denotes a DYAMOND model.

References

Feng, Z. et al. (2023). Mesoscale Convective Systems in DYAMOND Global Convection-Permitting Simulations. Geophys. Res. Lett., doi: 10.1029/2022GL102603.

Prein, A. et al. (2023). Km-Scale Simulations of Mesoscale Convective Systems (MCSs) Over South America – A Feature Tracker Intercomparison. DOI: 10.22541/essoar.169841723.36785590/v1.

How to cite: Feng, Z., Leung, R., Prein, A., Fiolleau, T., Jones, W., Moon, Z., Maybee, B., Song, F., Song, J., Núñez Ocasio, K., Klein, C., Varble, A., Roca, R., and Li, P.: Mesoscale Convective Systems in DYAMOND Models: A Feature Tracking Intercomparison., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1219, https://doi.org/10.5194/egusphere-egu24-1219, 2024.

EGU24-1281 | ECS | Orals | AS1.7

Cold Pools in the Trades: External Drivers and Self-Organization Impact 

Pouriya Alinaghi, Martin Janssens, Fredrik Jansson, A. Pier Siebesma, and Franziska Glassmeier

Recent observations of the trades highlight the covariability between cold pool (CP) properties and cloud cover, suggesting a potential impact of CPs on the cloud radiative effect (CRE). To explore this, we use an ensemble of 103 large-domain, high-resolution, large-eddy simulations (Cloud Botany). We investigate the extent to which the variability in CPs is driven by external conditions or convective self-organization. Our findings show that CPs are notably controlled by large-scale conditions, specifically (horizontal) wind speed and subsidence. The temporal evolution of CPs is tightly related to the diurnality in radiation. To understand the extent to which CPs vary with self-organization, we switch off the diurnality in radiation. Despite the absence of the diurnal cycle, CP time series still exhibit fluctuations. These fluctuations result from the recharge-discharge of thermodynamic and dynamic properties of the sub-cloud layer owing to CP-cloud interactions. Our results demonstrate that circulations induced by CPs reinforce the parent clouds, resulting in deepening and scale growth, followed by mesoscale arcs enclosing clear-sky areas. Finally, we show that CPs influence CRE, but only when they exist during the day. Our findings emphasize the importance of the relationship between the timescales of self-organization and the diurnal cycle of external conditions, greatly influencing the CRE dependency on self-organizing CPs.

How to cite: Alinaghi, P., Janssens, M., Jansson, F., Siebesma, A. P., and Glassmeier, F.: Cold Pools in the Trades: External Drivers and Self-Organization Impact, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1281, https://doi.org/10.5194/egusphere-egu24-1281, 2024.

EGU24-1848 | Posters on site | AS1.7

Do observations support ideas behind common mass flux closures? 

Raphaela Vogel and Juan Pedro Mellado

Determining the mass flux at cloud base is the principle closure needed in convective parameterizations. Here we evaluate if observations from the EUREC4A field campaign support ideas behind common shallow-convective mass flux closures. All parameters of the closures are diagnosed at the mesoscale (200km, 3h) from dropsonde data and turbulence measurements. The closure models are compared to a reference mass flux estimated as a residual of the sub-cloud layer mass budget from the same circular dropsonde arrays. We find that a closure using the subcloud convective velocity scale (w*) captures the magnitude but underestimates the variability of the reference mass flux. A closure using a  turbulence kinetic energy (TKE) based velocity scale instead explains 78% of mass flux variability. These results suggest that (1) the full TKE needs to be considered rather than just the convective contribution represented by w*, and (2) the TKE may contain information about the area fraction of thermals, which makes a separate cloud area fraction scale unnecessary to explain mass flux variability during EUREC4A.

How to cite: Vogel, R. and Mellado, J. P.: Do observations support ideas behind common mass flux closures?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1848, https://doi.org/10.5194/egusphere-egu24-1848, 2024.

EGU24-2105 | Orals | AS1.7

A passive tracer perspective on the origin and evolution of tropical cirrus clouds  

Blaž Gasparini, Peter N. Blossey, Aiko Voigt, Rachel Atlas, and Martina Krämer

The processes controlling tropical cirrus clouds are poorly understood, contributing to significant uncertainty in estimating how clouds respond to global warming. Much of this uncertainty stems from a lack of knowledge about the cirrus life cycle. Not knowing how cirrus clouds evolve also makes it hard to determine the fraction of clouds that comes from deep convective outflow compared to those formed by in situ ice nucleation at temperatures colder than -40°C. These two types of clouds are controlled by different processes that may operate differently in a warmer climate, making it even more important to assess their origin.

We implement passive tracers in the cloud-resolving model SAM used in a tropical channel setup to track the 3D evolution of cloudy parcels from two different perspectives:

  • A detrainment perspective, useful for tracking the evolution of anvil clouds.
  • An ice nucleation perspective, useful for following the evolution of in situ cirrus.

Using the detrainment tracer, we can accurately determine how long it's been since an air parcel left a deep convective plume. Our analysis shows that freshly detrained air parcels consist mainly of many large ice crystals with radii of 30-80 μm. These quickly fall out of the atmosphere, resulting in aged anvils containing fewer and smaller ice crystals.

The ice nucleation tracer tracks the time after the onset of ice nucleation. This proves valuable for studying the evolution pathways of in situ cirrus ice crystals. Initially, small, freshly nucleated in situ cirrus mostly contain 20-200 ice crystals/liter, occasionally spiking due to relatively rare homogeneous nucleation events. However, the number of ice crystals decreases rapidly, likely because of sublimation, leading to concentrations of < 10/liter in aged clouds.

Tracers also help us understand the climatology of cirrus formation. On average, we find that in situ cirrus account for 20% (at T>-50°C) to 70% (at T<-70°C) of all tropical cirrus.

While tracers cannot follow individual cloud parcels and different realizations of the tropical atmosphere in global models and other idealized frameworks may affect their behavior and interpretation somewhat, our research shows that they can provide valuable insights into cloud evolution and microphysics. They also have the potential to improve our mechanistic understanding of how tropical cirrus respond to global warming.

How to cite: Gasparini, B., Blossey, P. N., Voigt, A., Atlas, R., and Krämer, M.: A passive tracer perspective on the origin and evolution of tropical cirrus clouds , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2105, https://doi.org/10.5194/egusphere-egu24-2105, 2024.

EGU24-2919 | Orals | AS1.7

The role of cloud-cloud interactions and entrainment-mixing in the lifecycle of shallow cumulus clouds 

Jingyi Chen, Samson Hagos, Zhe Feng, Heng Xiao, Jerome Fast, Chunsong Lu, and Adam Varble

Limited understanding of the key factors that govern the lifecycle of cumulus clouds, including the interactions among clouds and with surrounding environments, contributes to climate prediction uncertainty. To investigate these processes, we tracked the lifecycle of thousands of individual shallow cumulus clouds within a large-eddy simulation during the Holistic Interactions of Shallow Clouds, Aerosols, and Land-Ecosystems (HISCALE) field campaign in the U.S. Southern Great Plains.

Our examination of these clouds followed two paths. First, we compared two distinct groups of clouds—those with growing cloud neighbors and those with decaying cloud neighbors. Clouds with growing neighbors were found to form over areas with larger surface heterogeneity than clouds with decaying neighbors. Clouds with growing neighbors also had less instability, less moisture and warmer air below cloud base than decaying neighbor clouds. This suggests that evaporation below the cloud base likely occurs before the formation of these clouds with decaying neighbor clouds due to the colder and moister air below cloud base. Larger instability leads to higher vertical velocity and convergence within the cloud, which causes stronger downdrafts and water vapor removal in the surrounding area. The latter appears to be the reason for the decaying neighboring clouds.

Second, we introduced two new metrics to assess the relationships between cloud shape and these processes: one reflecting the irregularity of cloud edges and another emphasizing the cloud horizontal aspect ratio. During the lifecycle of simulated cumulus clouds, cloud edge irregularity increased with minimal changes in aspect ratio. Irregularity-driven growth of the cloud perimeter was a strong indicator of cloud splitting, more so than growth driven by aspect ratio changes. Additionally, clouds with more irregular edges exhibited smaller gradients of properties at their boundaries, suggesting more intense mixing with the surrounding cloud-free environment.

These results advance insights into the interactions between cumulus clouds and their nearby environment entrainment that influence the evolution of cloud populations. Such knowledge can support the development of more accurate shallow cumulus parameterizations in the new generation of climate models.

How to cite: Chen, J., Hagos, S., Feng, Z., Xiao, H., Fast, J., Lu, C., and Varble, A.: The role of cloud-cloud interactions and entrainment-mixing in the lifecycle of shallow cumulus clouds, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2919, https://doi.org/10.5194/egusphere-egu24-2919, 2024.

EGU24-3192 | Posters on site | AS1.7

Do optically denser trade-wind cumuli live longer? 

Torsten Seelig, Felix Müller, and Matthias Tesche

We present a detailed investigation of the lifetime of Caribbean trade-wind cumulus clouds and the temporal evolution of their physical properties based on geostationary observations with the Advanced Baseline Imager (Schmit et al., 2017) aboard the GOES-16 satellite during the “ElUcidating the RolE of Cloud-Circulation Coupling in ClimAte” (EUREC⁴A; Stevens et al., 2021) field experiment in winter 2020. A first application of our upgraded cloud-tracking methodology (Seelig et al., 2021) to measurements with a spatio-temporal resolution of 2 × 2 km² and 1 min, respectively, enables the investigation of processes that control the lifetime of shallow marine cumulus clouds. Our analysis reveals that shallow marine cumulus clouds live longer when they span over a surface area that exceeds an order of tens of square kilometers. While these clouds show similar median cloud droplet size and number concentration compared to shorter-lived clouds, they contain more liquid water and, thus, show a cloud optical depth that is increased by about one third. Besides the effect of cloud optical depth, we find that the scale of the atmospheric motions with which the clouds interact is also critical to their lifetime.

References:

Schmit, T. J., Griffith, P., Gunshor, M. M., Daniels, J. M., Goodman, S. J., and Lebair, W. J.: A Closer Look at the ABI on the GOES-R Series, B. Am. Meteorol. Soc., 98, 681-698, https://doi.org/10.1175/BAMS-D-15-00230.1, 2017.

Stevens, et al.: EUREC4A, Earth Syst. Sci. Data, 13, 4067-4119, https://doi.org/10.5194/essd-13-4067-2021, 2021.

Seelig, T., Deneke, H., Quaas, J., and Tesche, M.: Life cycle of shallow marine cumulus clouds from geostationary satellite observations, J. Geophys. Res.: Atmos., 126(22), e2021JD035577, https://doi.org/10.1029/2021JD035577, 2021.

How to cite: Seelig, T., Müller, F., and Tesche, M.: Do optically denser trade-wind cumuli live longer?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3192, https://doi.org/10.5194/egusphere-egu24-3192, 2024.

EGU24-5369 | Posters on site | AS1.7

Reducing biases in low cloud cover over the tropical Atlantic in the Norwegian Earth System Model 

Richard Davy, Tarkeshwar Singh, Lingling Suo, and Francois Counillon

Biases in the representation of low cloud cover in climate models has been identified as one of the leading causes of uncertainty in equilibrium climate sensitivity. It is therfore crucial to reduce current climate model biases in low cloud cover in order to reduce uncertainty in projected climate change. We have conducted perturbed parameter simulations to assess the sensitivity of the simulated low cloud cover in the Norwegian Earth System Model to parameters within the CLUBB scheme. The CLUBB scheme unifies the atmospheric boundary layer turbulence scheme with the clouds schemes and so has the potential advantage of reducing inconsistencies between these components of the atmosphere. However, there are many parameters within the CLUBB scheme that are not well constrained and have unknown effects on simulated climate. We demonstrate that of the 12 parameters in the CLUBB scheme selected for perturbed-parameter experiments, there are just 2 which control the low cloud cover in the model. We used a combination of multi-linear regression models and offline data assimilation with parameter estimation to identify the optimum values for these two parameters to eliminate the bias in low cloud cover, and confirmed this through a second iteration of perturbed-parameter experiments.

How to cite: Davy, R., Singh, T., Suo, L., and Counillon, F.: Reducing biases in low cloud cover over the tropical Atlantic in the Norwegian Earth System Model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5369, https://doi.org/10.5194/egusphere-egu24-5369, 2024.

EGU24-6203 | Orals | AS1.7

Coupled Mesoscale to Microscale Simulations of Mixed-Phase Convective Clouds Observed during the Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) 

Branko Kosovic, Timothy Juliano, Lulin xue, Bart Geerts, Christian Lackner, and Nathaniel Abrokwah Oteng

Equatorward excursions of cold polar air masses during cold air outbreaks (CAOs) result in the development of mesoscale convective circulations that significantly affect surface fluxes. Air masses undergo intense transformations as they transition from the ice to the warmer ocean.  Initially strong surface heat fluxes and strong shear result in the formation of helical convective rolls and associated cloud streets that can extend for hundreds of kilometers. Further downwind helical convective rolls evolve into convective cells forming open cell clouds.

We study an intense CAO observed on 13 March 2020 during Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) [1]. COMBLE deployed the Department of Energy Atmospheric Radiation Measurement (ARM) Mobile Facility 1 (AMF1) at Andenes, Norway to observe a range of CAO conditions. We simulate the evolution of a CAO using coupled mesoscale to microscale simulations with the Weather Research and Forecasting (WRF) model. The coupled simulation using WRF includes a mesoscale domain with 1050 m horizontal grid cell coupled online with a cloud-resolving LES domain with horizontal grid cell size of 150 m that stretches through the full ~1000 km extent of a CAO, from the ice edge to Andenes. Within the cloud-resolving domain are nested two LES domains with 30 m grid cells. One of these domains is focused on the region of convective rolls while the other one is focused on convective cells. This configuration enables us to study the transformation of airmass at high resolution, providing unprecedented insight into the mixed phase cloud (MPC) transition from rolls to cells. We study the interaction between large-scale forcing, surface fluxes, radiative transfer, and cloud processes in the formation and evolution of mesoscale organization and MPCs. As part of this effort, we utilize the Cloud Resolving Model Radar Simulator (CR-SIM) to compare WRF more directly to the measurements. Our CR-SIM analysis suggests that convective cell structures and properties are well modeled at the AMF1 site when using turbulence-resolving resolutions.

How to cite: Kosovic, B., Juliano, T., xue, L., Geerts, B., Lackner, C., and Abrokwah Oteng, N.: Coupled Mesoscale to Microscale Simulations of Mixed-Phase Convective Clouds Observed during the Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6203, https://doi.org/10.5194/egusphere-egu24-6203, 2024.

EGU24-6252 | ECS | Orals | AS1.7

Quantifying cloud microphysical uncertainties in an extratropical cyclone’s ascending airstream using Lagrangian diagnostics 

Annika Oertel, Annette K. Miltenberger, Christian M. Grams, and Corinna Hoose

The characteristic large-scale and strongly precipitating cloud band in extratropical cyclones is associated with the so-called warm conveyor belt (WCB), which is a coherent cyclone-relative airstream that ascends cross-isentropically from the boundary layer into the upper troposphere. Cloud microphysical processes along this ascending airstream determine the total diabatic heating, cloud structure, and associated surface precipitation characteristics.

We disentangle uncertainty related to the representation of cloud microphysical processes in the two-moment microphysics scheme of the ICOsahedral Nonhydrostatic (ICON) modeling framework in a convection-permitting simulation setup for an extratropical cyclone case study in the North Atlantic. To quantify uncertainty, we employ a perturbed parameter ensemble (PPE) approach, whereby five selected uncertain parameters in the cloud microphysics scheme and environmental conditions relevant for cloud formation are perturbed simultaneously and systematically. Specifically, cloud microphysical uncertainty is quantified along Lagrangian WCB trajectories which are calculated online during the ICON simulations from the resolved 3D wind fields at every model time step for each of the 70 PPE members. The Lagrangian perspective not only facilitates the characterisation of the airstream’s ascent behaviour but also provides detailed insight in cloud and precipitation formation along the ascent. The application of the Lagrangian diagnostics to all PPE members enables the quantification of dominant contributions of uncertainty from the perturbed parameters for WCB ascent characteristics, such as ascent timescales and tracks, as well as for precipitation formation along the ascent.

For example, we show that the precipitation efficiency along the ascending airstream is most strongly influenced by cloud condensation nuclei (CCN) concentrations modifying the cloud droplet to rain drop conversion. Moreover, a trajectory-based airstream-relative composite analysis shows that increased CCN concentrations result in a downstream shift of the surface precipitation relative to the eastward propagating airstream as the precipitation efficiency is reduced. In addition, the Lagrangian diagnostics can illustrate the feedback between diabatic heating from cloud microphysical processes in the mixed-phase and local vertical velocity. In this contribution we present our analysis framework and show how the perturbed parameters influence various Lagrangian diagnostics for WCB ascent and associated cloud and precipitation formation.

How to cite: Oertel, A., Miltenberger, A. K., Grams, C. M., and Hoose, C.: Quantifying cloud microphysical uncertainties in an extratropical cyclone’s ascending airstream using Lagrangian diagnostics, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6252, https://doi.org/10.5194/egusphere-egu24-6252, 2024.

EGU24-7261 | ECS | Posters on site | AS1.7

Wave-Convection Interactions Amplify Convective Parameterization Biases in the South Pacific Convergence Zone 

Yuanrui Chen, Wenchao Chu, Jonathon Wright, and Yanluan Lin

Climate models have long struggled to realistically simulate the South Pacific Convergence Zone (SPCZ) and its variability. For example, the default Zhang-McFarlane (ZM) convection in the Community Atmosphere Model version 5 (CAM5) produces too much light precipitation and too little heavy precipitation in the SPCZ, with this bias even more pronounced in the SPCZ region than in the broader tropics. In this presentation, we show that implementing a recently developed convection scheme in the CAM5 yields significant improvements in the simulated SPCZ during austral summer and describe the main reasons behind these improvements. In addition to intensifying both mean rainfall and its variability in the SPCZ, the new scheme produces a larger heavy rainfall fraction that is more consistent with observations and a state-of-the-art reanalysis. This shift toward heavier, more variable rainfall amounts is linked to increases in both the magnitude and altitude of diabatic heating associated with convective precipitation, thereby intensifying lower tropospheric convergence along the SPCZ axis and increasing the extent to which convection influences the upper-level circulation. Increased diabatic production of potential vorticity in the upper troposphere increases the distortion effect exerted by convection on transient Rossby waves passing through the SPCZ region. The much weaker distortion effects in simulations using the ZM scheme mean that waves are more likely to propagate continuously through the region rather than dissipate locally, thereby reducing updrafts and weakening convection within the SPCZ. Our results outline a dynamical framework for evaluating model representations of tropical-extratropical interactions within the SPCZ region and clarify why convective parameterizations that produce a more realistic top-heavy profile of deep convective heating are beneficial to representing the SPCZ and its variability.

How to cite: Chen, Y., Chu, W., Wright, J., and Lin, Y.: Wave-Convection Interactions Amplify Convective Parameterization Biases in the South Pacific Convergence Zone, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7261, https://doi.org/10.5194/egusphere-egu24-7261, 2024.

EGU24-8708 | ECS | Orals | AS1.7

The role of parametrized shallow convection in tropical cloud systems 

Alessandro Savazzi, Louise Nuijens, Wim de Rooy, and Pier Siebesma

In current storm-resolving models, the parameterization of shallow cumulus convection is based on the mass-flux framework, originally tailored for coarse mesh sizes O(10-50km). Recent finer grids present a unique opportunity to study the coupling between clouds, convection, and the large-scale circulations. This finer resolution also prompts a critical inquiry into the role of shallow convection parameterization (SCP). Within the context of EUREC4A-MIP, we use HARMONIE-AROME with a grid spacing of 2.5 km to test the mesoscale cloud sensitivity to SCP. While cloud patterns are discernible at this resolution, individual shallow cumuli may not be fully resolved. Our investigation reveals that mesoscale properties of tropical shallow cumulus fields and associated circulations exhibit a pronounced dependence on sub-grid parametrization, with differences in cloud cover up to 20%. We simulate the period from January 1st to February 28th 2020, and compare three configurations of HARMONIE-AROME: 1) the control with active SCP, 2) UVmix-off without momentum mixing by shallow convection, 3) SCP-off without any mixing by shallow convection. Instead of an incremental effect, our results show that UVmix-off and SCP-off can produce opposite responses in the cloud field. UVmix-off produces large anvils, less precipitation, and a cooler lower-troposphere. In contrast, SCP-off produces many smaller clouds which precipitate more in a warmer lower-troposphere due to a more unstable environment, and the buildup of CAPE and turbulent kinetic energy. Importantly, our results underscore that the removal of SCP (and to a lesser extent, the removal of UV mixing) strengthens mesoscale circulations and augments their coverage through increased wind variance, predominantly at scales larger than 25 km. Stronger resolved vertical motion in SCP-off produces stronger circulations, whereas the altered wind mixing in UVmix-off primarily affects the coverage of circulations. The ability to look at parameterized tendencies provides further insight into where the convection is strengthening or weakening the winds. This nuanced exploration contributes valuable insights into the intricate dynamics of mesoscale cloud systems under varying shallow convective parameterizations.

How to cite: Savazzi, A., Nuijens, L., de Rooy, W., and Siebesma, P.: The role of parametrized shallow convection in tropical cloud systems, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8708, https://doi.org/10.5194/egusphere-egu24-8708, 2024.

EGU24-8740 | Orals | AS1.7

The Max Planck Cloud Kite 

Eberhard Bodenschatz, Mohsen Bagheri, Hossein Khodamoradi, Artur Kupitzek, Freja Nordsiek, Constantin Schettler, and Birte Thiede

Most of the Earth's atmosphere is covered with clouds, which significantly affect incoming and outgoing radiation and thus the Earth's energy balance. Clouds are a source of considerable uncertainty in weather and climate models. Their size ranges from submillimeters, where cloud microphysics is important, to hundreds of kilometers, where they affect weather and climate. The complex coupling of cloud and turbulent flow dynamics at these scales makes clouds difficult to understand. In addition, several long-standing important puzzles, such as the existence and/or presence of cloud holes (regions without droplets) and the sharpness of cloud boundaries, remain unsolved. Given the high Reynolds numbers in atmospheric clouds (Re~10^6-10^9), laboratory-generated flows (with few exceptions) and direct numerical simulations are not yet capable of achieving cloud-like flow dynamics. Therefore, field studies and, in particular, airborne measurements performed far from the Earth's topographic influences can approach the correct range of parameter space relevant to naturally occurring clouds.
We have developed the Max Planck CloudKite, which consists of a balloon-kite aerostat and a suite of scientific instruments for simultaneous measurements of aerosols and turbulence features in the atmospheric boundary layer and in clouds. Cloudkite is an independent platform capable of characterizing the atmospheric boundary layer and low-lying clouds within the boundary layer (<2 km) at almost any location on Earth. It has been successfully deployed in remote regions of the Atlantic aboard research vessels and also in northern Finland within the Arctic Circle. The cloud-resolving probe is equipped with Particle Image/Tracking Velocimetry (PIV/PTV), Inline Holographic Particle Imaging, Fast Cloud Droplet Probe (FCDP), multi-hole pitot tubes, and humidity, temperature and pressure sensors. In addition, 10 WinDart units, including aerosol spectrometers and 3D ultrasonic sensors, are installed on the tether to fully characterize the atmospheric boundary layer and clouds simultaneously. Overall, the results will greatly improve our understanding of cloud evolution and spatial structure, as well as cloud-aerosol interactions, which is urgently needed to address climate change challenges.

How to cite: Bodenschatz, E., Bagheri, M., Khodamoradi, H., Kupitzek, A., Nordsiek, F., Schettler, C., and Thiede, B.: The Max Planck Cloud Kite, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8740, https://doi.org/10.5194/egusphere-egu24-8740, 2024.

EGU24-8742 | Posters on site | AS1.7

Resolving shallow cumulus clouds: insights from high-resolution airborne measurements  

Gholamhossein Bagheri, Birte Thiede, Oliver Schlenczek, Freja Nordsiek, and Eberhard Bodenschatz

During the EUREC4A field campaign over the Atlantic Ocean near Barbados, we flew the Max Planck CloudKite aboard the German research vessel Maria S. Merian. In addition to three-dimensional wind speed, temperature, and humidity data, the scientific payload aboard CloudKite captured about one million holograms and half a million particle-image-velocimetry images, primarily in shallow cumulus clouds. The collected data allow us to capture the droplet size distribution and turbulence features with unprecedented resolution, thanks to the fast acquisition rate of the instruments combined with the low true air speed of the tethered CloudKite aerostat. We found that the clouds exhibit extreme variations in droplet size distribution both near the edge and in the core. The cloud droplets also exhibit clusters and empty regions, especially near the cloud edge.

How to cite: Bagheri, G., Thiede, B., Schlenczek, O., Nordsiek, F., and Bodenschatz, E.: Resolving shallow cumulus clouds: insights from high-resolution airborne measurements , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8742, https://doi.org/10.5194/egusphere-egu24-8742, 2024.

EGU24-9104 | ECS | Orals | AS1.7

The puzzle of shallow convection-circulation coupling 

Martin Janssens

Since shallow clouds over the tropical oceans are organised into mesoscale structures, explaining the role of these clouds in climate requires understanding what governs mesoscale patterns in shallow cumulus convection. Many puzzle pieces have emerged in recent years. These include both external forcings on the boundary layer, such as the import of extratropical eddies and water vapour with the large-scale flow, weak sea-surface temperature anomalies and remotely triggered gravity waves, as well as internal feedbacks between convection and its mesoscale environment, such as cold pool dynamics and self-reinforcing low-level moisture convergence. What all these mechanisms share, is their interaction with the low-level mesoscale vertical motion field, which itself is often organised into shallow circulations that couple tightly to the convection. Here, we will therefore propose to begin assembling the puzzle pieces by analysing the origins of shallow circulations, in a conceptual framework of weak mesoscale virtual temperature gradients. The analysis is enabled by the simultaneous presence of observations of clouds, thermodynamics and mesoscale vertical motion taken during the EUREC4A field campaign, and simulations from EUREC4A-MIP; it will therefore serve as an example of what other puzzle pieces might fall in place by combining observations with other intercomparison projects in the model hierarchy, such as Lagrangian LES MIP, CP-MIP and NextGEMS.

How to cite: Janssens, M.: The puzzle of shallow convection-circulation coupling, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9104, https://doi.org/10.5194/egusphere-egu24-9104, 2024.

EGU24-9151 | Posters on site | AS1.7

Lagrangian analysis of ice supersaturated air masses in connection with low level fronts of extratropical cyclones 

Philipp Reutter, Stefan Niebler, Annette Miltenberger, and Peter Spichtinger

Ice supersaturation is often found in the upper troposphere. The so-called ice supersaturated regions (ISSRs), i.e. air masses in the status of supersaturation with respect to ice, are formation regions of in-situ cirrus clouds. While an ISSR alone has a rather small effect on the radiation budget, this changes significantly when cirrus clouds develop within the ISSR. Hence, the transition from an ISSR to a cirrus cloud has important implications. In order to understand how ISSR and the embedded in-situ cirrus clouds form and develop, the transport pathways of water vapour have to be understood.

Therefore, to better understand the life cycle of extratropical ice-supersaturated regions (ISSRs), we utilize backward and forward trajectories initiated within ISSRs and analyze them. Furthermore, we connect these trajectories with information about the location of low-level frontal systems to investigate connections between ISSRs and extratropical cyclones. Particularly interesting is the relative position to the so-called warm conveyor belt (WCB) trajectories.

 

How to cite: Reutter, P., Niebler, S., Miltenberger, A., and Spichtinger, P.: Lagrangian analysis of ice supersaturated air masses in connection with low level fronts of extratropical cyclones, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9151, https://doi.org/10.5194/egusphere-egu24-9151, 2024.

EGU24-9182 | Orals | AS1.7

Links between subtropical high-pressure systems and stratocumulus clouds variation 

Hairu Ding, Bjorn Stevens, and Hauke Schmidt

Stratocumulus clouds contribute significantly to the global energy budget as they are the Earth’s predominant cloud type and contribute strongly to Earth’s albedo. They are known to predominate in the subtropics, especially on the eastern edge of the subtropical highs. Previous studies have confirmed the importance of these highs for stratocumulus clouds, but how much it varies can influence the cloudiness hasn’t been quantified, yet. Our study investigates this relation for both the annual cycle and deseasonalized time series for the five major subtropical high-pressure regions. It has been shown that the estimated cloud top entrainment index (ECTEI) is a useful predictor for the stratocumulus cloud fraction for both time scales. We show, however, that the variation of the highs provides additional information on the fraction change on an annual cycle. The Northern Hemisphere is more sensitive to the highs change compared to the Southern Hemisphere. Variations in the structure, area, and location of subtropical highs are not considered the dominant influencing factors (correlations about 0.3~0.4). Nevertheless, we found a qualitative preference that stratocumulus clouds prefer a flatter, large, and westward subtropical high.

How to cite: Ding, H., Stevens, B., and Schmidt, H.: Links between subtropical high-pressure systems and stratocumulus clouds variation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9182, https://doi.org/10.5194/egusphere-egu24-9182, 2024.

EGU24-9705 | ECS | Orals | AS1.7

Linking tropical large-scale circulation and deep convection to subtropical marine low-clouds in the Pacific Ocean 

Danny McCulloch, Hugo Lambert, Mark Webb, and Geoffrey Vallis

Global Climate Models (GCMs) are essential for predicting the impact of climate change in the coming decades. However, the primary source of uncertainty in these predictions is our limited understanding of cloud feedback and its representation in models. Improving our knowledge of how changes in local heating rates affect low clouds via tropical overturning circulation is crucial to refining climate projections. In this study, we use an AMIP climate assessment configuration (with CMIP6 forcing) of the Met Office Unified Model to test the remote effects on subtropical clouds caused by localised changes in tropical atmospheric circulation.  

We conduct this causal analysis by applying a heating/cooling perturbation in the free troposphere in a typical convecting and in a typical subsiding region in the equatorial Pacific Ocean. This method allows us to perturb large-scale circulation and track the subsequent effects on subtropical clouds. We find that when we apply a heating or cooling in the tropical free troposphere, the subsidence in the subtropics strengthens but we do not find a change in the low-cloud content. However, when we apply a cooling perturbation in the Southeast Pacific subsidence region, which increases subsidence, we get more local low-clouds. This is the opposite of what is suggested by previous studies which use a correlative approach on a global scale. 

We show how changing the intensity of the large-scale circulation in the equatorial Pacific influences subtropical low clouds, while tracking the effects of our perturbations in the transition regions between the tropics and subtropics. Our findings demonstrate a new way to conduct causal studies to better understand and isolate the influence of the free troposphere on large-scale circulation and subtropical clouds in a full GCM setup. Additionally, our findings emphasise how regional influences might differ from global results, highlighting the importance of recognising and quantifying regional contributions which dictate global trends.

 

How to cite: McCulloch, D., Lambert, H., Webb, M., and Vallis, G.: Linking tropical large-scale circulation and deep convection to subtropical marine low-clouds in the Pacific Ocean, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9705, https://doi.org/10.5194/egusphere-egu24-9705, 2024.

EGU24-9900 | ECS | Posters on site | AS1.7

Seeing doldrums from space 

Geet George and Julia Windmiller

Doldrums — the bane of sailors in ages past — are mesoscale regions of calm winds, usually seen dividing two zonal bands of convective clouds near the thermal equator. These features, together often manifest as the inter-tropical convergence zone (ITCZ), particularly over the Atlantic. The terms "ITCZ" and "doldrums" are often incorrectly used inter-changeably. With satellite observations, we show that they are in fact not the same meteorological feature. Although the doldrums seemed to have departed from current discussions, recent cross-equatorial ship-borne observations in the Atlantic have brought back attention to them and their role in shaping the distribution of convection. We use satellite measurements spanning more than 15 years to report statistics of doldrums over the Atlantic and the East Pacific. Along with their spatial extents, we document their zonal and meridional positioning as well as the seasonal and inter-annual variability therein. We also record the vertical extents of these calm horizontal winds, albeit with a shorter period of sampling. Co-located measurements of column moisture, surface rain rate and cloud liquid water provide an idea of the environmental conditions that are associated with the presence of doldrums. Particularly, we see an anomalously dry atmospheric column over the doldrums compared to that over the adjacent convergence bands, which is similar to those observed from the ship-based observations. We also find long periods (ca. 1 month) of westward propagation of doldrums, but there can be large differences in their spatio-temporal persistence among different years. Our characterization enables frameworks attempting to explain the physical mechanism of doldrums as well as their role in the mesoscale organization of the ITCZ.

How to cite: George, G. and Windmiller, J.: Seeing doldrums from space, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9900, https://doi.org/10.5194/egusphere-egu24-9900, 2024.

EGU24-9928 | ECS | Orals | AS1.7

Tracking Clouds: Comparing Geostationary Satellite Observations and Model Data in the EUREC4A domain 

Felix Müller, Torsten Seelig, and Matthias Tesche

Cloud modelling is a very important tool for climate research. However, it is not an easy task to validate model data and assess a model’s performance. Since cloud model data can not be expected to be an exact match of corresponding satellite data, there is no immediate method of comparison available.

We use a cloud tracking algorithm [1] to find the lifetime and cloud size distributions of the cloud datasets. This enables us to provide a unique quality assessment of the model data. Lifetime information is interesting because it encompasses multiple dynamic scales from micro to planetary regimes, while cloud size and cloud cover are important factors for the radiative properties of the clouds in a region and characterise the clouds’ general behavior.

Here we compare satellite data from the EUREC4A campaign [2] (observed by the Advanced Baseline Image onboard the GOES-16 satellite) and model output from ICON-LEM tailored for the EUREC4A campaign [3], where two resolutions are available. All datasets are located east of Barbados in the Caribbean Sea. We build on previous cloud tracking analyses for the GOES satellite dataset [1].

For the comparison between the three datasets, we first show the temporal development of cloud cover and number of clouds as an overview for the datasets. Secondly, we show the distributions of clouds lifetimes and sizes for all trajectories. The linear regression exponent for the logarithmic cloud size distribution can be expected to be around -2 on the global scale [4], which all three datasets come close to. However for this region, we would expect small clouds to have a bigger influence compared to the global view. This effect can be observed in the model data which have slightly more negative exponents for both resolutions. Thirdly, we show the average development of cloud size over the lifetime of the tracked clouds as a further metric for evaluating how well the model can represent the cloud-development processes.

References

[1] Seelig et al. (2023) “Do optically denser trade-wind cumuli live longer?”, in Geophysical Research Letters, doi: 10.1029/2023GL103339

[2] EUREC4A campaign: www.eurec4a.eu

[3] Schulz, Hauke & Stevens, Bjorn (2023) “Evaluating Large-Domain, Hecto-Meter, Large-Eddy Simulations of Trade-Wind Clouds Using EUREC4A Data” in Journal of Advances in Modeling Earth Systems, doi: 10.1029/2023MS003648

[4] Wood, Robert & Field, Paul (2011) “The Distribution of Cloud Horizontal Sizes”, in J. Climate, doi: 10.1175/2011JCLI4056.1

How to cite: Müller, F., Seelig, T., and Tesche, M.: Tracking Clouds: Comparing Geostationary Satellite Observations and Model Data in the EUREC4A domain, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9928, https://doi.org/10.5194/egusphere-egu24-9928, 2024.

Warm conveyor belts (WCB) are regions of large-scale coherent airflow within extratropical cyclones that rapidly ascend from the boundary layer to the upper troposphere. During their ascent, WCB air parcels experience various microphysical processes that produce mixed-phase clouds and large amounts of precipitation. They also transport water vapour and cloud condensate to the upper troposphere/lower stratosphere (UTLS), which is important for Earth’s radiative budget. Recent studies have found that deep and embedded convection play an important role in WCBs. This points to the necessity of high-resolution simulations, that are well validated with observational data to provide a “benchmark” for coarser-resolution global (climate) models. We conduct a Lagrangian investigation of the physical processes governing WCB moisture transport and cloud composition with a particular focus on (i) the microphysical processes controlling moisture loss from the WCB, and (ii) the cloud microphysical properties of the cirrus clouds in the WCB outflow.

To this end we conducted a case-study from the HALO-WISE campaign and ran a high-resolution doubly nested ICON simulation with a maximum (convection permitting) resolution of ~3km. Online trajectories are calculated that capture convective ascent and allow for a Lagrangian analysis of WCB moisture transport and WCB cloud structure.

The Lagrangian metrics show large differences in the behaviour of moisture transport to the UTLS for trajectories with different ascent timescales. Fast ascending trajectories ascend further south and to much lower pressures and temperatures than their slower counterparts. They also produce much more precipitation and have markedly different hydrometeor contents throughout the ascent. In the ice phase, slow ascending trajectories mainly produce ice and snow through depositional growth, whereas fast trajectories also produce graupel and hail by collision-coalescence. Warm rain processes dominate the moisture loss for all ascent timescales, but for fast ascending trajectories the conversion of moisture to precipitation by microphysical processes in the ice phase increases. These findings are important because widely used coarse-resolution simulations with convection parameterization run the risk of missing the physical processes we see for the fastest ascending trajectories.

How to cite: Schwenk, C. and Miltenberger, A.: A Lagrangian investigation of the (micro)physical processes controlling warm conveyor belt moisture transport and cloud properties, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10425, https://doi.org/10.5194/egusphere-egu24-10425, 2024.

EGU24-12792 | Orals | AS1.7

Role of Sub-Cloud Rain Evaporation on Boundary Layer Decoupling over Barbados Island 

Mampi Sarkar, Youtong Zheng, and Raphaela Vogel

This study investigates the influence of sub-cloud rain evaporation on the decoupling of sub-tropical marine cumulus-topped raining boundary layers. Using 24-hour wind lidar and Ka-band radar observations on February 9, 2020 from the Barbados Cloud Observatory (BCO), along with in-situ rain microphysical observations from the ATR aircraft during the EUREC4A field campaign, we extract rain microphysical parameters - raindrop number concentration (N0) and geometric mean diameter (Dg). These parameters, alongside surface relative humidity measurements, serve as inputs to initialize a single-column rain evaporation model, allowing us to derive vertical profiles of rain evaporation fluxes and evaporation cooling rates. Our analysis identifies 'top-heavy' profiles characterized by maximum evaporative cooling near the cloud base, featuring smaller Dg and larger N0. Conversely, 'bottom-heavy' profiles exhibit larger Dg and smaller N0, with maximum evaporative cooling closer to the surface. Notably, our findings reveal that top-heavy profiles, especially when cloud bases are higher, tend to be more decoupled than bottom-heavy profiles. The higher decoupling of the top-heavy profiles is attributed to the stable configuration of the evaporatively-cooled moisture layer just below the warmer cloud layer, hindering moisture transport to the cloud. In contrast, for a bottom-heavy profile where the evaporatively-cooled moisture layer is accumulated closer to the surface over a warmer sea surface, surface-driven mixing promotes moisture transport to cloud bases, resulting in less decoupling. The decoupling index, independently estimated from the difference between ceilometer-based cloud base height and empirically determined lifting condensation level, enhances the robustness of our results. While emphasizing the significant influence of sub-cloud rain evaporation on the decoupling of cumulus-topped raining boundary layers, our study has not explored other factors like surface and radiative fluxes, which could also contribute to the boundary layer decoupling.

How to cite: Sarkar, M., Zheng, Y., and Vogel, R.: Role of Sub-Cloud Rain Evaporation on Boundary Layer Decoupling over Barbados Island, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12792, https://doi.org/10.5194/egusphere-egu24-12792, 2024.

EGU24-12952 | ECS | Orals | AS1.7

Representation of marine low‐level clouds in global-coupled kilometer-scale simulations 

Ian D'Amato Dragaud, Jakub Nowak, Piotr Dziekan, Junhong Lee, Juan Pedro Mellado, and Bjorn Stevens

Marine boundary layer clouds stand out because of their importance for Earth's planetary albedo and their central role in determining Earth's sensitivity to forcing. The new global-coupled simulations at kilometer-scale resolution in both the atmosphere and the ocean in the framework of the H2020 nextGEMS project offer new opportunities to study cloud processes and their environmental factors, as well as provide unprecedented realism and new opportunities for comparison to observations. We examine the representation of (sub)tropical stratocumulus and trade-wind cumulus clouds by the IFS and ICON models configured with kilometer-scale resolution and global domains. The simultaneous consideration of ICON and IFS allows us to compare two strategies. The former simplifies parameterizations to understand process interactions better, sacrificing degrees of freedom to tune the model. The latter considers more sophisticated parameterizations, which allow for better tuning. The results of this study show the value of both. The performance of the four-year simulations is assessed in terms of the top-of-atmosphere (TOA) albedo and the vertical structure of the atmospheric boundary layer in eight regions where low-clouds are climatologically found. The stratocumulus regions are located in the eastern subtropical ocean basins, and the trade-wind cumulus regions are located west and equatorward from the stratocumulus ones. As an observational reference for the TOA albedo, we used satellite data from the CERES-EBAF TOA dataset.
Both models captured the mean horizontal distribution and seasonal cycle of TOA albedo and the typical vertical structure of the low atmosphere over the stratocumulus regions. Despite its relatively simplistic approach to sub-grid parameterizations, particularly turbulence mixing treated with the Smagorinsky scheme, ICON performed comparably well to IFS, which employs more sophisticated solutions, including eddy-diffusivity mass flux and convection schemes. Regarding trade-wind cumulus, both models overestimate the mean TOA albedo. To validate the simulated vertical structure of the atmospheric boundary layer in the northwestern Atlantic trade-wind regime, we used the radiosondes launched at the Barbados Cloud Observatory (BCO) during the EUREC4A field campaign. The ICON and IFS models represent the main characteristics of the vertical structure of wind speed, temperature, and moisture observed at the BCO. We also find some discrepancies between the model representation and the observations. The simulations represented a colder (1 K) vertical profile than the observations. The ICON represented a drier cloud layer between 1–2 km and a moister layer above it, which is attributed to too much vertical mixing across the top of the cloud layer and suggests some revision of the stability correction function. The IFS model represented this region better than ICON, which was expected because IFS uses a shallow convection scheme, which allows better control of this region. However, IFS represented slightly drier the lowest 500 m.

How to cite: D'Amato Dragaud, I., Nowak, J., Dziekan, P., Lee, J., Mellado, J. P., and Stevens, B.: Representation of marine low‐level clouds in global-coupled kilometer-scale simulations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12952, https://doi.org/10.5194/egusphere-egu24-12952, 2024.

The anvils of deep convective clouds (DCCs) have an important impact on global radiation balance. While the anvil cloud area feedback to warming temperatures is expected to have a cooling effect, it has the largest uncertainty of any cloud-climate feedback. Differences in anvil structure contribute to this uncertainty due to changes in the proportions of thicker, cooling anvil and thinner, warming anvil cirrus. A lack of long-term observational datasets of both convective and anvil properties of DCCs has limited our understanding of the connections between these processes.

Using a novel cloud tracking algorithm we detect and track the developing cores, thick and thin anvils of DCCs seen in 5 years of GOES-16 imagery, allowing investigations of their properties throughout the DCC lifecycle. Using this dataset, we compare how the amount of thin anvil cirrus changes with the intensity and organisation of observed DCCs. Previous studies of anvil structure have found that the proportion of thin cirrus increases with convective intensity across a range of regimes. We find that the thin anvil proportion increases with convective intensity both in area and lifetime. To the contrary, for more organised DCCs – those with more cores – we find, however, that the thin anvil area and lifetime both decrease as a proportion of the total anvil. While more intense DCCs have shorter growing phases and longer dissipating phases, the opposite is true for more organised DCCs. These differences in lifecycle have an important impact on thin anvil proportion. The contrast in structure and lifecycle between DCCs with increasing intensity and increasing organisation occurs despite both convective processes having positive impacts on the total anvil area, lifetime and temperature. As both the intensity and organisation of DCCs are expected to increase with warming, we may expect differences in anvil cloud area feedback between different regimes depending on the occurrence of isolated or organised DCCs.

How to cite: Jones, W. and Stier, P.: Contrasting effects of intensity and organisation on the structure and lifecycle of deep convective clouds, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13548, https://doi.org/10.5194/egusphere-egu24-13548, 2024.

EGU24-15608 | Posters on site | AS1.7

A detailed statistics of cloud and precipitation processes in the trades from the RV M.S. Merian 

Claudia Acquistapace, Sabrina Schnitt, Sibylle Krause, Nils Risse, Davide Ori, Dwaipayan Chatterjee, Torsten Seelig, Diego Lange, Florian Späth, and Isabel Mccoy

Shallow cumulus clouds always played an essential role in the uncertainties in climate predictions. The EUREC4A campaign was conceived to tackle the problem of how such clouds will respond to climate change. Recent studies showed that although the research outcomes of the EUREC4A campaign constrained their response to climate sensitivity, open questions remain on the importance of mesoscale processes and the role of precipitation in the cloud organization, both aspects not well represented in climate models. 

The research vessel (RV) Maria S. Merian, during the campaign, continuously provided high-resolution observations of clouds, precipitation, and atmospheric boundary layer properties in a vast area of the Atlantic Ocean east and south of Barbados island. Here, we exploit such observations to statistically characterize clouds and precipitation properties and the surrounding environment in which they develop. 

In agreement with the literature, we define shallow clouds with cloud tops within 600 m of lifting condensation level (LCL) and congestus clouds with cloud tops between 600 and 4000m. We characterize their cloud properties, rain rates, and raindrop size distributions. We investigate virga generated from shallow and congestus clouds and describe how humidity and temperature change with the different cloudy conditions. We also display the relation between the W-band radar reflectivity and the radar skewness, revealing insights into the precipitation onset for shallow and congestus clouds and characterizing their cloud lifetime. Finally, we connect the local boundary layer and cloud properties to configurations occurring at the mesoscale, providing additional characterizations of flower, fish, sugar, and gravel in terms of ship-based observations.

How to cite: Acquistapace, C., Schnitt, S., Krause, S., Risse, N., Ori, D., Chatterjee, D., Seelig, T., Lange, D., Späth, F., and Mccoy, I.: A detailed statistics of cloud and precipitation processes in the trades from the RV M.S. Merian, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15608, https://doi.org/10.5194/egusphere-egu24-15608, 2024.

EGU24-16010 | ECS | Posters on site | AS1.7

Lassoing Fish — Linking tropical Fish cloud structures to extratropical fronts 

Theresa Mieslinger, Julia Windmiller, and Bjorn Stevens

People on Barbados are used to “rope-like” cloud structures passing over the island and their association with more disturbed weather conditions. In more recent literature, such cloud structures are frequently named Fish owing to their fishbone-like appearance on satellite images. Schulz et al., 2021, identified Fish cloud structures in satellite imagery via machine learning and showed that they have a pathway coming from the extratropics and often show a frontal character based on their surface convergence field, both indicative of them being associated with extratropical fronts. Extratropical fronts are known to impact convection in the tropics. A wealth of past studies based on theory, observations and modelling showed the distinct water-vapor structure, precipitation characteristics, as well as radiative-dynamical mechanisms of extratropical intrusions and highlight their importance for tropical moist convection.

In our study, we investigate the link between well-studied extratropical fronts and Fish-like cloud appearances. We apply a neural network to identify Fish cloud structures across the global tropics and investigate them with respect to the characteristics of well-studied extratropical fronts. We aim to answer the questions whether all Fish patterns are the visual imprint of extratropical fronts and how their thermodynamical and dynamical properties change as they propagate to lower latitudes.

How to cite: Mieslinger, T., Windmiller, J., and Stevens, B.: Lassoing Fish — Linking tropical Fish cloud structures to extratropical fronts, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16010, https://doi.org/10.5194/egusphere-egu24-16010, 2024.

EGU24-17171 | ECS | Orals | AS1.7

Pattern Recognition of Convection in the Atlantic Intertropical Convergence Zone 

Lennéa Hayo, Julia Windmiller, Hauke Schulz, Claudia Acquistapace, and Susanne Crewell

The Intertropical Convergence Zone (ITCZ) in the Atlantic is typically described as a narrow band of precipitation and deep convection. However, this description often stems from long-term averaging of precipitation or outgoing longwave radiation in the ITCZ. On shorter time scales, the ITCZ is much more dynamic and various classifications of different patterns can be attempted. One possibility is based on satellite images collected during the GATE campaign in 1974 where four patterns - Line, Double Line, Broad and Cluster - were previously identified. In our analysis, we build on the pioneering results from GATE, supplement the category No Clouds, and validate the patterns based on 43 years of harmonized, equal-angle grid geostationary satellite images. In a first attempt, manual classification of these patterns in the visible spectrum proved feasible for 1000 km wide cutouts and for manually defining the extent of the pattern on the entire Atlantic ITCZ. Manual classification for July 2021 has already shown that all classes neither occur with the same frequency nor the same spatial ditribution over all regions of the Atlantic. For further analysis on the appearance of these patterns on longer time scales the satellite images have been classified by a machine learning algorithm, and their frequency dependence on season and region have been analyzed. These results now enable us to ask why these different patterns occur.

How to cite: Hayo, L., Windmiller, J., Schulz, H., Acquistapace, C., and Crewell, S.: Pattern Recognition of Convection in the Atlantic Intertropical Convergence Zone, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17171, https://doi.org/10.5194/egusphere-egu24-17171, 2024.

EGU24-17571 | ECS | Posters on site | AS1.7

Interactions between tropical low marine clouds and wind profiles using ALADIN/Aeolus 

Zacharie Titus, Hélène Chepfer, and Marine Bonazzola

Low clouds such as cumulus and stratocumulus cover a great part of the tropical belt all year long. Variables affecting the formation and dissipation of these clouds like the Sea Surface Temperature or humidity have been studied for a long time now. However, wind profiles could previously only be obtained by radiosondes (localized) or airborne measurements (regional). From 2018 to 2023, ESA ALADIN/Aeolus Doppler Wind LIDAR has orbited the Earth, collecting wind profiles at a global scale, between the surface and 20 km of altitude. This instrument has opened new perspectives regarding wind-cloud interactions with co-located low cloud profiles and wind profiles.

 

In a Large Eddy Simulation Helfer et al.[1]  have shown that wind shear can have an impact on the development of trade wind cumulus clouds in the first kilometers of the atmosphere. Mieslinger et al.[2]  have shown combining ERA5 wind and ASTER imagery, that stronger surface wind are correlated with a more important cloud cover. In our study, we will see how ALADIN/Aeolus can help us to better understand interactions between low clouds and wind with co-located observed wind profiles and cloud profiles. We will focus on the subtropical marine boundary layer, around strong subsidence regions, like the descending branch of the Hadley cell. In these regions, low clouds are present in number and ALADIN is rarely attenuated due to the rare occurrence of mid and high altitude clouds[3].

 

[1] Helfer et al. - How Wind Shear Affects Trade-wind Cumulus Convection (2020)

[2] Mielsinger et al. - How Wind Shear Affects Trade-wind Cumulus Convection (2020)

[3] Chepfer et al. - The GCM oriented CALIPSO Cloud Product (CALIPSO-GOCCP) (2010)

 

How to cite: Titus, Z., Chepfer, H., and Bonazzola, M.: Interactions between tropical low marine clouds and wind profiles using ALADIN/Aeolus, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17571, https://doi.org/10.5194/egusphere-egu24-17571, 2024.

EGU24-21182 | Posters on site | AS1.7

The Cold Pool Model Intercomparison Project (CP-MIP) 

Jan Kazil, Raphaela Vogel, Peter Blossey, Steven Boeing, Leif Denby, Salima Ghazayel, Thijs Heus, Roel Neggers, Girish Raghunathan, and Pier Siebesma

Atmospheric cold pools form when cool downdrafts from cumulus clouds spread out laterally at the surface. The cool surface air suppresses convection and erases clouds around the downdraft. Over the oceans, the resulting cloud-free areas are often larger than 100 km, and turbulence and clouds recover only after many hours. The properties, mechanisms, lifecycle, and radiative effect of cold pools are currently not well understood. This is in part because the key processes of cold pools proceed on scales below the resolution of large scale models, and in part because of model biases in cold pool simulations by high resolution models.

The Cold Pool Model Intercomparison Project (CP-MIP) seeks to investigate and improve the fidelity of model representation of convective cold pools. The goals of CP-MIP are the identification, characterization, and quantification of model biases through comparison with observed cold pool statistics, the convergence of models towards a robust basis for the study of cold pools, and the improved representation of cold pools in high resolution and large scale simulations.

We introduce CP-MIP, describe the approaches and objectives, and set out the elements of CP-MIP. The first stage of CP-MIP focuses on shallow convective cold pools over the tropical oceans, which are primarily associated with trade cumulus clouds. Observations from the EUREC4A and ATOMIC field campaigns, and modeling efforts from the CP-MIP partner projects contribute to CP-MIP. We present an analysis of first results.

How to cite: Kazil, J., Vogel, R., Blossey, P., Boeing, S., Denby, L., Ghazayel, S., Heus, T., Neggers, R., Raghunathan, G., and Siebesma, P.: The Cold Pool Model Intercomparison Project (CP-MIP), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21182, https://doi.org/10.5194/egusphere-egu24-21182, 2024.

EGU24-651 | ECS | Posters on site | HS7.1

Multi-scale comparison of rainfall measurement in Paris area between two optical disdrometers of different working principles 

Marcio Matheus Santos de Souza, Auguste Gires, and Jerry Jose

A disdrometer is an instrument designed to assess both the size and velocity of descending hydrometeors. The applications of rainfall measurements retrieved with the help of disdrometers are diverse, spanning areas such as traffic control, scientific research, airport observation systems, and hydrology. Modern disdrometers leverage microwave or laser technologies that have increased the accuracy of the measurements with each iteration. Still, the quality of measurements fluctuates depending on factors such as raindrop size, wind velocity, and rain rate. A comprehension of these variations is needed to better understand the level of reliability of each device depending on the specific rain conditions.

In this study, we compare the performance of two optical disdrometers : 3D Stereo disdrometer (manufactured by Thies Clima) and Parsivel2 (manufactured by OTT). Both devices provide size resolved measurement of rainfall along with velocity of falling drops. Parsivel is set to record data every 30 seconds over a sampling area of 54 cm² and arranges the information in 32 x 32 classes of drop size and velocity. Unlike the Parsivel, 3D Stereo does not discretize measurements, and directly provides the diameter and velocity of each falling drop in a sampling area of 100 cm² with a measuring resolution of 0.08 mm and 0.2 m/s respectively, and a temporal resolution of 1 millisecond. This finer resolution data enables us to study rainfall variability at very small scales which are not usually available.

Here, we used continuously and simultaneously measured data since 21/08/2023, from TARANIS observatory of ENPC (https://hmco.enpc.fr/portfolio-archive/taranis-observatory/). The initial comparison of the data was done using a time series of rain-rate for rainfall events in between a dry period of at least 15 minutes and total depth >0.7 mm. This revealed an unexpected disparity in the water volume collected between the devices. Parsivel collected more than 3D Stereo on every instance, and the disparity got bigger as the rain rate increased. With the purpose of studying the source of this disparity, the sampling area of the 3D Stereo was divided into 8 sections and compared with each other. This showed that the estimate of rainfall parameters such mean diameter, mean velocity of the drops (which were expected to be uniform over long periods regardless of the section where drops are measured) were not the same for the sections studied, and exhibited clear trends. To understand this discrepancy in a scale invariant way, and to evaluate the performance of devices across scales and not only at a single scale, the widely used framework for studying variability of geophysical fields – Universal Multifractals (UM) was employed for assessing the scaling behavior of fields. Rainfall from both devices showed previously reported average scaling behavior from 30 s to 30 min. The difference between rain events and also the behavior at finer scales, which can be accessed from 3D stereo disdrometer were also studied using the UM framework and will be discussed.

Authors acknowledge the Ra2DW project (supported by the French National Research Agency - ANR-23-CE01-0019), for partial financial support.

Keywords: rainfall; disdrometer; multifractals;

How to cite: Santos de Souza, M. M., Gires, A., and Jose, J.: Multi-scale comparison of rainfall measurement in Paris area between two optical disdrometers of different working principles, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-651, https://doi.org/10.5194/egusphere-egu24-651, 2024.

EGU24-2655 | Posters on site | HS7.1

Designing the TUDS rainfall observatory in northern Ghana 

Nick van de Giesen, Frank Annor, Sylvester Ayambila, Richard Dogbey, Vincent Hoogelander, Gordana Kranjac-Berisavljevic, Kingsley Kwabena, Rob Mackenzie, Marc Schleiss, and Remko Uijlenhoet

Convective rainfall in West Africa is poorly monitored and understood. There are large gaps between remote sensing rainfall products and what is observed on the ground. There are several reasons for these gaps. First, satellites and rain gauges measure at very different scales so one would expect that remote sensing products contain more events at lower intensities than small gauges. Second, a lot happens between the clouds observed by satellites and the ground. Rainfall may evaporate and move with the wind, causing further disconnects between space and ground observations. There are also indications that clouds in West Africa contain many small drops due to the presence of many aerosols, thereby possibly “misleading” satellite products. Finally, it is likely that there are further factors that are not yet accounted for.

In order to tackle this disconnect between ground and space observations, we plan to build the TUD - UDS, or TUDS, rainfall observatory near Tamale and Nyankpala in northern Ghana. The following are initial ideas that we would like to discuss at the EGU. It will be a multi-scale observatory, starting at a grid of nine gauges on a 500m grid (1km x 1km total). This small grid should capture the inherent spatial variability of convective rainfall events with convective cells of 2km or less. The largest grid would also contain nine gauges and have an extent of 10km x 10km, or larger. This outer grid would capture the movement of convective cells, including those contained within so-called line squalls. An intermediate grid may complete this picture. The structure will look, more or less, like the one in the picture below.

Different instruments will be at our disposal, from simple totalling rain gauges to disdrometers. There will be five Thies disdrometers, one Ott Parsivel, and several TAHMO stations and/or tipping bucket rain gauges. Also experimental intervalometers will be placed in the grid to better understand rainfall structure over time and space. Several instruments will be co-located to examine strengths and weaknesses of the different methods.

We explicitly invite comments and contributions.  

 

TEMBO Africa: The work leading to these results has received funding from the European Horizon Europe Programme (2021-2027) under grant agreement n° 101086209. The opinions expressed in the document are of the authors only and no way reflect the European Commission’s opinions. The European Union is not liable for any use that may be made of the information.

How to cite: van de Giesen, N., Annor, F., Ayambila, S., Dogbey, R., Hoogelander, V., Kranjac-Berisavljevic, G., Kwabena, K., Mackenzie, R., Schleiss, M., and Uijlenhoet, R.: Designing the TUDS rainfall observatory in northern Ghana, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2655, https://doi.org/10.5194/egusphere-egu24-2655, 2024.

Precipitation droplets are influenced by environmental fields and transform in time and space, following cloud microphysical processes. Accordingly, a raindrop size distribution (DSD) changes shape in a various form. However, DSDs cannot be calculated directly in radar or bulk models and are expressed using an approximate function. Exponential and gamma distribution are well-known as approximation functions, but there are DSDs of shapes that cannot be represented by these functions. One of them is a bimodal DSD with two peaks. Previous modeling studies have indicated that the bimodal DSD is formed when the collision-breakup process reaches equilibrium. On the other hand, recent observation-based studies have discussed the influence of convective activity within the precipitation system on forming the bimodal DSD. However, observations have not been able to quantitatively study the microphysical changes of individual particles and have yet to reveal the formation mechanisms within the precipitation system. In this study, we investigated quantitatively the process of the formation of the bimodal DSD by two-dimensional simulation of multicellular convection with the bin method. The simulation results showed that the bimodal DSD was formed during the updraft and downdraft in the mature stage of the multicell. Additionally, the bimodal DSD was formed at lower altitudes where there was inflow into the precipitation system. Particles that constituted the maximum of the bimodal DSD were found to have been advected by the inflow. Particles that constituted the local maximum dropped against the updraft. In contrast to these, particles that constituted the local minimum were less affected by the inflow and had difficulty dropping against the updraft. These results suggested that the bimodal DSD was formed by horizontal and vertical size sorting because of inflow and updrafts in the mature multicellular convection. In the future, it is necessary to simulate the reproduction of observed cases and compare them with observations.

How to cite: Okazaki, M., Yamaguchi, K., Yanase, T., and Nakakita, E.: Spatiotemporal structure of raindrop size distribution due to flow field in a convective precipitation system simulated by bin cloud microphysics model., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6387, https://doi.org/10.5194/egusphere-egu24-6387, 2024.

EGU24-6767 | Posters on site | HS7.1

Microphysical properties of the stratiform precipitation in Kyiv city based on OTT Parsivel2 and pluviograph data  

Svitlana Krakovska, Liudmyla Palamarchuk, and Anastasiia Chyhareva

Precipitation detailed characteristics, namely spectrum of particles by their sizes, phase and precipitation intensity with high-resolution timestep, still need to be investigated due to the complexity of their direct instrumental measurements but necessity for improving forecast for different applications including hydrological and emergency service. Our study is focused on the stratiform precipitation associated with cloud system (Ns-As) of warm front during prolonged and intense precipitation event on the 25th October 2023 in Kyiv, Ukraine. This warm front cloud system was connected with an occluded low over Poland which developed on the East periphery of a huge depression (970 hPa) over the Northern Atlantic.

We analyzed the OTT Parsivel² - Laser Weather Sensor measurement data with 10sec time steps. Parsivel² was installed nearby regular meteorological station, which is a part of the WMO network, and its measurements were used for verification. Precipitation intensity and raindrop distributions had wavy character, where we can distinguish a few waves of precipitation enhancement. The average intensity of the minimum wave was 0.02mm/min that corresponds to 30 raindrops with size varying from 0.5 to 1.5mm and maximum falling speed 4m/s for the largest raindrops. The average intensity of maximum precipitation enhancement wave was 0.15mm/min with around 100 raindrops per 10sec with sizes mainly from 0.5 to 2.5mm (with some raindrop sizes up to 3.5mm) and average falling speed 5-6m/s. Total amount of 26-hour precipitation event was 24.2mm according to OTT Parsivel² measurements and 26mm according to SYNOP data from Kyiv WMO station (ID 33345). We should note that in modern climate condition in Kyiv such prolonged frontal precipitation even in autumn is rather rare event in respect to previous decades.  

Gained results were compared with previous studies based on 20-year measurement by pluviograph at the same Kyiv WMO station. For stratiform precipitation, average maximum precipitation intensity within precipitation enhancement waves was around 0.11mm/min. Duration of main precipitation enhancement waves was around 21 minutes. Characteristics of precipitation enhancements waves are key for assessment of surface runoff value. The significant fraction of water on the ground that forms surface runoff goes mainly from such precipitation enhancement waves, when around 60 up to 90% of the maximum surface runoff can be formed.

In conclusion, OTT Parsivel² Laser Weather Sensor was used in Ukraine for the first time and demonstrated good performance versus the city station accumulation measurements and historical pluviograph data at the station. At the moment this instrument is under way to the Ukrainian Antarctic station Akademik Vernadsky where further exploitation will allow to test and obtain measurement data for different phase of precipitation, mostly mixed and solid and compare with data from Micro Rain Radar Pro. Obtained and future results will extend our understanding of precipitation formation, their microphysics and dynamics, interconnections between precipitation intensity and size/fall speed of raindrops and solid particles. Future studies could help to evaluate the transformation of cloud and precipitation formation processes under the climate change for better parameterization in numerical models, to study the microphysical structure and composition of precipitation.

How to cite: Krakovska, S., Palamarchuk, L., and Chyhareva, A.: Microphysical properties of the stratiform precipitation in Kyiv city based on OTT Parsivel2 and pluviograph data , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6767, https://doi.org/10.5194/egusphere-egu24-6767, 2024.

EGU24-7802 | Posters on site | HS7.1

Cloud based tool to enhance urban resilience with the Fresnel Platform using the Multi-Hydro Model 

Guillaume Drouen, Daniel Schertzer, Auguste Gires, and Ioulia Tchiguirinskaia

The aim of the Fresnel platform of École des Ponts ParisTech is to foster research and innovation in multiscale urban resilience. Studying the hydrological response of such complex urban areas accounting also for small scale spatio-temporal precipitation variability requires adapted tools. For these reasons, RadX provides a user-friendly graphical interface to run simulations using a fully distributed and physically based model: Multi-Hydro. RadX is designed as a Software as a Service (SaaS) platform, allowing users to work with data across a wide range of space-time scales and the appropriate tools for analyzing and simulating this data.

The hydrological model, developed at École des Ponts ParisTech, integrates four open-source software applications previously used and validated independently by the scientific community as well as practitionners. Its modular structure includes a surface flow module, sewer flow module, a ground flow module and a precipitation module. It is able to simulate the quantity of runoff and rainwater infiltrated into unsaturated soil layers from any space-time varying rainfall event at any location of the studied peri-urban watersheds, as well as depth and flow in all the pipes and nodes of the sewer network.

Users can launch hydrological simulations using the Multi-Hydro model directly from their web browser, while they are run on dedicated servers. They can adjust two key input parameters: the land use of the studied catchment and the rainfall data. Dedicated tools have been developed to enable users to modify the land use of the catchment with the same ease as using a raster graphic editor. Users can either choose real rainfall events captured by the X-band weather radar located at École des Ponts ParisTech or utilize user-defined synthetic rainfall as input. Data from other radar can also easily be integrated. 

For the simulation output, the interface provides users with different tools to study in detail the impact of the chosen input parameters. For instance, by simply selecting two sewer junctions on an interactive map, users can generate a sewer path between these two points and display an interactive representation of the water level heights in sewer conduits and junctions along the user-defined sewer network path.

Additional components can be integrated into RadX to meet specific requirements using visual tools and forecasting systems, including those from third parties. Developments are still in progress, with a constant loop of requests and feedback from the scientific and professional world.

How to cite: Drouen, G., Schertzer, D., Gires, A., and Tchiguirinskaia, I.: Cloud based tool to enhance urban resilience with the Fresnel Platform using the Multi-Hydro Model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7802, https://doi.org/10.5194/egusphere-egu24-7802, 2024.

EGU24-8714 | ECS | Orals | HS7.1

Improvements in rain gauge design and measurements to minimise under-catch errors 

Mark Dutton and Domenico Balsamo

Precipitation measurements provide historic and near real-time data for Met Services and ground truth references for modelling and forecasting.  Current methods suffer from well-known under-catch problems1.  These are caused by wind effect2 on the gauge, out-splash, evaporation, and internal tipping bucket (‘counting’) errors.  Thereby causing water-balance errors for Hydrology scientists.  Good gauge design and correct siting can minimise these errors but not eliminate them.

Over 10 years of research, into the best aerodynamic shape for a precipitation gauge, was carried out to minimize out-splash and maximize catch3.  Comparison field work1 and Computational Fluid Dynamic4 (CFD) research was undertaken between standard straight-sided, ‘chimney’ shaped, aerodynamic shaped and pit-installed (out of the wind) gauges.  This research demonstrated that it may be possible to quantify under-catch using gauge rim-based wind data, drop-size and drop-type information.  Field comparison between the “new instrument” and pit gauge will be needed.  Once quantified at source, it can then be used to accurately correct live data.

This new instrument uses ultrasonic wind sensors and Doppler-Shift measuring techniques to obtain wind versus rainfall catch data.  Also using optical and/or impact sensing techniques we can measure the individual drop size and count the drops involved in a rain event.  By adding weighing technology to the tipping bucket design and improving calibration methods, we can improve resolution and detect evaporation losses.  Also power efficient and controlled heating to allow the inclusion of solid precipitation measurements.  Then finally use machine learning (ML) techniques to correct the errors.

Therefore, the aim of this project is to design a simple to use intelligent instrument to minimise and possibly eliminate under-catch measurement errors balancing out the water budget.  Allow installation of the instruments at ground and raised levels without increase in errors caused predominately by the wind.  Create near real-time and historic field precipitation data, both corrected and non-corrected to be use by Met Services and Hydrology modelling scientists.

References

1. Sevruk, B. Methods of correction for systematic error in point precipitation measurement for operational use, World Meteorological Organization - Operational Hydrology, Report No. 21, 1982.

2. Pollock, M. D., et al. Quantifying and mitigating wind induced undercatch in rainfall measurements, Water Resources Research, 54, 2018.

3. Strangeways, Ian. Improving precipitation measurement. International Journal of Climatology. 24. 1443 - 1460. 10.1002/joc.1075, 2004.

4. Colli, M., et al.  A Computational Fluid-Dynamics Assessment of the Improved Performance of Aerodynamic Rain Gauges. Water Resources Research. 54. 10.1002/2017WR020549, 2018.

How to cite: Dutton, M. and Balsamo, D.: Improvements in rain gauge design and measurements to minimise under-catch errors, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8714, https://doi.org/10.5194/egusphere-egu24-8714, 2024.

EGU24-8789 | ECS | Orals | HS7.1

Merging personal weather stations with real-time radar rainfall estimates at the catchment scale 

Nathalie Rombeek, Markus Hrachowitz, Davide Wüthrich, and Remko Uijlenhoet

Real-time flood forecasting and warning during extreme rainfall events remains challenging since accurate and real-time available data are critical. Nowcasting based on radar rainfall can be utilized for this, as it has a high spatial and temporal resolution (i.e. typically 1 km and 5 min). However, the quantitative precipitation estimates (QPE) from the radar, upon which radar rainfall nowcasting is based, often contains substantial uncertainty and bias. While the QPE are usually corrected with official rain-gauge networks, these networks are sparse, and not always available in (near) real-time.

Instead, personal weather stations (PWS) can be used, as they have a much higher density and are available in real time. While PWS are prone to several sources of error, quality control algorithms can be used to improve their accuracy. Previous research already showed that merging quality controlled PWS with radar rainfall estimates reduces the underestimation for 1-hour accumulated rainfall at the pan-European scale. However, this has not yet been investigated at the catchment scale. This research aims to investigate the potential of merging PWS data with radar rainfall estimates for different catchments in the Netherlands, by considering multiple rainfall events starting from 2018. The goal is to quantify the performance in relation to rainfall type, quality control algorithms and catchment properties, validated against the climatological gauge-adjusted radar dataset from the KNMI. The insights obtained from this research have the potential to be utilized for real-time radar rainfall nowcasting and consequently flood forecasting.

How to cite: Rombeek, N., Hrachowitz, M., Wüthrich, D., and Uijlenhoet, R.: Merging personal weather stations with real-time radar rainfall estimates at the catchment scale, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8789, https://doi.org/10.5194/egusphere-egu24-8789, 2024.

EGU24-10819 | Orals | HS7.1

Spatial and temporal structure of normal and extreme rainfall 

András Bárdossy

The space time behaviour of precipitation is very complex. The knowledge of the dependence structures in space and time is very important for the assessment of flood risks. In this contribution the dependence structures of normal and extreme events are compared. Both rain gauges with high temporal resolution and radar images are investigated. Spatial and temporal copulas are used for this investigation. Due to the large number of zero observations, especially for short temporal aggregations an indicator approach is used to detect structural differences. The results show, that the temporal dependence structure of rainfall gradually changes with increasing intensity. Similar behaviour can be detected for the spatial structure with the addition of advection related differences in both ranges and angles of anisotropy. The findings indicate that metagaussian approaches which only consider spatial and temporal correlations are not appropriate for the description and the simulation of rainfall extremes. Finally a new structural simulation method using non-Gaussian dependence is presented.

How to cite: Bárdossy, A.: Spatial and temporal structure of normal and extreme rainfall, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10819, https://doi.org/10.5194/egusphere-egu24-10819, 2024.

EGU24-12007 | Orals | HS7.1

Revisiting nonterminal hydrometeors: Refining instrument uncertainty 

Michael Larsen, Andrei Vakhtin, and Anthony Gomez

The fall velocities of rain and drizzle drops are often assumed to be a deterministic function of their size. These diameter-fall speed relationships are intrinsically assumed in the retrievals provided by some commercial rain measurement instruments (e.g. the Joss-Waldvogel Disdrometer (Distromet), Micro Rain Radar (METEK), and 1-Dimensional Video Disdrometer (Joanneum Research)).

Some disdrometers are capable of independently measuring droplet size and fall-speed and provide evidence that not all drops adhere to the assumed size/fall-speed relationship. The ubiquity and magnitude of these deviations are still an area of some debate; clear identification of drizzle and rain drops falling at speeds different than their expected terminal fall velocities is muddied by conservative estimates of disdrometer resolution and performance. For a long time the bulk of observed non-terminal drop fall speeds were assumed to be instrumental artifacts and, even now, most investigators conclude drops falling at non-terminal speeds do not have a large impact on rain measurement science.

To date, uncertainties in disdrometer-derived drop sizes and fall speeds have usually been derived from the manufacturer estimates. Here, we improve on these estimates by using a field calibration source (the new ``Large Drop Generator'' from Mesa Photonics) that permits user-selectable generation of droplets with known sizes and fall speeds. From these data, empirical estimates of disdrometer sizing and fall velocity bias and uncertainty can be determined. This, then, allows for a more reliable estimate of the fraction of non-terminal drops in natural rain and a more reliable assessment of the impact of non-terminal drizzle and rain drops in data derived from instruments that assume a specific drop size/fall-speed relationship.

How to cite: Larsen, M., Vakhtin, A., and Gomez, A.: Revisiting nonterminal hydrometeors: Refining instrument uncertainty, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12007, https://doi.org/10.5194/egusphere-egu24-12007, 2024.

EGU24-12054 | Posters on site | HS7.1

Testing a new Radar QPE methodology for winter events with a low melting layer 

Raquel Evaristo, Ju-yu Chen, Alexander Ryzhkov, and Silke Trömel

The RY precipitation product of the German Weather Service (DWD) is severely affected by the presence of the low melting layer and frequently shows circular features of enhanced precipitation around the radar sites during the winter time. 
The radars tend to be installed at relatively high terrain and to scan at elevations at a minimum of 0.5° in order to avoid beam blockage and ground clutter. In doing so two problems arise:

1) the difference between the ground and the radar beams becomes a problem especially at large distances from the radar, and consequently precipitation processes in the lowest layers are not observed.
2) the radar beam often reaches the melting layer and may even cross it where it is sampling the snow above.As a result problems arise when deriving surface QPE from the radar: regions of enhanced QPE in ring shapes around the radar sites, and underestimation of the precipitation beyond the melting layer.

A new methodology (PVPR - Polarimetric Vertical Profile of Reflectivity) developed by Ryzhkov et al. 2022 is tested here for which the radar reflectivity (ZH) is reconstructed to correct for the effect of the melting layer and snow beyond. In this methodology the melting layer is detected independently for each azimuth based on the values of ZH and ρHV (cross-correlation coefficient between horizontal and vertically polarized radar waves). In particular the range bin at which the melting layer was reached is recorded (mlb_r). The strength of the melting layer (ML_S) is defined based on how much the value of ρHV  dropped within the melting layer. The values of ML_r and ML_S at a specific elevation are considered sufficient to characterize the melting layer, and are then compared with lookuptables which were generated by simulations of the melting layer effect on the radar beam. A correction factor is then applied based on the lookuptables to the ZH profile within and beyond the melting layer. Visually the result shows a smoother field of reflectivity without the obvious bright band and decreased values associated with snow at farther ranges.

In this study the PVPR methodology was used to correct ZH which in turn was used to calculate rain rates and rain accumulations in a few winter events in Germany.  The results show a strong improvement in the quality of the QPE when compared to rain gauges. The quality of the resulting QPE depends on the event and on the location of the radar. More specifically, the quality decreases when the melting layer is very low, at heights comparable to the radar height, and when the difference between the beam and the surface increases. These problems will be analyzed and potential solutions will be tested in order to improve the quality of the rainfall product.

Ryzhkov, Alexander, Pengfei Zhang, Petar Bukovčić, Jian Zhang, and Stephen Cocks. 2022. "Polarimetric Radar Quantitative Precipitation Estimation" Remote Sensing 14, no. 7: 1695. https://doi.org/10.3390/rs14071695 

How to cite: Evaristo, R., Chen, J., Ryzhkov, A., and Trömel, S.: Testing a new Radar QPE methodology for winter events with a low melting layer, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12054, https://doi.org/10.5194/egusphere-egu24-12054, 2024.

EGU24-12374 | ECS | Posters on site | HS7.1

Implications of the rainfall spatial variability for the real-time modeling of runoff triggering stony debris flows 

Mauro Boreggio, Matteo Barbini, Martino Bernard, Matteo Berti, Massimiliano Schiavo, Alessandro Simoni, Sandivel Vesco Lopez, and Carlo Gregoretti

In a mountainous environment, high-intensity and short-duration precipitation can generate sudden and abundant runoff at the base of rocky cliffs. This runoff, upon impacting the debris deposits present there, can trigger debris-flow phenomena. In the province of Belluno, in the Boite River valley, a network of rain gauges has been set up to monitor precipitation in the Rovina di Cancia site, where 12 debris-flow events have occurred in the last 10 years. The rain gauges are strategically placed both upstream and downstream of the debris-flow initiation area. In most cases, the precipitation showed significant spatial variability in both planimetric and altimetric aspects. This variability is crucial when simulating the runoff that triggers stony debris flows. The simulation of the peak runoff that triggered the 12 occurred events using a single rain gauge presented a high scatter compared to the simulation performed with the spatially recorded rainfall, except when the chosen rain gauge was close to the rocky cliffs. Furthermore, modelling using radar estimates as rainfall input also displayed significant variability based on the rain gauge used to correct the radar data. Essentially, accurate real-time simulation of runoff triggering debris flows requires the presence of rain gauges upstream of the initiation area, particularly in close proximity to the rocky cliffs.

How to cite: Boreggio, M., Barbini, M., Bernard, M., Berti, M., Schiavo, M., Simoni, A., Vesco Lopez, S., and Gregoretti, C.: Implications of the rainfall spatial variability for the real-time modeling of runoff triggering stony debris flows, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12374, https://doi.org/10.5194/egusphere-egu24-12374, 2024.

EGU24-13111 | ECS | Orals | HS7.1

Identification of wet and dry periods in commercial microwave link observations via information theory framework 

Anna Špačková, Martin Fencl, and Vojtěch Bareš

Commercial microwave links (CML) have already demonstrated their promising potential in rainfall observation and sensing. The CMLs enable indirect monitoring of path-averaged rainfall intensity as the transmitted signal is attenuated along the link path mainly by raindrops. However, the signal is also attenuated during dry weather periods and is affected by both atmospheric and hardware conditions. Faulty separation of wet and dry periods can easily lead to incorrect rainfall estimates and remains challenging to estimate due to irregular fluctuations of the attenuated signal.

This study aims to use information theory approach to estimate wet and dry periods in the CML signal attenuation observation, which is achieved by evaluating individual predictors and combinations of predictors. The method enables any data to be used as predictors without the need for parameters to describe relations between different variables, as the discrete probability distributions are applied. The model that provides the strongest information content to the wet and dry classification is binarized using an optimized threshold and validated. Thiesen et al. (2019) recently applied this approach to identify rainfall-runoff events in discharge timeseries.

Data of non-winter periods between 2014 and 2016 are used with a temporal resolution of 1 minute. For one CML in the Prague network, wet and dry periods were defined manually as reference (target). Predictors included raw CML data (signal attenuation), as well as derived timeseries such as signal attenuation shifted in time, relative magnitude of attenuation, gradient of the signal attenuation and signal deviation. In addition, external predictors such as temperature deviation, rain gauge precipitation observations or synoptic types are used as additional predictors.

By selecting different predictors, it is possible to compare effectiveness in estimating the reference wet and dry periods. Variation in the strength of the relations between the target and the predictors allows ranking the suitability of available predictors and their combinations for the task. Subsequently, having the best performing predictor, it is combined with others and their collective performance was iteratively evaluated to find the most accurate combination of three predictors described in a multidimensional discrete distribution model. The resulting predictor combination was then converted into binary form and validated. A method comparison is performed with separation of constant and moving average baseline attenuation for wet periods identification as well as wet/dry classification using a threshold for rolling standard deviation of the signal.

Having sufficient data amount for data-driven models enables utilizing the relationships within the dataset without being limited by parametric or operational assumptions, which are often embedded part of wet/dry in classification methods.

References
Thiesen, S., Darscheid, P., and Ehret, U.: Identifying rainfall-runoff events in discharge time series: a data-driven method based on information theory, Hydrol. Earth Syst. Sci., 23, 1015–1034, https://doi.org/10.5194/hess-23-1015-2019, 2019.

This work was supported by the Grant Agency of the Czech Technical University in Prague, grant no. SGS23/048/OHK1/1T/11.

How to cite: Špačková, A., Fencl, M., and Bareš, V.: Identification of wet and dry periods in commercial microwave link observations via information theory framework, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13111, https://doi.org/10.5194/egusphere-egu24-13111, 2024.

EGU24-14062 | Posters on site | HS7.1

Upward transport in a canopy assisted by raindrop impacts on plant leaves 

Tristan Gilet and Loïc Tadrist

The interception of raindrops by plant leaves induces a redistribution of water, nutrients, and micro-organisms, from the surface of these leaves to their surroundings. It consequently shapes the plant ecosystem. For example, in wheat fields (as in most major crops), splashing raindrops are the main mechanism of spore dispersal for fungal diseases at the epidemic stage, with severe consequences on crop yield. Surprisingly, the observed dispersal is not only downward (wash off / dripping) or outward (splash), but also upward, which may considerably speed up the fungus propagation. Other nutrients and microorganisms might also benefit from such upward transport external to the plant.

In this work, we unravel an efficient and universal mechanism of upward transport: after a raindrop splashed on a plant leaf, the residual water on the leaf can be shot upward as the leaf springs back. We illustrate this phenomenon with several plant leaves. Then we present results obtained from systematic experiments with artificial leaves, thanks to which both the mechanics of rain-induced leaf motion and the fluid dynamics of leaf-induced droplet ejections are elucidated. We identify the range of mechanical properties of the leaf that makes upward shooting fully effective. Finally, we show that the efficiency of this upward transport increases more than proportionally with rain intensity. Its occurrence and role in shaping ecosystems will be largely amplified in the case of an increased frequency of extreme rain events.

How to cite: Gilet, T. and Tadrist, L.: Upward transport in a canopy assisted by raindrop impacts on plant leaves, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14062, https://doi.org/10.5194/egusphere-egu24-14062, 2024.

EGU24-16024 | ECS | Orals | HS7.1

Quantifying precipitation intermittency for Bergen, Norway, from measurements and models across a wide range of time scales 

Ingrid O. Bækkelund, Mari B. Steinslid, and Harald Sodemann

Intermittency of rainfall is an important property, for example in the context of urban flooding. There is currently a lack of information about the ability of numerical weather prediction models to represent precipitation intermittency for different weather situations, in particular at high resolution in space and time. Here we present a new way to quantify rainfall intermittency based on a near-continuous, high-resolution precipitation dataset from Bergen, Norway, one of the rainiest cities in Europe. 

We quantify precipitation intermittency from a precipitation dataset acquired at the Geophysical Institute, Bergen, spanning the period 2019-2022 at a 1 min time resolution. Precipitation rates were obtained from a Total Precipitation Sensor TPS-3100 (Yankee Environmental Systems Inc., USA) and a Parsivel2 disdrometer (OTT Hydromet GmbH, Germany). In addition, we use precipitation output at 1 min resolution from the regional high-resolution weather forecasts model HARMONIE-AROME for selected events. Precipitation intermittency is then identified for a range of minimum inter-event times (MIT) from 1 min to 24 h, and precipitation event durations from 1 min to 33 days. Next, the precipitation events for different intermittencies are related to average meteorological characteristics during the events with respect to air temperature, pressure, wind speed, rain rate and amount, and corresponding weather regimes.  

We compile the intermittency information into a 2-dimensional heat map that can be considered as a characteristic fingerprint for precipitation in Bergen. Particular frequency maxima and minima appear to be related to different precipitation processes and weather regimes. A scale gap between 30 min and 2 h event duration for MIT larger than 12 h indicates that separate factors control precipitation processes at these time scales. Weather regimes show a clear influence on the precipitation characteristics, with a markedly higher probability for long-duration rain events in the zonal flow regime for longer event durations at high MITs compared to the Scandinavian trough regime. A comparison between precipitation intermittency simulated by HARMONIE-AROME shows reasonable agreement with observed event characteristics for events lasting more than 1h, while events with durations of 30 min and less are poorly represented. 

How to cite: Bækkelund, I. O., Steinslid, M. B., and Sodemann, H.: Quantifying precipitation intermittency for Bergen, Norway, from measurements and models across a wide range of time scales, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16024, https://doi.org/10.5194/egusphere-egu24-16024, 2024.

EGU24-17471 | Orals | HS7.1

Wind-induced bias of catching-type precipitation gauges and their overall collection efficiency 

Luca G. Lanza, Arianna Cauteruccio, and Enrico Chinchella

In windy conditions, the measurement of liquid and solid atmospheric precipitation is still a challenge even using the most advanced automatic instrumentation (Cauteruccio et al., 2021). The measurement accuracy is affected by various environmental sources of bias, including siting issues and exposure. These add to the instrumental bias, which can be minimized in case of accurate instrument calibration. Wind is however recognised as the most impactful source of environmental bias, outperforming by 3 to 50 times the total impact of all other environmental factors.

Computational Fluid Dynamics simulation with embedded liquid (raindrops) and solid (snowflakes) particle tracking is here used to quantify the wind-induced bias of catching-type precipitation gauges. Starting from the numerically calculated catch ratios, six common commercial gauges having different outer geometry are compared in terms of their expected performance under various precipitation intensity and wind speed conditions. Preliminary wind tunnel experiments allowed full validation of the simulated aerodynamic behaviour and its effect on water drop trajectories.

The overall collection efficiency is shown to depend on the precipitation intensity and its functional dependence is quantitatively derived as a measure of the instrument performance under a wind climatology characterised by a uniform probability density function. A less pronounced diversion of hydrometeor trajectories is shown – at any given size – by instruments with aerodynamic design than in case of more traditional geometry.

Chimney-shaped instruments rank low in case of liquid precipitation measurements, while a high performance is shown by inverted conical and Nipher shielded instruments and the investigated quasi-cylindrical gauges have intermediate behaviour, which depends on their specific aerodynamic features. All instruments rank low at light to moderate precipitation intensity for the measurement of solid precipitation, except the Nipher shielded gauge.

This work provides the basic information needed to apply adjustments to the measured data and supports manufacturers in upgrading instruments with an existing design by introducing on-board adjustments of the measured precipitation. These would only require contemporary measurement of the wind velocity (often included in typical meteorological stations). The full work and the numerically derived adjustments for the six investigated commercial gauges are published in Cauteruccio et al. (2024).

References

Cauteruccio, A., Colli, M., Stagnaro, M., Lanza, L.G. & Vuerich, E. (2021). In situ precipitation measurements. In T. Foken (Ed.), Handbook of Atmospheric Measurements (359-400). Switzerland, Springer Nature. ISBN 978-3-030-52170-7, https://doi.org/10.1007/978-3-030-52171-4_12.

Cauteruccio, A., Chinchella, E. and L.G. Lanza (2024). The overall collection efficiency of catching-type precipitation gauges in windy conditions. Water Resour. Res., in press. https://doi.org/10.1029/2023WR035098.

How to cite: Lanza, L. G., Cauteruccio, A., and Chinchella, E.: Wind-induced bias of catching-type precipitation gauges and their overall collection efficiency, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17471, https://doi.org/10.5194/egusphere-egu24-17471, 2024.

EGU24-18921 | Orals | HS7.1

Unveiling the Geodetic Distribution of Temporal Characteristics in Rainstorm Events across Republic of Korea 

Hoyoung Cha, Jongjin Baik, Hyeon-Joon Kim, Jinwook Lee, Jongyun Byun, and Changhyun Jun

Abstract

This study analyzed geodetic distribution about temporal characteristics in rainstorm (> 1 hour) observed at approximately 600 rainfall stations across Republic of Korea. Utilizing minute-scale precipitation data observed by rainfall stations from 2000 to 2022, independent rainstorm events separated from rainfall data per unit time (i.e., 10, 20, 30, and 60 minutes) and Inter-Event Time Definition (IETD) (i.e., 2, 3, 4, and 6 hours). The significant variations in rainfall characteristics are defined as the number of independent rainstorm events, rainfall duration (hour), amount (mm), and intensity (mm/hour) for quantifying the temporal characteristics across rainfall stations. We quantified temporal characteristics among rainfall characteristics observed by rainfall stations based on latitude and longitude. The number of independent rainstorm events varies significantly depending on unit time and IETD, and the occurrence of events was frequently observed in areas characterized by island features. The rainfall amount for independent rainstorm events obscured significant characteristics, excluding Halla Mountain on Jeju Island. The geodetic distribution for the duration and intensity per rainstorm event varied depending on the characteristics of the region (i.e., island, mountain, etc.). Based on these results, it was confirmed that certain temporal characteristics vary according to regional features. In future research, we intend to utilize this information to cluster rainfall stations based on temporal characteristics.

Keywords: Independent Rainstorm Events, Temporal Characteristics, Geodetic Distribution, Regional Features, Republic of Korea

Acknowledgment

This research was supported by Korea Environment Industry & Technology Institute (KEITI) funded by Korea Ministry of Environment (RS-2022-KE002032 and 2022003640001) and was also supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2022R1A4A3032838 and No. RS-2023-00250239).

How to cite: Cha, H., Baik, J., Kim, H.-J., Lee, J., Byun, J., and Jun, C.: Unveiling the Geodetic Distribution of Temporal Characteristics in Rainstorm Events across Republic of Korea, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18921, https://doi.org/10.5194/egusphere-egu24-18921, 2024.

EGU24-20231 | Posters on site | HS7.1

A new method for disaggregating path-averaged rain rates from commercial microwave links 

Martin Fencl and Marc Schleiss

Commercial microwave links (CMLs) serve as point-to-point radio connections in cellular backhaul and offer a promising way to measure rainfall opportunistically. Raindrops along the CML path attenuate electromagnetic waves, allowing the conversion of this attenuation into path-averaged rain rates. Wide coverage of CML networks, high density in urban areas, and cost-effective operation present clear advantages over traditional rain gauges and radar networks. However, the integrated nature of CML data poses a challenge. When transforming this data into spatially representative rainfall estimates, such as 2D maps, path-integrated rain rates need to be converted into point data and interpolated to a regular two-dimensional Cartesian grid. The most direct method involves reducing each CML observation to a single-point measurement at the path's center, followed by interpolation using techniques like kriging or inverse distance weighted (IDW) interpolation. Yet, past studies indicate that for longer CMLs (several kilometers) and intense localized rain showers, this approach can introduce significant biases and unrealistic rainfall distributions due to the substantial spatial and temporal variability of rainfall.

In this contribution, we introduce a new disaggregation method employing random cascades. The method redistributes rainfall amounts along CML paths across progressively smaller scales using a discrete, conservative multiplicative random cascade. Inspired by the EVA (Equal-volume area) cascade developed by Schleiss (2020) for disaggregating spatially intermittent rainfall fields, our approach involves splitting each CML segment into two new segments with different path-lengths but identical path-integrated rainfall. We call this new method CLEAR (CML segments with equal amounts of rain). CLEAR is tested for CML network of 77 CMLs located in Prague, CZ. First, the disaggregation is evaluated using simulated CML observations and, second, CML rain rates derived from real attenuation data.

Our findings demonstrate that CLEAR surpasses reconstruction algorithms that reduce CML observations into a single point. It accurately replicates the highly diverse rainfall distributions observed along CMLs, including their intermittency. Moreover, the stochastic nature of the cascade enables the quantification of uncertainty associated with the spatial redistribution of rainfall rates along CMLs.

References

Schleiss, Marc. “A New Discrete Multiplicative Random Cascade Model for Downscaling Intermittent Rainfall Fields.” Hydrology and Earth System Sciences 24, no. 7 (July 23, 2020): 3699–3723. https://doi.org/10.5194/hess-24-3699-2020.

How to cite: Fencl, M. and Schleiss, M.: A new method for disaggregating path-averaged rain rates from commercial microwave links, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20231, https://doi.org/10.5194/egusphere-egu24-20231, 2024.

EGU24-20898 | Posters on site | HS7.1

Comparative analysis of rainfall characteristics for two distinct research plots 

Jürgen Komma, Borbala Szeles, Katarina Zabret, Mojca Šraj, and Juraj Parajka

In natural environments, rainfall causes soil erosion, which has a significant impact on the agricultural production and the ecological conditions of the streams. Due to different types of vegetation, their unique characteristics and seasonality, there are still a lot of open scientific questions about how rainfall interception process influences the rainfall erosivity and soil erosion. With the aim of improving knowledge about rainfall interception by different vegetation and its impact on the rainfall erosivity, an interdisciplinary and international research team (Faculty of Civil and Geodetic Engineering at the University of Ljubljana, Slovenian Forestry Institute and Technical University of Vienna) work together in the research project entitled “Evaluation of the impact of rainfall interception on soil erosion”. In the scope of the project, drop size distribution measurements above and below selected plants will be conducted in combination with classical measurements of rainfall partitioning. The measurements are ongoing in the small urban park in Ljubljana, Slovenia and in the experimental catchment with mainly agricultural land use in Lower Austria (The Hydrological Open Air Laboratory HOAL in Petzenkirchen). To evaluate the differences in rainfall characteristics for the two research plots, a comparative analysis on rainfall event properties such as rainfall amount, duration and intensity, size and velocity distribution of raindrops is performed. The aim of the presentation is to introduce the project and presents the first comparison of the rainfall characteristics at research plots in Austria and Slovenia.

Acknowledgments: This contribution is part of the ongoing research project entitled “Evaluation of the impact of rainfall interception on soil erosion” supported by the Slovenian Research and Innovation Agency (project J2-4489) and the Austrian Science Fund (FWF) I 6254-N.

How to cite: Komma, J., Szeles, B., Zabret, K., Šraj, M., and Parajka, J.: Comparative analysis of rainfall characteristics for two distinct research plots, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20898, https://doi.org/10.5194/egusphere-egu24-20898, 2024.

Understanding historical evolution and future projections of drought are crucial for Madagascar, which experiences drought almost every year. Not only it contributes to the economic development of the area but it also helps to mitigate direct and indirect impacts of drought on human’s lives and natural ecosystems. To begin with, it is crucial to use accurate datasets for assessing drought in order to get reliable findings. However, Madagascar lacks reliable station datasets. Here, we present the first evaluation of performance of available observed precipitation datasets over the country: gridded precipitation datasets from gauge-based, reanalysis and satellite estimates. Among the 15 analyzed datasets, CHIRPS (Climate Hazards Group Infrared Precipitation with Station data version 2) and ERA5 (European Centre for Medium-Range Weather Forecasts reanalysis fifth generation- Land dataset) the lowest biases compared to the rest. Thus, they are used as the reference for evaluating the performance of CMIP6 HighResMIP simulations. The assessment employs diverse methods, accompanied by the use of the Taylor skill score for ranking the overall performance of the models. The results show that EC-Earth3P-HR, ECMWF-IFS-HR, ECMWF-IFS-LR and HadGEM3-GC31-MM perform the best. The evaluated precipitation datasets are used in current ongoing research of recent drought evolution and its impact on vegetation over Madagascar. Preliminarily results show that the SPI (Standard Precipitation Index) exhibit decreasing trend for all chosen SPI scales (SPI3, SPI6 and SPI12). This indicates that the occurrence of drought over Madagascar has amplified within the study period of 1981 to 2022. Eventually, the evaluation of future projections of drought over the Island would be the next goal to be tackled in order to provide bases for planning appropriate measures in lessening the impact of drought, building effective adaptation strategies and structuring climate change policies.

How to cite: Randriatsara, H. H.-R. H. and Holtanova, E.: Precipitation over Madagascar: Assessment of observed datasets and CMIP6 HighResMIP models for further analysis of drought and its impact on vegetation , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-199, https://doi.org/10.5194/egusphere-egu24-199, 2024.

EGU24-527 | ECS | Orals | AS1.10

Diurnal Variability of Global Precipitation: Insights from Hourly Satellite and Reanalysis Datasets 

Rajani Kumar Pradhan, Yannis Markonis, and Francesco Marra

Accurate estimation of precipitation at the global scale is of utmost importance. Even though satellite and reanalysis products are capable of providing high spatial-temporal resolution estimations at the global level, their uncertainties vary with regional characteristics, scales, and so on. The uncertainties among the estimates, in general, are much higher at the sub-daily scale compared to daily, monthly and annual scales. Therefore, quantifying these sub-daily estimations is of specific importance. In this context, this study seeks to explore the diurnal cycle of precipitation using all the currently available space-borne and reanalysis-based precipitation products with at least hourly resolution (IMERG, GSMaP, CMORPH, PERSIANN, ERA5) at the quasi-global scale (60N - 60S). The diurnal variability of precipitation is estimated using three parameters, namely, the precipitation amount, frequency, and intensity, all remapped at a common resolution of 0.25 and 1 h. All the estimates well represent the spatio-temporal variation across the globe. Nevertheless, considerable uncertainties exist in the estimates regarding the peak precipitation hour, as well as the diurnal mean precipitation amount, frequency, and intensity. In terms of diurnal mean precipitation, PERSIANN shows the lowest estimates compared to the other datasets, with the largest difference observed over the ocean rather than over land. As for diurnal frequency, ERA5 exhibits the highest disparity among the estimates, with a frequency twice as high as that of the other estimates. Furthermore, as expected being based on model reanalysis, ERA5 shows an early diurnal peak and the highest variability compared to the other datasets. Moreover, among the satellite estimates, IMERG, GSMaP, and CMORPH exhibit a similar pattern with a late afternoon peak over land and an early morning peak over the ocean.

How to cite: Pradhan, R. K., Markonis, Y., and Marra, F.: Diurnal Variability of Global Precipitation: Insights from Hourly Satellite and Reanalysis Datasets, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-527, https://doi.org/10.5194/egusphere-egu24-527, 2024.

EGU24-736 | ECS | Posters virtual | AS1.10

Regional Variation in Precipitation characteristics observed by space-borne Precipitation Radar  

Amit Kumar, Atul Kumar Srivastava, and Manoj Kumar Srivastava

Dual-frequency precipitation radar (DPR) placed on the Global Precipitation Measurement (GPM) satellite provides a three-dimensional distribution of precipitation between 650N- 650S. The availability of precipitation parameters at the spatial resolution of 0.10*0.10 and temporal resolution of 30 minutes can be used to investigate the microphysical process responsible for the precipitation. We analyzed the GPM-DPR level 2 V07 observed data collected over the Southern region of India and the surrounding Oceanic region to understand the precipitation characteristics in the pre-monsoon and monsoon seasons. India Meteorological Department (IMD) gridded rainfall data at the resolution 0.250 is used to validate GPM-DPR data over the landmass region. There is significant variation in the temporal and spatial distribution of reflectivity (Z), rain rate (R), and DSD parameters such as mass-weighted mean diameter (Dm) and normalized intercept parameter (Nw). In the monsoon season, higher precipitation frequency provides considerable accumulated precipitation throughout India. However, the frequency of intense rainfall is higher in the pre-monsoon season than in the monsoon season, as most of rain events occur over the Ocean instead of land. The mean of R, Z, and Dm is small, and a large Nw value is observed in the monsoon season, as stratiform clouds (more than 68%) contribution in monsoon precipitation is more than convective clouds. The distribution of average Dm, Z, and R in pre-monsoon indicates the presence of bigger rain droplets, possibly due to the enhancement in the collision-coalescence process and slow-down of the break-up process. The share of convective clouds in overall precipitation on the land surface increased in the pre-monsoon season. The fluctuation in Dm not only occurs with topography, season, and R, but also with the concentration of heavy ice precipitation particles above the bright band and microphysical process. Simultaneously, in both pre-monsoon and monsoon seasons, a modest relationship was detected between the incidence of heavy precipitation and maximum echo top reflectivity.

How to cite: Kumar, A., Srivastava, A. K., and Srivastava, M. K.: Regional Variation in Precipitation characteristics observed by space-borne Precipitation Radar , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-736, https://doi.org/10.5194/egusphere-egu24-736, 2024.

EGU24-1520 | ECS | Orals | AS1.10

Sensitivity to the vertical structure of hydrometeors using Polarimetric RO 

Antía Paz Carracedo, Ramon Padullés Rulló, and Estel Cardellach Galí

The Global Navigation Satellite System (GNSS) Radio Occultation (RO) technique sounds the atmosphere providing high quality vertical profiles of the thermodynamics on a global scale. The Polarimetric RO (PRO) technique is an extension of traditional RO that retrieves precipitation information in addition to the standard thermodynamic products. The technique has been demonstrated aboard the Spanish Low Earth Orbiter (LEO) PAZ, as part of the Radio Occultation and Heavy Precipitation (ROHP) experiment led by the Institut de Ciències de l’Espai (ICE-CSIC/IEEC) in collaboration with NOAA, UCAR, and NASA/Jet Propulsion Laboratory. This mission enables the investigation of intense precipitation events and their associated meteorological conditions by retrieving atmospheric thermodynamic variables and offering insights into the vertical structure of precipitation.

The determination of the vertical structure is accomplished through the observable differential phase shift (ΔΦ), defined as the difference in the accumulated phase delay between the two linear polarizations (H-V) as function of the tangent point of the PRO rays. During intense precipitation events certain challenges arise in obtaining high-quality measurements of thermodynamic parameters due to signal attenuation. However, the PRO technique is less affected by attenuation, presenting an opportunity to obtain high-resolution thermodynamic profiles and information about the vertical structure of hydrometeors, simultaneously.

Validation of the PRO technique with two-dimensional data has been successfully conducted using the Global Precipitation Measurement (GPM) mission gridded products (like Integrated Multi-satellitE Retrievals for GPM, IMERG). In this analysis, vertical structure validation has been performed using data from the Next Generation Weather Radars (NEXRAD), a network of dual-polarized Doppler radars operating at the S-band, covering the entire United States territory. By exploiting the dual-polarization capabilities of NEXRAD, a comparison of the specific differential phase shift (KDP) structures with the PRO observable ΔΦ aids in examining similarities and differences in the detection of precipitation between the two instruments.

Furthermore, to explore the sensitivity of the PRO technique to various types of hydrometeors, the Weather Research and Forecasting-Advanced Research Weather Model (WRF-ARW) is employed for a comparative analysis, focusing on hydrometeor water contents. The variation of the model’s microphysics parametrizations allows for the study of the PRO technique’s sensitivity based on different assumptions about hydrometeors. Changes in these parametrizations impact total precipitation, vertical structure of hydrometeors, cloud properties, energy budget, spatial structure, among others. The validation and sensitivity study of the PRO technique will contribute to an enhanced understanding of the observables obtained and will offer insights into the phenomena characterizing intense precipitation situations.

How to cite: Paz Carracedo, A., Padullés Rulló, R., and Cardellach Galí, E.: Sensitivity to the vertical structure of hydrometeors using Polarimetric RO, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1520, https://doi.org/10.5194/egusphere-egu24-1520, 2024.

EGU24-1983 | ECS | Orals | AS1.10

Hydrometeor classification using dual-polarized C-band Doppler weather radars: comparison to a dual-polarization Doppler profiler 

Linda Bogerd, Hidde Leijnse, Aart Overeeem, Remko Uijlenhoet, and Sibbo van der Veen

The innovation of dual-polarization Doppler weather radars has improved the accuracy of precipitation estimates over the past decades. Retrieving hydrometeor types from dual-polarization weather radar data, however, remains challenging. In this study, we used a hydrometeor classification scheme from wradlib to identify hydrometeor types aloft from two C-band weather radars in the Netherlands. Four recent case studies, from 2022 and 2023, were selected. A dual-polarization Doppler profiling radar, operating at Ka-band and W-band at an elevation angle of 45 degrees, was employed as a reference. First, the output of the wradlib scheme was used to determine the hydrometeor type. Based on this classification, we selected computed scattering properties from the open access ARTS Microwave Single Scattering Properties Database. Furthermore, mixing ratios of the hydrometeors were computed by combining measured C-band reflectivities using the hydrometeor type probabilities from wradlib. The hydrometeor type determines the scattering behavior of a single precipitation particle while the mixing ratio prescribes the particle size distribution (PSD), which is determined using parametrizations as employed in the Harmonie weather model. With the PSD and the hydrometeors’ terminal fall speeds, which are also taken from Harmonie, we produced spectra of various polarimetric variables that could be compared to those derived from the profiling radar. Besides incorrect classifications resulting from the wradlib algorithm, differences between constructed and observed spectra stem from various uncertainties associated with the retrievals from the profiler. Firstly, the hydrometeor canting angle distribution affects the backscattering to the radar. Secondly, the PSD parametrizations as employed in HARMONIE have been employed, while numerous alternatives exist that could yield different results. Finally, uncertainties are associated with the conversion of 45-degree measurements from the profiling radar to vertically-pointing spectra. Nonetheless, this study offers important insights into the performance of dual-polarization C-band weather radars regarding the classification of hydrometeor types.

How to cite: Bogerd, L., Leijnse, H., Overeeem, A., Uijlenhoet, R., and van der Veen, S.: Hydrometeor classification using dual-polarized C-band Doppler weather radars: comparison to a dual-polarization Doppler profiler, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1983, https://doi.org/10.5194/egusphere-egu24-1983, 2024.

EGU24-2024 | Orals | AS1.10

Global Snowfall as Revealed by High Resolution Satellite Precipitation Products 

Lisa Milani, Jackson Tan, and George J. Huffman

The Integrated Multi-satellitE Retrievals for GPM (IMERG) product and the Global Precipitation Climatology Project (GPCP) product are two global precipitation datasets that also provide a diagnostic estimate of the probability of precipitation phase, thus enabling a quantification of snowfall rates. With recent improvements to the latest versions of the two algorithms, IMERG V07B and GPCP V3.2 represent a unique opportunity to study the global snowfall rates at an unprecedented resolution.

This presentation examines the distribution of snowfall in IMERG V07B and GPCP V3.2 both globally and regionally. By leveraging IMERG’s high resolution and GPCP’s consistent record, we investigate the climatology not just from a snowfall volume point of view but also from peak snowfall intensity and snow event duration perspectives that only high-resolution data can provide. To assess the reliability of the results, we compare the IMERG and GPCP snowfall against global observations from CloudSat. For example, the comparison revealed deficiencies in passive microwave retrievals of snowfall rates in IMERG over Greenland and Antarctica. Furthermore, we leverage IMERG’s half-hourly resolution to demonstrate its unprecedented potential in tracking snowfall events around the globe.

With the latest advances in the algorithms, IMERG V07 and GPCP V3.2 represent a unique opportunity to study snowfall globally using a combination of fine resolution, complete global coverage, and long record.

How to cite: Milani, L., Tan, J., and Huffman, G. J.: Global Snowfall as Revealed by High Resolution Satellite Precipitation Products, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2024, https://doi.org/10.5194/egusphere-egu24-2024, 2024.

EGU24-2149 | Orals | AS1.10 | Highlight

Status and Developments in NASA GPM  

George Huffman

The joint U.S.-Japan Global Precipitation Measurement (GPM) mission is approaching a decade of operations, and continues to pursue research, dataset production, and outreach related to precipitation.  Key activities over the last year were the release of an improved “Version 07” of all GPM precipitation and latent heating products, boosting the orbit of the GPM Core Observatory (GPM CO) to 435 km, and improving quality control on precipitation retrievals from the GPM constellation of passive microwave satellites.

This presentation summarizes key improvements to the GPM products and provides some examples of the changes between Versions 06 and 07 in algorithm performance.  One important operational change that affected Version 07 is that the scanning strategy for the Ka-band radar channel changed in May 2018; all products that depend on Ka were revised to accommodate this change.  For example, in Version 07 the Goddard Profiling (GPROF) algorithm has implemented improvements in regions where orographic enhancement and suppression take place and where the surface is snowy/icy, and again covers radiometers reaching back to 1987.  The Combined Radar Radiometer Algorithm (CORRA) now incorporates modified drop-size distribution constraints that substantially reduce bias.  Revisions to the Convective-Stratiform Heating (CSH) algorithm employ new radiative transfer retrievals as well as accounting for terrain in the vertical coordinates.  Each algorithm was adjusted to ensure continuity for each product across the boundary in 2014 between the predecessor Tropical Rainfall Measuring Mission (TRMM) and the GPM CO.  The U.S. Science Team’s Integrated Multi-satellitE Retrievals for GPM (IMERG) was upgraded to account for distortions in the probability density function of regional precipitation rates due to weighted averaging in the Kalman filter used for “morphing” the passive microwave data.

Maintaining the GPM CO orbital altitude in the the current very active solar cycle has been forcing the use of more fuel than planned and consequently shortening the forecasted life of the mission from the early 2030's to the late 2020's.  It was considered vital to regain some of this lifetime to ensure overlap with the upcoming Atmosphere Observing System mission to provide cross-calibration of instruments.  To accomplish this, the orbital altitude was raised from 400 to 435 km on 7-8 November 2023.  Thereafter, the primary GPM CO algorithms had to be revised to account for the change in observing parameters.  By meeting time this action should be complete.

Recently, a screening algorithm based on auto-encoding was developed that uncovered 162 orbits (out of the many thousands of orbits across all years and all satellites) of passive microwave retrievals that had highly anomalous values.  Removing these defective retrievals has improved the integrity of both the GPROF and IMERG records.  However, the nature of the IMERG processing interacted sufficiently badly with the now-discovered anomalous orbits that it was necessary to completely reprocess the IMERG Final Run record, now labeled Version 07B.

The presentation also considers major issues that require continued attention, including the use of machine learning algorithms and the operational challenge of swarms of “small”, perhaps short–lived satellites.

How to cite: Huffman, G.: Status and Developments in NASA GPM , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2149, https://doi.org/10.5194/egusphere-egu24-2149, 2024.

EGU24-2207 | ECS | Posters on site | AS1.10

Effects of Cloud Seeding on Air Quality and Particulate Matter Dynamics: United States, China, and United Arab Emirates case studies 

Marya Al Homoud, Stephan Macko, and Ashraf Farahat

Space-Borne and ground-based data are used to investigate the environmental effects of cloud seeding on air quality and Particulate Matter (PM2.5 and PM10) dynamics. Seven sites in United States (Texas, Wyoming, California, Idaho, Utah, Nevada, and Montana), two sites in China(Henan and Fujian Gutian), and one site in the United Arab Emirates (Abu Dhabi) are considered for this work. Long-terms statistical analysis of aerosol optical depth (AOD), Ångström exponent(AE), precipitation, and particulate matter is performed. Meanwhile, meteorological data including temperature, humidity, pressure, and wind speed/direction are analyzed. Air quality conditions before, during, and after cloud seeding missions are tested using ground monitoring stations. Data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Terra were also used to perform a statistical correlation between aerosol optical depth (AOD) and ground PM observation. An increase in PM concentration was observed during cloud seeding missions’ period, which indicates a possible effect of silver iodide crystals fired during the missions in increasing the concentration of PM in air. The study found that cloud seeding missions have a possible effect on increasing PM10 compared to PM2.5 concentration, which point to the possible effect of meteorological conditions on washing out silver iodide particles fired during the missions.

 

How to cite: Al Homoud, M., Macko, S., and Farahat, A.: Effects of Cloud Seeding on Air Quality and Particulate Matter Dynamics: United States, China, and United Arab Emirates case studies, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2207, https://doi.org/10.5194/egusphere-egu24-2207, 2024.

EGU24-4232 | Posters on site | AS1.10

Early evaluation of effects on Dual-frequency Precipitation Radar observations by the orbit boost of the GPM Core Observatory  

Takuji Kubota, Takeshi Masaki, Gennosuke Kikuchi, Masato Ito, Tomohiko Higashiuwatoko, Kaya Kanemaru, Nobuhiro Takahashi, Kosuke Yamamoto, Kinji Furukawa, and Tomomi Nio

The NASA and the JAXA performed orbit boost maneuvers in November 2023 that raised an altitude of the Global Precipitation Measurement (GPM) Core Observatory from 400 km to 435 km to extend its lifetime. Effects of the orbit boost on the spaceborne precipitation radar have been investigated in the Tropical Rainfall Measuring Mission (TRMM) performed in August 2001. This study evaluates effects on DPR observations due to the GPM orbit boost.

Firstly, spacecraft altitudes of the GPM Core Observatory were analyzed during the period from 13rd October to 17th November 2023. The minimum altitudes were changed from about 400 km to about 435 km by the orbit boost. The averaged altitudes were changed from about 407 km to about 442 km by it. Thus, 407km and 442km were adopted as typical averaged satellite altitudes in pre-boost and the post-boost, respectively.

Spatial resolution at the nadir and swath width is changed at 5.04km×5.04km and 255.8 km at satellite altitude of 407 km to 5.48km×5.48km and 277.9 km at satellite altitude of 442 km, respectively.  Distances between adjacent footprints in the cross-track direction between the pre-boost and the post-boost using observation data and they confirmed that changes of the sampling were larger in the cross-track direction (about 5 km to 5.5 km at the nadir).

It was found that the DPR coverage tendency was changed by the GPM orbit boost. In pre-boost, DPR achieved 100% coverage in 8 days. On the other hand, with post-boost, the coverage was still 99.9834% after 24 days, slightly less than 100%. This coverage trend is expected to change with satellite maneuvers. The maneuver is expected to change the orbit elements, thereby covering all locations.

The sensitivity degradation of the DPR is expected owing to the increase of satellite altitude. Measured radar reflectivity factor (Zm) at storm top height (STH) over the ocean for is used as an indicator of the sensitivity. With analyzing Zm at STH over the ocean, the sensitivity degradation was found for about 0.8-0.9dB for KuPR, and about 0.7-0.9dB for KaPR.

How to cite: Kubota, T., Masaki, T., Kikuchi, G., Ito, M., Higashiuwatoko, T., Kanemaru, K., Takahashi, N., Yamamoto, K., Furukawa, K., and Nio, T.: Early evaluation of effects on Dual-frequency Precipitation Radar observations by the orbit boost of the GPM Core Observatory , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4232, https://doi.org/10.5194/egusphere-egu24-4232, 2024.

A Multiscale Analysis of a Nocturnal Extreme Rainfall Event of 14 July 2017 in Northeast China

 

Gaili Wang1, Da-Lin Zhang2,1, and Jisong Sun1

1State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Science, Beijing, China

46 South Street Zhongguancun, Beijing, China 100081

2 Department of Atmospheric and Oceanic Science, University of Maryland, College Park, College Park, Maryland

 Abstract

A multiscale observational analysis of a nocturnal extreme rainfall event that occurred at Changtu in Northeast China on 14 July 2017 is performed using global analysis, automated surface observations, Doppler radar, rawinsonde and disdrometer data. Results show that the large-scale environment was characterized by high convective available potential energy and precipitable water, moderate convective inhibition, and a southwesterly low-level jet (LLJ) capped by an inversion layer. The first and subsequent convective cells developed along a quasi-stationary surface convergence zone in a convection-void region of a previously dissipated meso-a-scale convective line. Continuous convective initiation through backbuilding at the western end and the subsequent merging of eastward-moving convective cells led to the formation of a near-zonally oriented meso-b-scale rainband, with reflectivity exceeding 45 dBZ (i.e., convective core intensity). This quasi-stationary rainband was maintained along the convergence zone by the LLJ of warm-moist air, aided by local topographical lifting and convectively generated outflows. A maximum hourly rainfall amount of 96 mm occurred during 0200-0300 BST as individual convective cores with a melting layer of >55 dBZ reflectivity moved across Changtu with little intermittency. The extreme-rain-producing stage was characterized with near-saturated vertical columns, and rapid number concentration increases of all raindrop sizes. It is concluded that the formation of the meso-b-scale rainband with continuous convective backbuilding, and the subsequent echo-training of convective cores with growing intensity and width as well as significant fallouts of frozen particles accounted for the generation of this extreme rainfall event. This extreme event was enhanced by local topography and the formation of a mesovortex of 20~30 km in diameter.

How to cite: Wang, G.: A Multiscale Analysis of a Nocturnal Extreme Rainfall Event of 14 July 2017 in Northeast China , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4235, https://doi.org/10.5194/egusphere-egu24-4235, 2024.

EGU24-5053 | Posters on site | AS1.10

Evaluation of detecting algorithm for potential refreezing rain area using the road icing accidents report  

Soohyun Kwon, Jeong-Eun Lee, Seungwoo Lee, and Hee-Jeong Choi

Freezing rain is a meteorological phenomenon in which precipitation melts in the upper atmosphere, transforming into super-cooled droplets near the ground due to lower temperatures. In Northern Europe and North America, strong winter storms often accompany freezing rain, leading to road or facility damage. In Korea, several traffic accident have also occurred due to road icing caused by freezing rain, demanding the development of monitoring technologies for enhanced safety measures. In order to provide the information about the road hazard warning and ensuring safety, we analyzed the atmospheric condition and dual-polarimetric characteristics for road icing and developed the algorithm to detect the potential refreezing rain area by using dual polarization radar and 3-D wet-bulb temperature.
 We selected road icing accidents including precipitation and inversion layer events from 2019 to 2021, and analyzed the changes in surface temperatures and wet-bulb temperatures at surface and the hydrometeor classified using dual-polarization variables at upper layer. The hydrometeor at the accident sites were classified with rain or super-cooled droplet, and wet-bulb temperatures ranged between -2 to 1.5 degrees. This information was used to determine the potential refreezing rain area. The inversion layer was also analyzed by the calculation of 3-dimensional wet-bulb temperatures through multi-quadratic interpolation using various observations (AWS, sounding, Buoy, etc.) and the Korea Local Analysis and Prediction System (KLAPS). The dual-polarization variables were employed to classify the hydrometeor type and investigate the possibility of ice particle melting within the inversion layer. The area for potential refreezing rain was designated as dangerous/cautious zones based on ground temperature conditions when snow particles melted within the inversion layer.
The performance of the algorithm for potential refreezing rain areas was evaluated during cold seasons when incidents of refreezing rain, often referred to as black-ice events occurred. We analyzed the hourly and monthly frequencies of detecting dangerous/cautious zones during traffic accidents caused by refreezing rain.

※ This research was supported by the "Development of radar based severe weather monitoring technology (KMA2021-03121)" of "Development of integrated application technology for Korea weather radar" project funded by the Weather Radar Center, Korea Meteorological Administration.

How to cite: Kwon, S., Lee, J.-E., Lee, S., and Choi, H.-J.: Evaluation of detecting algorithm for potential refreezing rain area using the road icing accidents report , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5053, https://doi.org/10.5194/egusphere-egu24-5053, 2024.

EGU24-5606 | ECS | Posters on site | AS1.10

Evaluation of precipitation product characteristics over Germany for hydrologic model forecasts 

Suad Hammoudeh, Klaus Goergen, Alexandre Belleflamme, and Stefan Kollet

As a primary component of the Earth’s hydrological cycle, precipitation plays a central role in many environmental processes and human activities. The availability of reliable precipitation data is essential for many sectors and applications, such as water resources management, flood and drought risk analysis, or hydrological modeling. In this study, we evaluate the characteristics of different precipitation datasets based on distinct methodologies and sources. This is in the context of high-resolution hindcasts and prototypical daily forecasts with the integrated hydrological model ParFlow over a central European model domain, where precipitation is a first order driver as part of the atmospheric forcing. Our objective is to determine, how closely precipitation from the ECMWF HRES numerical weather prediction matches in-situ observations, and how HRES compares to other precipitation products, some of which might be suitable for a bias adjustment of the hydrological model inputs. The European Climate Assessment & Dataset (ECA&D) in-situ daily precipitation observation dataset of 5072 stations in our ParFlow model domain serves as the reference. The time span of the comparison is from 2014 to 2022. Aside from ECMWF HRES, the evaluation includes at present data at different spatio-temporal resolutions: The ERA5 reanalysis as a background dataset, the HYRAS interpolated hydrometeorological raster data from the German Weather Service (DWD), the meteorological radar data product OPERA, a European composite dataset from EUMETNET, and the radar data product RADOLAN from DWD. Due to the spatial coverage of some datasets, the analysis is restricted to Germany constituting a subset of the hydrological model domain. The initial part of this evaluation uses only daily data, and precipitation products are compared at station locations. The spatial distribution and temporal variability is assessed with annual and seasonal sums, mean errors, and spatial correlation coefficients. Precipitation intensity is analyzed through the spatial distribution of the typical climate indices. The temporal characteristics of precipitation is determined through the precipitation fraction, i.e., the number of moderately wet days (75th percentile), very wet days (95th percentile), and consecutive wet days. Perkin's skill score is used for the comparison of the empirical distributions. While preliminary results indicate that HRES agrees well with the observational reference data, some form of bias adjustment may still be necessary.

How to cite: Hammoudeh, S., Goergen, K., Belleflamme, A., and Kollet, S.: Evaluation of precipitation product characteristics over Germany for hydrologic model forecasts, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5606, https://doi.org/10.5194/egusphere-egu24-5606, 2024.

EGU24-6461 | ECS | Orals | AS1.10

ResRadNet: A 3D-Residual Neural Network Approach for Temporal Super-Resolution and Ground Adjustment of Weather Radar Rainfall Estimates 

Julius Polz, Luca Glawion, Hiob Gebisso, Lukas Altenstrasser, Maximilian Graf, Harald Kunstmann, Stefanie Vogl, and Christian Chwala

Weather radars are advanced tools for atmospheric observations that provide QPE with a high spatial representativeness and a high temporal resolution (e.g. 5-minutes). However, due to their indirect measurement aloft, strong systematic errors as well as temporal sampling errors compared to rain gauge measurements at even higher resolution (e.g. 1-minute) persist. As a solution, bias and advection correction techniques are used. Residual neural networks have proven to be efficient tools to approximate the behavior of dynamical systems. Here, we present ResRadNet, a 3D-residual neural network (3D-RNN), that is capable of correcting biases and increasing the temporal resolution of weather radar based quantitative precipitation estimates (QPE). ResRadNet is trained to correctly reproduce 1-minute rain gauge data from sequences of 5-minute radar images and information about the orography. The dataset used in this study consists of 8 years of country-wide rainfall observations in Germany. The weather radar composite used as model input is based on reflectivity derived rainfall information from 17 C-band radars. The rain gauge reference consists of 1066 rain gauges with a 1-minute resolution used to train and test ResRadNet. An additional 1138 rain gauges with a daily resolution are used for long-term evaluation of remaining biases. The results showed that ResRadNet can significantly increase the linear correlation and reduce the root mean squared error of the QPE field compared to rain gauge data at 1- and 5-minute, as well as daily resolutions. A qualitative analysis also showed that ResRadNet is a suitable optical flow estimator and that the provided rainfall fields are not subject to temporal or spatial inconsistencies even though spatio-temporal consistency was not enforced during training. Therefore, our study shows how using 3D-RNNs can provide accurate 1-minute, ground-adjusted, and advection-corrected QPE.

How to cite: Polz, J., Glawion, L., Gebisso, H., Altenstrasser, L., Graf, M., Kunstmann, H., Vogl, S., and Chwala, C.: ResRadNet: A 3D-Residual Neural Network Approach for Temporal Super-Resolution and Ground Adjustment of Weather Radar Rainfall Estimates, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6461, https://doi.org/10.5194/egusphere-egu24-6461, 2024.

Mountainous Southern California experiences both wet and dry extremes in precipitation. During the wet extremes, shallow landslides and flash flooding are common consequences. These hazardous land surface impacts are typically triggered by short periods of extreme and local precipitation, oftentimes embedded within a larger storm. To characterize the hydrometeorological conditions that result in these impactful events, high-resolution precipitation information is required. In the topographically complex areas of Southern California, existing radars have insufficient coverage due to beam blockage and other issues, while sparse sub-daily rain gauge networks are not able to represent the high spatiotemporal precipitation variability. Coincidentally, this variability is often important in determining the locations where shallow landslides or flash floods are triggered. To this end, this work has developed a set of high-resolution quantitative precipitation estimates (QPEs) by blending information from rain gauges and bias corrected satellite precipitation estimates from U.S. operational precipitation products. The final product is a decadal (2014-2023) record of QPEs with high spatial (4km) and temporal (6-hourly) resolution, calibrated for the region and suitable for use in analyses of mountainous extreme precipitation events and associated hydrologic impacts. Validation of this final dataset is presented, including cross-validation to verify the bias correction efficacy. The final dataset is then used to examine the orographic precipitation variability and extremes. Both the climatological and event-scale orographic variability are examined for the Southern California mountainous regions. At the event-scale, emphasis is placed on understanding the variability for the most extreme precipitation events, which have the highest likelihood of resultant land surface impacts. A rigorous statistical analysis of the precipitation extremes is also presented, including an examination of the dominant patterns of extreme precipitation and several indices to characterize the nature of these extremes. Lastly, the influence of upstream atmospheric precursor conditions (namely, atmospheric instability and boundary layer moisture flux) on the distribution of the most significant extreme precipitation events is explored. As the spatial distribution of extreme precipitation events can impact the locations likely to experience hazardous land surface conditions during a particular storm, this has the potential to provide additional information for enhancement of predictability of these impactful events.

How to cite: De Biasio, E. and Georgakakos, K.: Analysis of extreme high-resolution precipitation based on gauge-corrected satellite observations in mountainous Southern California, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6570, https://doi.org/10.5194/egusphere-egu24-6570, 2024.

EGU24-6758 | Orals | AS1.10

Precipitation variability in CMIP6 climate models across the North Atlantic–European region 

Eva Plavcova, Ondrej Lhotka, Romana Beranová, and Radan Huth

Long-term changes in climate variability are an important aspect of the climate change with various impacts on society and environment. In contrast to numerous studies which evaluated projected changes in mean values and extremes of precipitation amount, intensity and/or frequency, studies on changes in precipitation variability have been relatively scarce. To understand whether and how the precipitation variability will change in the future, projections of climate models are utilized. However, accurate simulation of this precipitation characteristic by current climate models is pivotal.

In our study we analyze outputs from 13 CMIP6 GCMs across the North Atlantic–European region focusing on winter and summer seasons separately. We classify days with a total precipitation amount exceeding 1 mm as wet days, while the remaining days are considered as dry days. Precipitation probability denote the mean probability of a wet day, and precipitation variability is represented by the tendency to cluster wet/dry days into sequences. To quantify this, we use the persistence parameter defined as the 1-lag autocorrelation of a discrete two-state Markov chain.

Firstly, we evaluate whether precipitation variability is simulated correctly over the historical period (1980–2010) by comparing model outputs against the ERA5 reanalysis. Subsequently, we analyse projected changes in the future period (2070–2100) using simulations forced by two Shared Socio-economic Pathways (SSP585 and SSP245). This allows for a comparison of possible future climate changes under different climate policies.

We identify biases common to all models, notably an overestimated precipitation probability across much of Europe in winter, while its underestimation in summer, and a general tendency of models toward higher autocorrelation of wet/dry days. Projected changes in precipitation characteristics are more pronounced for the more pessimistic SSP585 scenario. We find that the changes in precipitation variability are independent on the changes in precipitation probability. Our findings also indicate that the model biases and simulated changes in precipitation probability and variability can be linked to the biases and changes in synoptic-scale atmospheric circulation.   

How to cite: Plavcova, E., Lhotka, O., Beranová, R., and Huth, R.: Precipitation variability in CMIP6 climate models across the North Atlantic–European region, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6758, https://doi.org/10.5194/egusphere-egu24-6758, 2024.

Cloud microphysics parameterization has several ice-crystal-related parameters that define the characteristic of ice crystal. Weather Research and Forecasting (WRF) Double-Moment 6-class (WDM6) parameterization scheme adopts the fall velocity-diameter and mass-diameter relationships from Heymsfield and Iaquinta (2000, HI00 hereafter) with the assumed single-bullet shape of ice crystals, and the mean mass-weighted terminal velocity-mixing ratio relationship from Heymsfield and Donner (1990, HD90 hereafter). There are a total five parameters that define ice-crystal characteristics, and these parameters vary according to different shapes of ice crystals, contributing to uncertainties of simulated precipitation. To assess these uncertainties, we generate 50 sampling sets using Latin hypercube sampling within the recommended range from previous studies. Numerical experiments are conducted for two major types of winter precipitation, namely Air-mass Transformation (AT) and Ease-coast Terrain effect (ET) types, over the Korean peninsula. The simulation results indicate that parameters defining the mass-diameter relationship are most sensitive for simulating precipitation in the AT type, while parameters defining the fall velocity-diameter relationship are most sensitive for the ET type. Sensitivity experiments are designed by adjusting the sensitive parameters for each type by ±20% to mitigate biases in surface precipitation observed in the control experiments. In the AT type, the sensitivity experiment simulates more solid-phase precipitable hydrometeors, such as snow and graupel, resulting in increased precipitation over the region with a negative bias. Conversely, in the ET type, the sensitivity experiment reduces the amount of snow and graupel, leading to a decrease in precipitation over the area with a positive bias. Our analysis underscores the high priority of tuning parameters related to ice-crystal characteristics to reduce uncertainty in precipitation simulations, depending on the type of winter precipitation.

 

Key words: Ice crystal, Uncertainty parameter, WDM6, Winter precipitation

 

Acknowledgement: This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (RS-2023-00272424) and Korea Meteorological Administration Research and Development Program under Grant (RS-2023-00240346)

How to cite: Kim, K.-B. and Lim, K.-S. S.: Impact of Parameters Related to Ice Crystal on the Simulation of Winter Precipitation over the Korean Peninsula, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6793, https://doi.org/10.5194/egusphere-egu24-6793, 2024.

EGU24-6976 | ECS | Posters on site | AS1.10

The analysis and evaluation of rainfall events of different durations in the Tibetan Plateau 

Xiaoyan Ling, Yingying Chen, Kun Yang, Xin Li, and Xu Zhou

The Tibetan Plateau, known as the 'Asian Water Tower,' has drawn significant attention to its hydrological cycle and associated atmospheric dynamics. The Qiang-tang Plateau, located in the northern part of the Tibetan Plateau's  endorheic basin (hereinafter referred to as the plateau), experiences notable climate and water cycle variations. The spatial characteristics of its precipitation determine the spatial patterns of hydrological elements and ecological environments in the Qiang-tang Plateau. However, its harsh environment and challenging conditions for station establishment have resulted in a severe scarcity of precipitation observation data. Presently, mainstream reanalysis products consistently overestimate precipitation levels on the plateau and fail to accurately simulate daily precipitation variations. To address this, utilizing data from 206 tipping-bucket rain gauges deployed across the plateau from 2017 to 2020, the study investigates rainfall events of different durations: short-term (1-3 hours), medium-term (4-6 hours), and long-term (7 hours or more).

The research reveals that the precipitation intensity at plateau sites is generally low, with short-term rainfall events being predominant. However, the contribution of short-term rainfall events increases spatially from the southeast edge to the inland of the plateau. Notably, the Qiang-tang Plateau exhibits a significantly higher proportion of short-term precipitation compared to other regions on the plateau. Furthermore, based on a newly established mountainous precipitation transect, it was discovered that as one ascends from the Gangdisi Mountains to the Qiang-tang Plateau, the contribution of short-term rainfall to the total precipitation significantly increases with elevation. Additionally, an analysis of mainstream reanalysis products (ERA5, MERRA2) and high-resolution model simulation data (HAR2) for different duration rainfall events indicates that reanalysis products consistently underestimate the contribution of short-term precipitation while overestimating long-term precipitation. HAR2 outperforms ERA5 specifically in the Qiang-tang Plateau and the northeast part of the plateau, whereas MERRA2 fails to capture the spatial heterogeneity of different duration rainfall events. Although reanalysis products can capture the diurnal peak of short-term precipitation, they tend to prematurely estimate the diurnal peak of long-term precipitation.

How to cite: Ling, X., Chen, Y., Yang, K., Li, X., and Zhou, X.: The analysis and evaluation of rainfall events of different durations in the Tibetan Plateau, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6976, https://doi.org/10.5194/egusphere-egu24-6976, 2024.

EGU24-7087 | Orals | AS1.10

A Summary and Comparison of the latest GPCP Daily and Monthly Products (Version 3.2) and the Plan Forward 

Ali Behrangi, George J. Huffman, and Robert F. Adler

This presentation is composed of four major parts: (1) a brief overview of the latest Global Precipitation Climatology Project (GPCP) Daily and Monthly products (V3.2) and satellite-gauge input data sets used in them, (2) comparison of the GPCP V3.2 products with the previous version of GPCP Daily (V1.3) and Monthly (V2.3) products and highlighting major changes, (3) assessment of the GPCP V3.2 products over the Oceans using Passive Aquatic Listeners (PALs) and over sea ice using snow depth data from combination of ICESat-2 and Cryosat-2 observations, and (4) a brief description of the plans towards the next generation of the GPCP products. GPCP is a popular combined satellite-gauge precipitation dataset in which the long-term CDR standards of consistency and homogeneity are emphasized, going back to 1983 for GPCP Monthly V3.2. Several major changes occurred in V3.2 including: (1) moving from Monthly 2.5°x2.5° and Daily 1.0°x 1.0° spatial resolution in V2.3 to 0.5°x0.5° for both Daily and Monthly products, (2) addition of more recent satellite data such as the Tropical Rainfall Measuring Mission (TRMM), CloudSat, Global Precipitation Measurement (GPM) mission, and the Gravity Recovery and Climate Experiment (GRACE) mass change observations, and (3) use of new precipitation retrieval and calibration methods. Compared to V2.3, GPCP V3.2 shows about  6.5% increase in global oceanic and about a 4.5% increase in global (land and ocean) precipitation rates with some major changes over the ocean between 40 oS and 60 oS. Similar to V2.3, a near-zero global precipitation trend was observed in V3.2.  However, regional trends, which are substantial, remain generally similar between V2.3 and V3.2. Evaluations over the oceans using PALs showed that GPCP v3.2 substantially outperforms GPCP V2.3 in representing rain occurrence and rain intensity at a daily scale, likely due to the use of IMERG in the daily product of GPCP V3.2. Comparison of the GPCP V3.2 product over sea ice, suggests that GPCP V3.2 generally captures the snowfall accumulation pattern over sea ice, compared to that obtained from the combination of ICESat-2 and Cryosat-2 observations, as well as that from ERA5. However, the products show considerable differences in the amount of snowfall accumulation, with ERA5 often showing the highest values. We will end the presentation by briefly discussing our plans for further improvement of GPCP including higher spatial and temporal resolution, lower latency, and the use of more advanced gauge analysis and precipitation retrieval methods.

How to cite: Behrangi, A., Huffman, G. J., and Adler, R. F.: A Summary and Comparison of the latest GPCP Daily and Monthly Products (Version 3.2) and the Plan Forward, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7087, https://doi.org/10.5194/egusphere-egu24-7087, 2024.

EGU24-7197 | Posters on site | AS1.10

Deep learning for X-band radar quantitative precipitation estimation using polarimetric measurements 

Ruiyang Zhou, Aofan Gong, Bu Li, Youcun Qi, and Guangheng Ni

Accurate estimation of surface precipitation with high spatial and temporal resolution is crucial for disaster weather detection and decision-making regarding water resources management. Polarimetric weather radar is an important instrument for quantitative precipitation estimation (QPE). Conventional parametric approaches, such as the radar reflectivity (Z) and rain rate (R) relations, cannot fully represent the spatial and temporal variability of clouds and precipitation due to parameterization errors and dependence on raindrop size distribution (DSD). Furthermore, these relations estimate rainfall on a grid-by-grid basis, preventing the incorporation of spatial information into precipitation estimation.

In recent years, machine learning has made rapid advancements in non-linear fitting and feature extracting. Since 2020, multiple studies constructed MLP or CNN-based QPE models that used polarimetric radar observations to retrieve precipitation. These researches have consistently demonstrated that machine learning algorithms perform better than traditional parametric methods in different regions and climatic conditions(Chen & Chandrasekar, 2021; Li et al., 2023; Osborne et al., 2023; Tian et al., 2020; Zhang et al., 2021; Zhou et al., 2023).

The aforementioned studies have highlighted the immense potential of deep learning for radar QPE, but they are based on S-band radar data. Because X-band radar has a shorter wavelength, the electromagnetic scattering characteristics of hydrometeors differ from those of S-band radar, especially for specific differential phase (kdp), which is closely related to rainfall. Furthermore, X-band radars have different spatial resolutions from S-band radars, which indicates that directly applying a model trained with S-band radar data to X-band radar data may introduce biases. Therefore, we develop a CNN-based QPE model using polarimetric measurements from X-band radars and compare its performance against traditional parametric methods. The input data for the CNN model is a matrix with dimensions (6, 9, 9). The matrix is composed of two matrices of size (3, 9, 9), which is the polarimetric measurements from the two lowest scan elevation angles and 9*9 surrounding range gates. This allows the input data to capture the spatial and physical characteristics of the precipitation field. The results reveal that the CNN-based model not only enhances the accuracy of radar QPE with a diminished bias but also provides a more precise depiction of the spatial distribution of precipitation in comparison to conventional methods.

How to cite: Zhou, R., Gong, A., Li, B., Qi, Y., and Ni, G.: Deep learning for X-band radar quantitative precipitation estimation using polarimetric measurements, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7197, https://doi.org/10.5194/egusphere-egu24-7197, 2024.

EGU24-7336 | Orals | AS1.10

The Precipitation Retrieval and Profiling Scheme for the Special Sensor Microwave Imager/Sounder 

Anja Niedorf, Christopher Kidd, Hannes Konrad, Karsten Fennig, and Marc Schröder

The Special Sensor Microwave Imager/Sounder (SSMIS) of the US Defense Meteorological Satellite Program (DMSP) has been the mainstay of observations used for precipitation retrievals over the last 20 years. The sensor, building upon the heritage of the DMSP Special Sensor Microwave/Imager (SSMI) series that operated between 1987 and 2020, provides precipitation-capable frequencies from 18-183 GHz at resolutions up to 15x13 km. The longevity of the SSMIS and the SSMI satellite series makes these sensors extremely important for the retrieval of precipitation at the climate-scale. The adaptation of the Precipitation Retrieval and Profiling Scheme (PRPS), originally developed for passive microwave sounders, to the SSMIS aims to provide model-free precipitation retrievals that can be incorporated into the Global Interpolated Rainfall Estimation (GIRAFE) product developed by EUMETSATs Satellite Application Facility on Climate Monitoring (CM SAF).

Fundamental to the PRPS is the avoidance of external dynamic data sets, such as model information, to ensure that the retrieval scheme is purely a satellite-based observational product. The scheme relies upon the generation of observational databases, based upon co-temporal and co-located observations made by the satellite sensor(s) and observations of precipitation made by either satellite-based precipitation radar or surface radars. For the PRPS-SSMIS, the databases have been generated using observations from SSMIS sensors on the F16, F17, F18 satellites matched against the precipitation estimates provided by the NASA/JAXA Dual frequency Precipitation Radar (DPR) on the NASA/JAXA Global Precipitation Measurement mission (GPM) core observatory. The orbits of the SSMIS and GPM provide about 20,000 crossing points per satellite between 2016 and 2022, and generate about 30M co-located (<2.5km) and co-temporal (<15mins) entries for the a priori database. The retrieval stage of the PRPS uses this database as a reference against which the satellite observations are made to provide an estimate of the surface precipitation. The PRPS-SSMIS as implemented here, provides instantaneous precipitation estimates across the globe at a spatial resolution of 15x15 km.

This presentation will show some initial results of the scheme which show that the PRPS-SSMIS retrievals are comparable with those generated by NASA’s operational precipitation retrieval scheme, GPROF. At the instantaneous scale the PRPS tends to generate less light precipitation and more heavy precipitation, this can be explained in part by the difference in the resolution of the PRPS-SSMIS (15x15 km) and GPROF-SSMIS (45x74 km). Crucially, the PRPS provide much more information on light precipitation compared with the existing CM SAF SSMIS retrieval scheme (not utilised in the current GIRAFE version because of these detection issues). At the monthly scale, the PRPS generates very similar results to GPROF with all the main precipitation features correctly portrayed.

How to cite: Niedorf, A., Kidd, C., Konrad, H., Fennig, K., and Schröder, M.: The Precipitation Retrieval and Profiling Scheme for the Special Sensor Microwave Imager/Sounder, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7336, https://doi.org/10.5194/egusphere-egu24-7336, 2024.

EGU24-7551 | Orals | AS1.10

GIRAFE v1: A global precipitation climate data record from satellite data including uncertainty estimates 

Hannes Konrad, Anja Niedorf, Stephan Finkensieper, Rémy Roca, Marc Schröder, Sophie Cloché, Giulia Panegrossi, Paolo Sanò, Christopher Kidd, Rômulo Augusto Jucá Oliveira, Karsten Fennig, Thomas Sikorski, and Rainer Hollmann

We present a new precipitation climate data record (CDR) GIRAFE (Global Interpolated Rainfall Estimation), which has recently been released by EUMETSATs Satellite Application Facility on Climate Monitoring (CM SAF). For now, it covers a time period of 21 years (2002 – 2022) with global coverage and 1° x 1° spatial resolution. GIRAFE is a completely satellite-based dataset obtained by merging infrared (IR) data from geostationary satellites and passive microwave radiometers (PMW) onboard polar-orbiting satellites. Additional to daily sum and monthly mean precipitation rate, a sampling uncertainty on daily scale within the range of geostationary satellites (55°S-55°N) is provided. The implementation of a continuous extension of GIRAFE via a so-called Interim CDR service started and associated data will become available.

For retrieving instantaneous rain rates from PMW observations, three different retrievals for microwave imagers (HOAPS) and sounders (PNPR-CLIM and PRPS) were used. Quantile mapping is applied to the instantaneous rain rates of the 19 different PMW sensors to achieve stability in GIRAFE over time. The IR observations undergo a dedicated quality control procedure. The uncertainty estimation is based on decorrelation ranges from variograms in spatial and temporal dimensions. The merging of PMW and IR data as well as the technique for uncertainty estimation in GIRAFE is based on the Tropical Amount of Precipitation with an Estimate of ERrors (TAPEER) approach.

Here, we present details on the GIRAFE algorithm and uncertainty estimation as well as results of the CM SAF quality assessment activity comprised of comparisons against other established global, regional and local precipitation products.

How to cite: Konrad, H., Niedorf, A., Finkensieper, S., Roca, R., Schröder, M., Cloché, S., Panegrossi, G., Sanò, P., Kidd, C., Jucá Oliveira, R. A., Fennig, K., Sikorski, T., and Hollmann, R.: GIRAFE v1: A global precipitation climate data record from satellite data including uncertainty estimates, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7551, https://doi.org/10.5194/egusphere-egu24-7551, 2024.

EGU24-7913 | ECS | Orals | AS1.10

Forecast Verification Analysis of the CombiPrecip Ensemble 

Athanasios Ntoumos, Ioannis Sideris, Marco Gabella, Urs Germann, and Alexis Berne

CombiPrecip is a real-time application developed by MeteoSwiss since 2012, which combines point raingauge measurements with radar-derived spatial estimations of precipitation over a vast 710x640km2 domain, extending beyond the Swiss borders. It relies on the geostatistics-based kriging with external drift as an interpolation technique. This method is probabilistic by nature, yielding both a mean value and an associated variance for every estimation. The purpose of our study is two-fold: (i) validate that the variance provided by the underlying geostatistical method of CombiPrecip does properly represent the uncertainty of the CombiPrecip product and (ii) devise a numerical method to build an ensemble of realistic-looking members based on this geostatistical variance. For this, we employ widely used probabilistic verification measures (reliability diagrams, rank histograms, ROC curves) for a large set of cross – validation results over the period 2016 – 2022. In addition, based on established methods developed within the nowcasting community, we produce ensembles of N realistic precipitation members that not only mimic the spatial autocorrelation of the mean-value CombiPrecip but also replicate its pixel-scale variance. Overall, our results indicate that observations fall reasonably well in the uncertainty range provided by the CombiPrecip ensemble.

 

How to cite: Ntoumos, A., Sideris, I., Gabella, M., Germann, U., and Berne, A.: Forecast Verification Analysis of the CombiPrecip Ensemble, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7913, https://doi.org/10.5194/egusphere-egu24-7913, 2024.

EGU24-8019 | Posters on site | AS1.10

Regional variation of climatological cloudburst frequency estimated from historical observations of daily precipitation sums 

Torben Schmith, Peter Thejll, Flemming Vejen, and Bo Christiansen

Cloudburst are geographically localized extreme rainfall events where a large amount of rain falls within a few hours. The combination of small spatial scale, short duration and scarceness makes it difficult to reveal any systematic regional differences in occurrence. Here we estimate climatological cloudburst frequencies from the daily precipitation sums for a dense network of 161 historical Danish stations covering the period 1914-2010. We do this using supplementary sub-hourly precipitation observations from a modern network and relate the daily probability of cloudburst occurrence to the corresponding daily precipitation sum using binary regression. This allows a subsequent estimation of the cloudburst frequency from the daily sums from the historical observations. To validate the method, we use stations from the modern network that have been operating for 30 years or longer. For these stations, we demonstrate significant skill by comparing observed and estimated cloudburst frequencies with a jackknife procedure. We then apply the binary regression model using the 161 historical series as input and estimate climatological cloudburst frequencies throughout Denmark. We find large and systematic regional variations across Denmark. The methodology also allows determining temporal changes of cloudburst frequency and we find large differences across Denmark.

How to cite: Schmith, T., Thejll, P., Vejen, F., and Christiansen, B.: Regional variation of climatological cloudburst frequency estimated from historical observations of daily precipitation sums, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8019, https://doi.org/10.5194/egusphere-egu24-8019, 2024.

EGU24-8111 | Posters on site | AS1.10

Study of the Urban Heat Island effect in Cyprus by using Earth Observation  

Charalambos Soderiades, Kyriacos Tryfonos, Silas Michaelides, Athos Agapiou, and Diofantos Hadjimitsis

Urbanization activities and their effects in Cyprus are more pronounced in the last 35 years, leading to a drastic change of the local climate of Cyprus’ main cities. Indeed, the contrast of energy absorption between developed urban areas and surrounding rural areas results in a variation of the local climate. The monitoring of the Urban Heat Island (UHI) is essential in the effort to produce heat maps of the urban area of Limassol, a town on the south coast of Cyprus. The area affected by UHI must be examined systematically in order to extract information that is vital in assisting decision- and policy-makers to adopt effective mitigation strategies and improve urban planning. This study presents the findings from the literature review of studying the UHI using earth observation, and reports on the results of the UHI effects for the whole Cyprus area, by using Landsat-5/8 TM & Sentinel-3 satellite images. NDVI calculations were conducted to derive the Fraction of Vegetation (FV) and calculate Emissivity over the last 20 years (2003-2023). Urban heat Island determination between several cities in Cyprus is presented in this study.  The results of this study are intended for use by the local authorities in support of the proposed revision of the local plans for the area by proposing a new ‘sustainability index‘ that uses UHI for urban planning purposes.

How to cite: Soderiades, C., Tryfonos, K., Michaelides, S., Agapiou, A., and Hadjimitsis, D.: Study of the Urban Heat Island effect in Cyprus by using Earth Observation , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8111, https://doi.org/10.5194/egusphere-egu24-8111, 2024.

EGU24-8403 | Orals | AS1.10

High-resolution data products for precipitation monitoring from the WegenerNet 3D Open-Air Laboratory for Climate Change Research 

Andreas Kvas, Jürgen Fuchsberger, Gottfried Kirchengast, Robert Galovic, Daniel Scheidl, Christoph Bichler, and Ulrich Foelsche

The WegenerNet 3D Open-Air Laboratory for Climate Change Research, located in southeastern Austria in an area of about 22 km x 16 km around the city of Feldbach (46.93°N, 15.90°E), provides a unique setup for studying extreme hydrometeorological events such as heavy precipitation, hailstorms, and drought periods. Its 3D upper air instrumentation consists of a polarimetric X-band Doppler weather radar, a microwave radiometer for vertical profiling of temperature, humidity, and cloud liquid water, an infrared cloud structure radiometer, and a water-vapor-mapping GNSS station network. This enables comprehensive upper-air monitoring of precipitation events with high spatial- and temporal resolution in near real-time. These 3D sensors complement the high-density WegenerNet hydrometeorological ground station network, which covers the area by 156 stations measuring precipitation, temperature, humidity, and (at selected locations) wind and soil parameters. This highly synergistic measurement setup enables robust internal cross-evaluation, calibration and quality control for obtaining reliable observations and derived WegenerNet data products. The 3D instrumentation is operational since mid-2021, providing a consistent and growing data record of nearly three years so far.

We present the first release of upper air data cube products derived from the WegenerNet 3D Open-Air Laboratory, aimed at studying (heavy) precipitation events. This includes radar-derived precipitation and hydrometeor classification with 500 m spatial resolution and 2.5 min time resolution at multiple altitude levels, cloud coverage and base height maps with 10 min resolution, vertical profiles of temperature and humidity, atmospheric stability indices with 10 min resolution, and GNSS- and radiometer-derived tropospheric path delays as well as precipitable water vapor with 2.5 min to 10 min resolution. In addition to these Level 2 data products, quality-controlled Level 1 observational data, such as radar reflectivities and differential phase measurements, GNSS tropospheric delays and gradients, and infrared and microwave brightness temperatures are also made available to the scientific community. These data products, and accompanying metadata, are available in the form of user-friendly 3D data cubes accessible through the WegenerNet Data Portal.

How to cite: Kvas, A., Fuchsberger, J., Kirchengast, G., Galovic, R., Scheidl, D., Bichler, C., and Foelsche, U.: High-resolution data products for precipitation monitoring from the WegenerNet 3D Open-Air Laboratory for Climate Change Research, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8403, https://doi.org/10.5194/egusphere-egu24-8403, 2024.

EGU24-9273 | Orals | AS1.10

Global hydro-climatological indicators and changes in the global hydrological cycle and rainfall patterns 

Andreas Dobler, Cristian Lussana, and Rasmus Benestad

There are only a few climate indicators that describe the state of the global hydrological cycle. In this presentation, we argue that important climate indicators based on global daily precipitation are lacking and propose three new indicators: 1) the daily global precipitation amount, 2) the daily global surface area receiving precipitation, and 3) the global mean daily precipitation intensity. Historically, assessing these indicators is limited by the extent of global observational networks. However, recent advancements in satellite observations and reanalysis data, particularly the ERA5 reanalysis, have enabled better estimations.

We present an analysis of the proposed indicators using ERA5 data and other data sources. We also discuss limitations and biases of the data sources, e.g. ERA5's tendency to overestimate precipitation. Further, a wavelet analysis of spatial characteristics of 24-hour precipitation is conducted, offering insights into the spatial extent and intensity of precipitation systems and their variations over time. To address the question whether long-term changes reflect real changes in Earth's global hydrological cycle due to warming, or may be artefacts from changes in the assimilated (satellite) data in ERA5, we examine an ensemble of CMIP6 simulations under scenarios of increasing greenhouse gas concentration.

Our analysis reveals that ERA5 shows a decrease in the global area of daily precipitation from 43% to 41% between 1950 and 2020. At the same time, the total daily global precipitation amount increased from 1440 Gt to 1510 Gt. The wavelet analysis of ERA5 data indicates that individual precipitation systems have become smaller in spatial extent but more intense over this period, suggesting an accelerated global hydrological cycle with reduced global rainfall area. The CMIP6 simulations show a robust decrease in the precipitation area towards the end of the 21st century in agreement with ERA5. However, compared to the reanalysis the changes are smaller and less rapid.  Nevertheless, our results suggest that in a warming climate the daily precipitation area may shrink, contributing to an increase in the mean daily precipitation intensity.

How to cite: Dobler, A., Lussana, C., and Benestad, R.: Global hydro-climatological indicators and changes in the global hydrological cycle and rainfall patterns, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9273, https://doi.org/10.5194/egusphere-egu24-9273, 2024.

EGU24-10114 | ECS | Posters on site | AS1.10

Investigation of climatic changes for hailstorms over the Alps using spatiotemporal satellite imagery and self-supervised machine learning 

Paula Bigalke, Claudia Acquistapace, and Daniele Corradini

Severe hailstorms are becoming more frequent in Central Europe showing increasing interannual variability. The Pre-Alpine and Alpine region seems to be especially affected due to its complex terrain, that initiates convection and can intensify many hail favoring processes. This results in increasingly strong large hail events, which are often very local phenomena. Ground-based observations from weather radars are most reliable for detecting hail, however, prove to be challenging in the Alpine region due to interference at mountain ranges.

Passive Microwave satellite observations offer a useful alternative for detecting hail: a probability for hail can directly be derived from Passive Microwave channels with a high spatial coverage. However, this data is only available at certain times during satellite overpasses, thus, capturing only a few of these events. The highest temporal coverage is given by visible, near-infrared and infrared data from MSG. Though not directly sensitive to hail its high spatiotemporal resolution can identify early stages of severe storm developments.

Recently, self-supervised machine learning approaches have been used to classify spatial cloud patterns from satellite measurements from MSG over the Atlantic and Germany. The model learns to sort similar cloud organization patterns into the same classes.

In this work, we aim at adapting this model to also include the temporal component to then classify the evolution of typical cloud patterns leading to severe hailstorms over the Alpine region. The framework will later be used to characterize changes in spatiotemporal evolution of large hail bearing systems and associated environmental conditions across a multi-year dataset. First steps are presented here including the investigation of the optimal training dataset using the available data sources.

How to cite: Bigalke, P., Acquistapace, C., and Corradini, D.: Investigation of climatic changes for hailstorms over the Alps using spatiotemporal satellite imagery and self-supervised machine learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10114, https://doi.org/10.5194/egusphere-egu24-10114, 2024.

EGU24-10338 | ECS | Posters on site | AS1.10

Using commercial microwave links and SEVIRI observations for rainfall estimation in Zambia 

Nico Blettner, Rebecca Wiegels, Harald Kunstmann, and Christian Chwala

In Zambia, like in many African countries, the dedicated rainfall observation network is sparse, whereas accurate information about rainfall is crucially needed. In such a data-poor country, opportunistic sensors like commercial microwave links (CMLs) can be very beneficial. However, the irregular spatial distribution and the fact that many CMLs are very long and operate at low frequencies are common characteristics for rural areas in Africa which make rainfall retrieval with CMLs challenging. In addition, the lack of reference data complicates the adoption and adjustment of existing CML processing methods. In particular, the detection of rain events in noisy CML data, which can have a significant effect on the resulting estimated rainfall amounts, requires special attention as the long low-frequency CMLs provide comparatively noisy data. One option to support CML data processing is the usage of satellite data.

We use level 1.5 data from Meteosat Second Generation (MSG) SEVIRI to generate a precipitation probability (PC) product, similar to the PC products from NWC SAF. Our PC product is generated by a convolutional neural network (CNN) which was trained with SEVIRI and high-resolution radar data in Germany and which was validated with station data in Burkina Faso. We use this PC product to improve the rain event detection during the data processing of almost 1000 CMLs with 15-minute min-max data over several months of the rainy season 2021/2022. In addition, we use two other rain event detection methods, the Python implementation of the nearby-link approach from RAINLINK and the simple rolling standard-deviation method. From the processed CML rainfall estimates, we produce interpolated rainfall maps which we then validate with rain gauge data.

Preliminary results show that the nearby-link and rolling standard-deviation method produce satisfactory results in urban regions where CML density is high and CML frequencies are larger than 10 GHz. The application of the SEVIRI-based PC product for improved CML data processing, in particular for the long low-frequency CMLs, is currently being investigated and we will present first results to analyze its potential and limitations.

How to cite: Blettner, N., Wiegels, R., Kunstmann, H., and Chwala, C.: Using commercial microwave links and SEVIRI observations for rainfall estimation in Zambia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10338, https://doi.org/10.5194/egusphere-egu24-10338, 2024.

EGU24-12329 | Posters on site | AS1.10

Precipitation retrievals from the SSMIS using the PRPS scheme: formulation, validation and intercomparison. 

Chris Kidd, Anja Niedorf, Hannes Konrad, Marc Schröder, and Karsten Fennig

The US Department of Defense (DoD) Meteorological Satellite Program (DMSP) has provided a long-term record of passive microwave observations from the Special Sensor Microwave/Imager (SSM/I) and the Special Sensor Microwave Imager/Sounder (SSMIS). These observations, available from 1987 to the present, provide the backbone of data used for global precipitation measurements. The SSM/I and SSMIS instruments have similar lower frequency channels (19.35-85.0 GHz vs 19.35-91.655 GHz), with the SSMIS having higher frequency channels at 150 GHz and three around 183.31 GHz.

The Precipitation Retrieval and Profiling Scheme (PRPS) is a retrieval scheme designed to be efficient and avoid the use of any external dynamic data sets, such as model information. This is particularly important for a truly independent data product that can be used for evaluating model performance. The PRPS was originally designed for use with cross track sounding instruments but has been adapted to other passive microwave sensors: here it has been adapted to utilise the SSMI and SSMIS Fundamental Climate Data Records generated by the EUMETSAT CM SAF. The PRPS-SSMIS relies upon an observational a priori database derived for each sensor paired with a database index file to provide a computationally efficient retrieval scheme.

This poster will present an outline of the PRPS-SSMIS scheme together with the validation and intercomparison of the resulting precipitation products. At present the databases for the retrieval scheme are based upon 7 years of observations (2016-2022) from SSMIS sensors on the F16, F17, F18 DMSP satellites, matched to co-incident and co-temporal measurements of precipitation from the Global Precipitation Measurement (GPM) mission’s Dual frequency Precipitation Radar (DPR). Comparisons are made at a number of scales: ‘climate’ scale comparisons are made against the GPCP v3.2 global precipitation product, through to instantaneous precipitation retrievals which are compared with surface radar over the US and Europe. In addition, comparisons are made with the Ferraro and GPROF precipitation products to assess consistency with other estimates. Overall, the PRPS-SSMIS retrievals tend to underestimate the precipitation, primarily due to the internal assumptions in the retrieval scheme as a result of the skewed distribution of precipitation occurrence and may easily be corrected. Correlations between the PRPS-SSMIS products and the GPCP are similar to those of the GPROF-SSMIS products, particularly when a comparable spatial resolution is used. Both the GPROF and PRPS scheme outperform the Ferraro precipitation product in terms of bias and correlation and are more consistent over time.

How to cite: Kidd, C., Niedorf, A., Konrad, H., Schröder, M., and Fennig, K.: Precipitation retrievals from the SSMIS using the PRPS scheme: formulation, validation and intercomparison., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12329, https://doi.org/10.5194/egusphere-egu24-12329, 2024.

EGU24-12674 | Posters on site | AS1.10

Cloud radar spectral polarimetry for drop-size-distribution profiling: perspectives and challenges 

Alexander Myagkov, Tatiana Nomokonova, and Michael Frech

Rainfall is a critical component of the Earth's water cycle, influencing global economic stability, access to food and freshwater, and daily life. Rain is also frequently used as a calibration target for various remote-sensing instruments. As such, timely and accurate observations of rainfall are vital for meteorological applications. The microphysical properties of rain are commonly characterized by the drop-size distribution (DSD), which determines the water content, intensity of precipitation, and kinetic energy of the rain.

Conventional methods for measuring DSD include in situ instruments such as optical disdrometers and polarimetric weather radars. Disdrometers measure the size and velocity of raindrops within a narrow laser beam, providing data only at the surface level and having uncertainties due to the limited sampling area. Polarimetric weather radars, on the other hand, can observe rain profiles over larger areas, but typically only capture higher moments of the DSD, which then require specialized retrieval methods to derive DSD properties. Such retrievals are typically based on known size-shape-velocity relations for raindrops and a scattering model. Polarimetric variables are of an especial value because they allow to decouple the contribution of shape, size, and concentration of raindrops to the observations. In addition, the polarimetric variables can be accurately calibrated. The results of retrieval based on the moments are, however, prone to uncertainties related to measurement errors and limited information content of the DSD moments.

Polarimetric Doppler cloud radars, operating at millimeter wavelengths, offer an alternative to traditional methods of the DSD estimation. They can measure the same set of parameters as weather radars but spectrally resolved, i.e. the cloud radar can separately measure droplets coexisting in the same volume but moving with different velocities relative to the radar. Since velocity of droplets is a proxy of their size, spectrally resolved measurements contain much more information about the underlying DSD.

This study explores the potential of polarimetric cloud radars to retrieve DSD profiles. We highlight the advantages of this approach, including the ability of the non-parametric estimation of DSD profiles. We also examine existing challenges, such as the impact of resonance effects on observations due to the comparable wavelength of cloud radars and droplet sizes. These effects require accurate representation in scattering models and size-shape-velocity relationships. Current literature lacks explanations for some observations, indicating a need for further research and development of retrieval methods based on spectral polarimetric cloud radar data.

How to cite: Myagkov, A., Nomokonova, T., and Frech, M.: Cloud radar spectral polarimetry for drop-size-distribution profiling: perspectives and challenges, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12674, https://doi.org/10.5194/egusphere-egu24-12674, 2024.

EGU24-12890 | ECS | Posters on site | AS1.10

The new real-time radar-gauge-CML adjustment system pyRADMAN at DWD 

Maximilian Graf, Christian Chwala, Malte Wenzel, Christian Vogel, Harald Kunstmann, and Tanja Winterrath

Adjusting weather radar data with ground-based precipitation observations is an established way to overcome radar-specific uncertainties. Most commonly, rain gauge data is used for this task. Commercial microwave links (CMLs) deployed by mobile network operators offer another source of rainfall information that can be used to adjust weather radar data. One of the main advantages of CMLs for this task is the real-time availability of their data with a latency of less than a minute. In addition, their large number, with high densities in particular in urban regions, and the path-averaging nature of their measurements have the potential to improve radar adjustment at short aggregation times.

We developed the Python framework pyRADMAN which is capable of merging weather radar with rain gauge and CML data with selectable temporal aggregations from minutes to hours. The path-averaging nature of the CML data is considered when merging with the gridded radar data. Computational efficiency has been taken into consideration in all implementations allowing a full countrywide radar adjustment for Germany, including the required processing of CML rainfall estimates, within 2 minutes with a pure Python implementation. pyRADMAN has now been continuously operating at DWD in real time since August 2023. Currently, real-time data streams from the gridded weather radar composite (based on 17 radar sites), ~1500 rain gauges, and ~5000 CMLs are handled by pyRADMAN, and products consisting of different combinations of sensors are produced for several aggregation times and latencies. 

We will show the general concept of pyRADMAN and present results from merging radar data with rain gauge and CML data. Our analysis will consist of selected events and monthly statistics. Results will be shown for aggregation times from 5 to 60 minutes and latencies of production from 5 to 20 minutes (increasing the number of available rain gauges for merging with increasing latency).

How to cite: Graf, M., Chwala, C., Wenzel, M., Vogel, C., Kunstmann, H., and Winterrath, T.: The new real-time radar-gauge-CML adjustment system pyRADMAN at DWD, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12890, https://doi.org/10.5194/egusphere-egu24-12890, 2024.

EGU24-12908 | Orals | AS1.10

Meteorological and Remote Sensing Analysis of the Severe Storm “Daniel” over Greece 

Panagiotis T. Nastos, Elissavet Feloni, Alexandros Paraskevas, and Ioannis T. Matsangouras

Omega blocking, a meteorological phenomenon characterized by a persistent high-pressure system resembling the Greek letter omega (Ω) in the atmosphere, has recently been observed in the Mediterranean region. This atmospheric setup can have significant impacts on the weather patterns in the area, leading to prolonged periods of stable and dry conditions or, conversely, intense storms. One noteworthy instance of this phenomenon occurred with the arrival of Storm “Daniel" in Greece on September 4, 2023. This storm brought about a substantial disruption in the Mediterranean climate, particularly in the Thessaly region, Central Greece. The combination of omega blocking and Storm “Daniel” resulted in exceptionally high levels of precipitation and severe weather conditions, leading to significant flooding and damage in affected areas. The Thessaly region, Central Greece bore the brunt of the storm, experiencing significant flooding that damaged homes, roads, and agricultural areas. This inundation also led to the displacement of residents and posed challenges for local authorities in providing relief and assistance. Additionally, Storm “Daniel” had an economic impact, particularly on agriculture, as crops were damaged or destroyed by the excessive rainfall. Transportation networks were also affected, causing delays and disruptions in the affected areas. Overall, Storm Daniel underscored the need for effective disaster preparedness and response measures in Greece to minimize the impact of such severe weather events in the future and protect the well-being of its residents.

This research paper delves into a thorough examination of the severe Storm "Daniel," which impacted Greece on September 4, 2023, with a particular emphasis on its significant consequences on September 5, 2024. An all-encompassing approach is employed to analyze the storm, including a synoptic assessment, a thorough examination of weather conditions, and the utilization of remote sensing data. The synthesis of synoptic analysis yields insights into the broader atmospheric patterns and dynamics that contributed in the formation and progression of Storm "Daniel". Additionally, the incorporation of remote sensing data provides a distinctive perspective on the storm's characteristics, including its spatial extent, precipitation distribution, and the identification of vulnerable areas. By integrating these three analytical aspects, our aim is to provide a comprehensive overview of Storm “Daniel”, shedding light on its genesis, intensification, and the crucial meteorological factors that contributed to its exceptional precipitation.

Understanding the relationship between omega blocking and the occurrence of storms like “Daniel” in the Mediterranean is crucial for predicting and mitigating the potential impacts of such extreme weather events in the future. This research and analysis can aid in developing more accurate forecasting and early warning systems to protect communities in the region from the adverse effects of these atmospheric phenomena.

How to cite: Nastos, P. T., Feloni, E., Paraskevas, A., and Matsangouras, I. T.: Meteorological and Remote Sensing Analysis of the Severe Storm “Daniel” over Greece, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12908, https://doi.org/10.5194/egusphere-egu24-12908, 2024.

EGU24-13597 | Orals | AS1.10

Improved Revisit Times of Microwave Observations of Precipitation: Recent Scientific Results from the Temporal Experiment for Storms and Tropical Systems (TEMPEST) Missions 

Steven C. Reising, Christian D. Kummerow, Venkatachalam Chandrasekar, Shannon T. Brown, Chandrasekar Radhakrishnan, Chia-Pang Kuo, and Richard Schulte

Small satellite constellations provide the potential to improve spatiotemporal resolution of microwave observations of precipitation from low-Earth orbit.  Shorter revisit times are essential to improve understanding of the development and evolution of extreme precipitation systems, in turn improving numerical weather prediction and accuracy of parameterization of extreme weather events in global climate models.  To this end, Temporal Experiment for Storms and Tropical Systems (TEMPEST) was proposed in 2013 as a constellation of 6U CubeSats in LEO to provide frequent observations of rapidly developing storms.  TEMPEST-D, the resulting NASA Earth Venture Technology Mission, demonstrated the first global observations from a multi-frequency microwave radiometer on a CubeSat for nearly three years from 2018 to 2021. TEMPEST-D exceeded expectations for scientific data quality, instrument calibration, radiometer stability, and mission duration. TEMPEST-D brightness temperatures were validated using double-difference intercomparison with scientific and operational microwave sensors, including GPM/GMI and four Microwave Humidity Sounders (MHS), operating at similar frequencies to TEMPEST-D channels at 87, 164, 174, 178 and 181 GHz. TEMPEST-D performance was shown to be comparable to or better than much larger operational sensors, in calibration accuracy, precision, stability and instrument noise, during its nearly 3-year mission.

A nearly identical TEMPEST flight spare was produced by JPL alongside TEMPEST-D for risk reduction.  The TEMPEST flight spare was made available to the U.S. Space Force to demonstrate low-cost space technologies for improving global weather forecasting. TEMPEST was then integrated with the Compact Ocean Wind Vector Radiometer (COWVR) produced by NASA/JPL for the U.S. Air Force. COWVR and TEMPEST were launched together as the Space Test Program – Houston 8 (STP-H8) on December 21, 2021, and deployed on the ISS Japanese Experiment for at least 3 years of operations. COWVR and TEMPEST have performed complementary observations of Earth’s oceans and atmosphere from the ISS nearly continuously since January 8, 2022. Atmospheric retrievals of water vapor profiles, clouds, and precipitation from COWVR/TEMPEST-H8 are performed collaboratively by JPL and Colorado State University.

Atmospheric inversion techniques have been developed to retrieve water vapor altitude profiles, as well as single-layer cloud liquid water and cloud ice water, from TEMPEST brightness temperatures, using ECMWF Reanalysis v5 (ERA5) data as an initial guess. These retrievals are enhanced through the inclusion of geostationary infrared data from GOES-16 ABI channels, increasing the number of levels and reducing the error of water vapor retrieval, particularly in the upper troposphere. 

The accuracy and precision of TEMPEST-D brightness temperatures have previously been validated using clear-sky oceanic observations.  Recent studies have extended the validation of both TEMPEST-D and TEMPEST-H8 to include observations of tropical cyclones, hurricanes, and typhoons using GPM-GMI passive microwave brightness temperatures and GPM-DPR active microwave vertical cumulative reflectivity.  These passive/active microwave intercomparisons employ techniques developed for quantitative evaluation of the cross correlation between TEMPEST-D and RainCube observations of tropical cyclones, hurricanes, and typhoons.  Such passive/active microwave observations also provide the basis for the development of surface rain rate estimates and retrieval of the vertical structure of precipitation from combined TEMPEST and DPR observations.

How to cite: Reising, S. C., Kummerow, C. D., Chandrasekar, V., Brown, S. T., Radhakrishnan, C., Kuo, C.-P., and Schulte, R.: Improved Revisit Times of Microwave Observations of Precipitation: Recent Scientific Results from the Temporal Experiment for Storms and Tropical Systems (TEMPEST) Missions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13597, https://doi.org/10.5194/egusphere-egu24-13597, 2024.

EGU24-13739 | Orals | AS1.10

THE HYDROMETEOR IDENTIFICATION FOR THE GPM DPR: Version 8 Updates 

Chandra V Chandrasekar, Minda Le, and Ari-Matti Harri

Since May 2018, the Dual-frequency Precipitation Radar (DPR) on board the GPM core observatory satellite has operated in full scan mode. Dual-frequency full swath data provides us a valuable chance to improve our knowledge of precipitation processes by providing greater dynamic range, more detailed information on microphysics, and better accuracies in rainfall and liquid water content retrievals [1]. The DPR Level-2 algorithms consist of several modules including the classification (CSF) module, where precipitation type is classified into three major types: stratiform, convective, and other.  Besides that, estimates of the melting layer top and bottom are provided in the classification module with product name as “binDFRmMLTop”, “binDFRmMLBottom” and the quality metric of “flagMLquality”. Three flags namely, identifiers of falling snow on the ground, graupel or hail along vertical profile termed, “flagSurfaceSnowfall”, “flagGraupelHail” and “flagHail” are recently developed in the DPR level-2 algorithm using a concept of precipitation type index (PTI). All these are currently developed products (version 7) in classification module of GPM DPR level-2 algorithm based on full-swath dual-frequency observations [2][3][4]. 

 

In near future, a new feature will be added to the version 8 of the GPM DPR level-2 algorithm to provide vertical profile of hydrometeors for full swath data.  A conceptual flow  for initial implementation will be presented. The judgements are made mainly on the DPR products omly to provide an independednt assessment. Mixed phase hydrometeors are judged with melting layer top and bottom together with the 0° isotherm. Flag of surface snowfall is used to identify snow only profile, while flags for detecting graupel and hail help identify range bins with those hydrometeor types. The whole judgement  a robust detection system to not only combine the products but enforce meteorologically meaningful. In the initial phase, five hydrometeor types will be introduced. They are dry snow/ice crystal (DS/ICE), wet snow (WS), graupel (GPL), hail (Hail) and rain (Rain). DS/ICE, GPL and Hail represent low-density, medium-density, high-density particles respectively.  

How to cite: Chandrasekar, C. V., Le, M., and Harri, A.-M.: THE HYDROMETEOR IDENTIFICATION FOR THE GPM DPR: Version 8 Updates, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13739, https://doi.org/10.5194/egusphere-egu24-13739, 2024.

EGU24-14642 | ECS | Posters on site | AS1.10

Trend analysis of remotely sensed and forecasted precipitation in Iceland 1982-2050 

Iman Rousta, Marjan Dalvi, and Haraldur Olafsson

Precipitation is a major energy resource in Iceland. This study employs the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) dataset to examine how precipitation patterns have evolved across Iceland from 1982 to 2021 and forecast them for the period 2022-2050.  The data confirms the known basic pattern of substantial precipitation in the south, while the northern interior plains are relatively arid.  The maximum precipitation is found in the South-East, but values are lower than suggested by glaciological and runoff data.  There is a non-significant overarching trend in annual precipitation across the country. However, a statistically significant declining trend (R>0.3, p-value=0.05) is observed in the interior regions of the East and Northeast regions. Conversely, a statistically significant increasing trend (R>0.3, p-value=0.05) is detected in coastal areas of these two regions. Future forecasting (2022-2050) suggests a very slight increase in Iceland's annual precipitation (approximately 0.6 mm/year). The findings of this study underline the importance of local scale monitoring of precipitation and comparison of methods of assessment of true ground precipitation.

How to cite: Rousta, I., Dalvi, M., and Olafsson, H.: Trend analysis of remotely sensed and forecasted precipitation in Iceland 1982-2050, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14642, https://doi.org/10.5194/egusphere-egu24-14642, 2024.

EGU24-14760 | ECS | Posters on site | AS1.10

Remotely sensed assessment of Urmia Lake drying up; Climate change or anthropogenic effects?! 

Marjan Dalvi, Iman Rousta, and Haraldur Olafsson

Urmia Lake is the largest hypersaline lake in Western Asia and it is currently facing severe desiccation. Immediate action is necessary to prevent irreversible damage to the environment and economy. The lake covers the majority of the Urmia Lake watershed. This study aimed to analyze the changes in Land Surface Temperature (LST) during the day and night in the area using MODIS 1 km, 8 days, version 061 (MOD11A2) images. The study also looked at water level variations using TOPEX/POSEIDON and Jason 1, 2, and 3, and precipitation variations using CHIRPS images from the period of 2001-2023. The results indicate that the water level of Urmia Lake has significantly declined by about 10 meters in the last few decades. Approximately 95 percent of the lake has dried up. The continuous declining trend of the water level started in 2001 and has led to an increase in LST day, about 0.03 ℃/year, and a decrease in LST night, about 0.07 ℃/year. Precipitation variations did not show any significant trend during the study period. Due to the high salt content caused by the lake drying up, the area is becoming a center for salty dust that can negatively affect the surrounding habitats. The trend of precipitation variations suggests that climate is not the primary factor responsible for the lake's desiccation.

How to cite: Dalvi, M., Rousta, I., and Olafsson, H.: Remotely sensed assessment of Urmia Lake drying up; Climate change or anthropogenic effects?!, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14760, https://doi.org/10.5194/egusphere-egu24-14760, 2024.

Cloudbursts over the NWH have become more common in recent years. The uncertainty caused by the sparse density of the station network over NWH encouraged us to employ the developed dataset, Indian Meteorological Ensemble Dataset (IMED), which explicitly accounts for topographical complexity and uncertainties in precipitation estimations. In the NWH, where monitoring stations are sparse, and cloudbursts are hard to discern, this study examines IMED's efficiency in identifying cloudburst events. We aim to use the mean, 70th percentile, 80th percentile, and 99th percentile values from 30 ensembles of IMED data every day with a resolution of 0.25 degrees.  We evaluated 18 events in the NWH between 2014 and 2016, which were documented in different paper publications. Furthermore, we compare the cloudburst identification ability of the CHIRPS dataset to that of the IMED datasets. A pixel-wise analysis shows that IMED performs better than the CHIRPS dataset in this event detection. With the mean value of IMED, it can capture five events, whereas four events are captured by the CHIRPS dataset. With the 70 percentile, 80 percentile, and 99th percentile, IMED can capture more events. This study concludes that IMED performs better than CHIRPS in identifying cloud burst events over the NWH region.

How to cite: Peringiyil, A., Saharia, M., and Op, S.: Assessment of the Indian Meteorological Ensemble Dataset (IMED) Performance in Identifying Cloudburst Events over the Northwest Himalayas (NWH) , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15910, https://doi.org/10.5194/egusphere-egu24-15910, 2024.

EGU24-16304 | Orals | AS1.10

The WInd VElocity Radar Nephoscope (WIVERN): a candidate mission for the ESA Earth Explorer 11 

Alessandro Battaglia, Anthony Illingworth, Frederic Tridon, Pavlos Kollias, Maximilian Maahn, Cathy Hohenegger, and Filippo Emilio Scarsi

The WIVERN (WInd VElocity Radar Nephoscope, www.wivern.polito.it) concept (Illingworth et al., 2018), is one of the two remaining candidate missions of the ESA Earth Explorer program. The mission is now entering Phase A, which is expected to end in July 2025 with, at the ESA User Consultation Meeting, the final selection of the mission that will be launched in 2032.

WIVERN promises to complement the Aeolus Doppler wind lidar that measures predominantly clear air winds by globally observing, for the first time, the vertical profiles of winds in cloudy areas. The mission will also strengthen the cloud and precipitation observation capability of the Global Observing System by providing unprecedented revisit time of cloud and precipitation vertical profiles.

The mission hinges upon a single instrument, i.e., a dual-polarization Doppler W-band scanning cloud radar with a circular aperture non-deployable main reflector larger than 3 m. The WIVERN antenna conically scans a large swath (of about 800 km) around nadir at an off-nadir angle of about 38o at 12 revolutions per minute. This viewing geometry allows daily revisits poleward of 50°, 20-km horizontal resolution, and approximately 1-km vertical resolution (Battaglia et al., 2022). A key element to achieve Doppler accuracy and large Nyquist folding velocity is the use of closely spaced pulse pairs with polarization diversity (one pulse is H polarised, the other V polarised). In this paper we will discuss the status of the mission including the updated scientific objectives and outline some of the technical challenges of the measuring technique. We will also present examples of Level 2 products with particular focus on the cloud and precipitation products highlighting the benefit of the improved sampling and of the reduced clutter particularly over ocean surfaces compared to nadir-looking radars.

Illingworth, A. J., and Coauthors, 2018: WIVERN: A New Satellite Concept to Provide Global In-Cloud Winds, Precipitation, and Cloud Properties. Bull. Amer. Meteor. Soc., 99, 1669–1687, https://doi.org/10.1175/BAMS-D-16-0047.1. 

Battaglia, A., Martire, P., Caubet, E., Phalippou, L., Stesina, F., Kollias, P., and Illingworth, A.: Observation error analysis for the WInd VElocity Radar Nephoscope W-band Doppler conically scanning spaceborne radar via end-to-end simulations, Atmos. Meas. Tech., 15, 3011–3030, https://doi.org/10.5194/amt-15-3011-2022, 2022.

 

How to cite: Battaglia, A., Illingworth, A., Tridon, F., Kollias, P., Maahn, M., Hohenegger, C., and Scarsi, F. E.: The WInd VElocity Radar Nephoscope (WIVERN): a candidate mission for the ESA Earth Explorer 11, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16304, https://doi.org/10.5194/egusphere-egu24-16304, 2024.

EGU24-16514 | Orals | AS1.10

Introducing the path-integrated attenuation as an additional filter in the quality index of spaceborne and ground-based radar calibration bias estimates 

Eleni Loulli, Johannes Bühl, Silas Michaelides, Athanasios Loukas, and Diofantos Hadjimitsis

This study analyses polarimetric weather radar data to explore their potential for comprehensive and reliable precipitation and thus, drought monitoring in Cyprus. For this purpose, we compare reflectivity measurements from the two ground-based X-band dual-polarization radars of the Department of Meteorology of the Republic of Cyprus with measurements obtained from the Dual-Frequency Precipitation Radar (DPR) onboard NASA’s Global Precipitation Measurement (GPM) mission. The comparison considers six years (2017–2023) of observations. It is implemented using the volume-matching method proposed by Schwaller and Morris (2011), as extended by Crisologo et al (2018) to take into account the beam blockage fraction as the basis of a quality index. To further enhance the consistency and precision of the calibration bias, we introduce path-integrated attenuation as an additional filter in the quality index. The path-integrated attenuation of the ground radars is estimated using a forward gate-by-gate attenuation correction method based on an iterative approach with scalable constraints. The level of path-integrated attenuation of the GPM Dual-Frequency Precipitation Radar is evaluated based on the GPM 2AKu variable piaFinal.

Acknowledgements

The authors acknowledge the ‘EXCELSIOR’: ERATOSTHENES: EΧcellence Research Centre for Earth Surveillance and Space-Based Monitoring of the Environment H2020 Widespread Teaming project (www.excelsior2020.eu). The ‘EXCELSIOR’ project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 857510, from the Government of the Republic of Cyprus through the Directorate General for the European Programmes, Coordination and Development and the Cyprus University of Technology.

The authors also acknowledge the Department of Meteorology of the Republic of Cyprus for providing the X-band radar data.

How to cite: Loulli, E., Bühl, J., Michaelides, S., Loukas, A., and Hadjimitsis, D.: Introducing the path-integrated attenuation as an additional filter in the quality index of spaceborne and ground-based radar calibration bias estimates, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16514, https://doi.org/10.5194/egusphere-egu24-16514, 2024.

EGU24-17076 | Posters on site | AS1.10

The global CML data collection initiative GCDCI: The solution for scaling up CML rainfall estimation in developing countries? 

Christian Chwala, Remko Uijlenhoet, Aart Overeem, Tanja Winterrath, and Nick van de Giesen

Rainfall estimation from commercial microwave link (CML) attenuation data has matured and is being implemented by several European meteorological services. Individual studies have also confirmed its applicability in developing countries. But data collection and data access remain cumbersome, requiring to start from scratch in each country and in each cooperation with a new mobile network operator (MNO). More often than not the precious CML attenuation data that is produced for monitoring purposes is not stored on a long-term basis and thus is lost forever if no cooperation with researchers or meteorological services incentivizes archiving.

To avoid further loss of data and to allow to better scale up CML data acquisition and data collection across different countries, we propose to start the global CML data collection initiative (GCDCI). The GCDCI will provide containerized templates for the required IT systems for CML data collection, archiving and monitoring, as well as template documents for the required legal agreements. Each MNO will get a separate cloud-based compute and storage infrastructure which they can use to do long-term monitoring and analysis of their network, providing an incentive for them to transfer their data to the GCDCI platform. Potentially, access for third parties, based on trilateral agreements with GCDCI and individual MNOs, could be implemented, e.g. to allow the development of derived products by the private sector. A central compute infrastructure, only accessible by GCDCI staff, will access data from the individual instances of the MNOs and do a centralized CML data processing. Potentially the centralized processing can be combined with real-time satellite data to both enhance the CML data processing as well as the generation of rainfall products from satellite data.

With our poster we want to spark a discussion about this approach and start forming a consortium to put it into operation as a not-for-profit organization with inspirations from initiatives like TAHMO and GPCC.

How to cite: Chwala, C., Uijlenhoet, R., Overeem, A., Winterrath, T., and van de Giesen, N.: The global CML data collection initiative GCDCI: The solution for scaling up CML rainfall estimation in developing countries?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17076, https://doi.org/10.5194/egusphere-egu24-17076, 2024.

EGU24-19469 | ECS | Posters on site | AS1.10

Global Performance Assessment of 20+ Precipitation Products Using Radar Data and Gauge Observations 

Xuetong Wang, Hylke Beck, and Raied Alharbi

Accurate precipitation (P) estimates are crucial for a wide range of applications, including water resource management, disaster risk reduction, agricultural planning, and infrastructure development. Over the past few decades, numerous gridded P products have been developed, with varying temporal and spatial resolutions, derived from diverse data sources, and employing different methodologies and algorithms. However, these products frequently exhibit significant uncertainties, errors, and biases, underscoring the importance of selecting the most suitable product for each application. In this study, we conducted a comprehensive evaluation of the strengths and weaknesses of over 20 freely available global gridded P products. We used the European RADar CLIMatology (EURADCLIM) gauge-radar dataset, the US Stage-IV gauge-radar product, and observations from approximately 20,000 global stations as ground truth. Our assessment included several new products, such as PDIR-Now and GPM+SM2RAIN, as well as an experimental Random Forest (RF) model, a potential new version of the Multi-Source Weighted-Ensemble Precipitation (MSWEP) product. For the assessment, we employed a broad range of performance metrics sensitive to various aspects of P time series, including the versatile Kling-Gupta Efficiency (KGE) and its components (correlation, bias, and variability), as well as the Critical Success Index (CSI), wet day bias, peak bias, and trend error. Additionally, we assessed the relative performance in different physiographic regions, seasons, and P regimes, and among various product types (satellite, (re)analysis, gauge, and combinations thereof). The RF model showed the best overall performance, achieving a mean CSI of 0.42. In comparison, the current MSWEP version, CHIRP, ERA5, GSMaP and IMERG achieved mean CSI values of 0.40, 0.21, 0.36, 0.32, and 0.32, respectively. Our study highlights the stark differences in performance among various state-of-the-art P products and provides a baseline for the development of new machine learning-based P products.

How to cite: Wang, X., Beck, H., and Alharbi, R.: Global Performance Assessment of 20+ Precipitation Products Using Radar Data and Gauge Observations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19469, https://doi.org/10.5194/egusphere-egu24-19469, 2024.

EGU24-21231 | Orals | AS1.10 | Highlight

Tropical Cyclone and Convective Storm Observations with the NASA TROPICS Constellation Mission 

William Blackwell and the TROPICS Science Team

Four NASA TROPICS Earth Venture (EVI-3) CubeSat constellation satellites were successfully launched into orbit on May 8 and May 25, 2023 (two CubeSats in each of the two launches).  TROPICS is now providing nearly all-weather observations of precipitation horizontal structure, cloud ice, and 3-D temperature and humidity at high temporal resolution to conduct high-value science investigations of tropical cyclones. TROPICS is providing rapid-refresh microwave measurements (median refresh rate of approximately 60 minutes for the baseline mission) over the tropics that can be used to observe the thermodynamics of the troposphere and precipitation structure for storm systems at the mesoscale and synoptic scale over the entire storm lifecycle. Hundreds of high-resolution images of tropical cyclones have been captured thus far by the TROPICS mission, revealing detailed structure of the eyewall and surrounding rain bands.  The new 205-GHz channel in particular (together with a traditional channel near 92 GHz) is providing new information on the inner storm structure, and, coupled with the relatively frequent revisit and low downlink latency, is already informing tropical cyclone analysis at operational centers.

The TROPICS constellation mission comprises four 3U CubeSats (5.4 kg each) in two low-Earth orbital planes inclined at approximately 33 degrees with a 550-km altitude. Each CubeSat comprises a Blue Canyon Technologies bus and a high-performance radiometer payload to provide temperature profiles using seven channels near the 118.75 GHz oxygen absorption line, water vapor profiles using three channels near the 183 GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel at 205 GHz that is more sensitive to precipitation-sized ice particles. TROPICS spatial resolution and measurement sensitivity is comparable with current state-of-the-art observing platforms. Data is downlinked to the ground via the KSAT-Lite ground network with latencies better than one hour. NASA's Earth System Science Pathfinder (ESSP) Program Office approved the separate TROPICS Pathfinder mission, which launched into a sun-synchronous orbit on June 30, 2021, in advance of the TROPICS constellation mission as a technology demonstration and risk reduction effort. The TROPICS Pathfinder mission continues has yielded useful data for 30+ months of operation and has provided an opportunity to checkout and optimize all mission elements prior to the primary constellation mission.

How to cite: Blackwell, W. and the TROPICS Science Team: Tropical Cyclone and Convective Storm Observations with the NASA TROPICS Constellation Mission, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21231, https://doi.org/10.5194/egusphere-egu24-21231, 2024.

EGU24-21379 | Posters on site | AS1.10

Investigating SST's Role in Seasonal Climate Variations: A WRF Model Analysis in the Tropical Zone, Thailand 

Surapong Lerdrittipong, Jian Zhong, Martin Widmann, Christopher Bradley, and Simon Dixon

The phenomenon of climate change, with its unique alterations in global temperatures and weather trends, presents a mounting obstacle for accurate weather prediction and climate simulation. This study uses the Weather Research and Forecasting (WRF) model to investigate the impact of variations of Sea Surface Temperature (SST) during the rainy season (17 May to 31 Oct 2016). The research aims to quantify the effect of changes in SST (0.5 to 2.0 degrees Celsius) in a climate-sensitive period. Utilising model configured for Thailand's specific geographic and climatic conditions, the study integrates SST data derived from satellite measurements and observations assess temperature, precipitation, and extreme weather events. Our results indicate the pronounced sensitivity of the WRF model to SST variations, with notable discrepancies in predicting rainfall patterns and temperature anomalies. These findings emphasise that SST is a critical factor in climate modelling and the need for accurate SST input in forecasting models, especially in the context of climate change. The study contributes to a better understanding of the WRF model's capabilities and limitations in simulating seasonal climate variations in tropical regions. It may also stress the importance of the governments to engage in effective water and irrigation management strategies, including improved drainage systems and adaptive agricultural practices, to mitigate climate change impacts like flooding and drought. Further research is recommended for other seasons and extended periods for a deeper understanding of the WRF model's performance against evolving climate dynamics.

How to cite: Lerdrittipong, S., Zhong, J., Widmann, M., Bradley, C., and Dixon, S.: Investigating SST's Role in Seasonal Climate Variations: A WRF Model Analysis in the Tropical Zone, Thailand, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21379, https://doi.org/10.5194/egusphere-egu24-21379, 2024.

EGU24-21680 | Posters virtual | AS1.10

Identification and characterization of hailstorms over France using DPR-GPM sensor 

Laura Rivero Ordaz, Andrés Merino, Andrés Navarro, Francisco Javier Tapiador, José Luis Sánchez, and Eduardo García-Ortega

Severe weather events, particularly hailstorms with large hydrometeors, cause heavy losses worldwide. The south of France is one of the European regions most affected by these hydrometeors and is also one of the most studied because an extensive hailpad network of detection devices that has been in operation there for more than three decades. These direct observations are extremely useful because provide a very complete and reliable "ground truth". Space-based sensors are becoming increasingly important in monitoring hailstorms. Global Precipitation Measurement (GPM) is an international mission designed to advance precipitation measurements from multispectral sensors. The GPM core satellite carries a powerful and unprecedented Dual-Frequency Precipitation Radar (DPR) for studying 3D precipitation characteristics. Furthermore, it improves the accuracy of precipitation estimation and facilitates the analysis of the microphysical structure of clouds. The objective of the present work was to evaluate the DPR sensor capability in identifying hailstorms. Data from more than 1000 hailpads during eight field campaigns in southern France were used. We identified eight hailstorms over France where DPR data were coincident with ground-based observations from hailpad network during 2014–2021. In addition, variables provided by the DPR sensor indicative of hail presence were studied. The Ku band demonstrated greater capacity in identifying hailstorms. Storms with larger reflectivity values (≥50 dBZ, Ku band), both near the surface and throughout the vertical column, were those with a more clearly defined vertical structure and thus more powerful convective development. The intensity of these hailstorms was confirmed with the ground-based data. This work could contribute to enhancing the detection and prediction of hailstorms, thereby helping to mitigate the associated risks.

How to cite: Rivero Ordaz, L., Merino, A., Navarro, A., Tapiador, F. J., Sánchez, J. L., and García-Ortega, E.: Identification and characterization of hailstorms over France using DPR-GPM sensor, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21680, https://doi.org/10.5194/egusphere-egu24-21680, 2024.

EGU24-1436 | ECS | Posters on site | AS1.11

Response of Snow Cloud Bands to Sea Surface Temperatures over Japan Sea 

Kaito Sato and Masaru Inatsu

The response of snow cloud bands to the increase in sea surface temperatures (SSTs) over the Japan Sea was investigated. We focused on a typical snowfall event in Japan by intense cloud bands around a convergence zone on December 25, 2021. After confirming that a regional atmospheric model fairly reproduced the event, we conducted three sensitivity experiments replacing the initial and boundary values with air temperatures and/or SSTs uniformly increasing by 4 K. The results revealed that the model experiment with higher SSTs or lower air temperatures supplied more evaporation to the planetary boundary layer, which encouraged the higher cloud to along the convergence zone. This dominated the transversal mode (T-mode) of cloud bands in the east of the zone, diagnosed by a newly developed technique that discriminates it from the longitudinal mode (L-mode) by means of the absolute value of horizontal advection of hydrometers. In contrast, the experiment with lower SSTs or higher air temperature exhibited wider areas dominated by the L-mode cloud bands over the Japan Sea.

How to cite: Sato, K. and Inatsu, M.: Response of Snow Cloud Bands to Sea Surface Temperatures over Japan Sea, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1436, https://doi.org/10.5194/egusphere-egu24-1436, 2024.

EGU24-2859 | ECS | Orals | AS1.11

Importance of Age of Convective Clouds for Explosive Ice Crystal Number Growth via Secondary Ice Production 

Deepak Waman, Sachin Patade, Arti Jadav, Vaughan Phillips, and Corinna Hoose

In many aircraft studies of natural convective clouds (CCs), it has long been observed that at subzero levels warmer than –38oC, the number concentrations of ice particles exceed the number concentration of available active ice nuclei particles (INPs). This suggests that following initial primary ice formation via INP activity at these levels in CCs, there must be some natural mechanisms present to enhance the number concentration of ice crystals, known as secondary ice production (SIP) mechanisms. SIP may form 1) during riming of supercooled cloud droplets between –3 and –8oC (Hallett-Mossop [HM] process), and during 2) fragmentation of freezing raindrops, 3) ice-ice collision, and 4) sublimation of ice particles. However, the relative importance of these SIP processes may differ for differing cloudy conditions.

The present study discusses the importance of the age of the simulated CCs in their lifecycle to determine which SIP process is active. The degree of enhancement in the number concentrations of ice crystals due to SIP activity is defined using the term called ‘ice enhancement’ (IE) ratio. A line of CCs observed during the MC3E campaign in 2011 over Oklahoma, USA was simulated using the WRF-based Aerosol-Cloud (AC) model for a 3D mesoscale domain. AC initiates primary ice by predicting the INP activity of solid aerosol particles such as mineral dust, black carbon, and biological particles. Furthermore, AC forms secondary ice from the SIP processes mentioned above. The simulated microphysical characteristics of the MC3E clouds agree well with the coincident aircraft, ground-based, and satellite observations, with errors of ±30%.

It is predicted that for relatively young developing CCs, with their tops warmer than –15oC, the HM process and raindrop-freezing fragmentation dominate the overall ice enhancement, creating an IE ratio as high as 104. As the cloud goes through its lifecycle, becoming mature, fragmentation in ice-ice collision becomes prolific, forming IE ratios of about 103, both in updraft and downdraft regions. While it is weak (IE ratios < 10) in the updraft regions, fragmentation in sublimation is predicted to create IE ratios of up to about 102.

How to cite: Waman, D., Patade, S., Jadav, A., Phillips, V., and Hoose, C.: Importance of Age of Convective Clouds for Explosive Ice Crystal Number Growth via Secondary Ice Production, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2859, https://doi.org/10.5194/egusphere-egu24-2859, 2024.

EGU24-4086 | Orals | AS1.11

Advancements in Cold Cloud Physics: Insights from a Decade of Airborne In-Situ Measurements with the PHIPS Instrument  

Emma Järvinen, Martin Schnaiter, Guanglang Xu, and Shawn Wagner

Airborne in-situ measurements provide a valuable opportunity to measure ice cloud properties in their natural atmospheric contexts, significantly contributing to our understanding of complex atmospheric processes. Traditional in-situ measurement techniques, relying on forward scattering, shadowgraphs or holography, have provided valuable insights into cloud particle size information and shape. However, finding answers to unresolved research questions often requires alternative and more advanced measurement technologies.

In this talk, we discuss one of those more advanced airborne instruments, a single-particle cloud imager and nephelometer (PHIPS), and review its first decade of airborne operations. PHIPS was intended to unravel the link between ice crystal microphysics and angular light scattering properties in cirrus clouds on a single particle basis. We demonstrate how the combination of angular scattering function measurements with simultaneous in-situ microscopy can be used to develop new parameterisations of ice cloud single-scattering properties for radiative transfer models. Furthermore, we explore the distinctions between these observational-based parameterisations and conventional parameterisations assuming idealised ice crystal shapes.

The single-particle light scattering function, detected with high enough angular resolution, emerges as a potent tool to distinguish between spherical and aspherical particles. Consequently, such measurements could be used to reliably discriminate hydrometeor phases in mixed-phase clouds. We illustrate how this method provides new insights into the ice formation via secondary ice processes in Southern Ocean boundary layer clouds. Additionally, we present first attempts to evaluate parameterisations for secondary ice processes in numerical models (CAM6 and CM1) based PHIPS observations. 

Our results underscore the necessity of airborne in-situ measurements and more advanced technologies in improving our understanding of fundamental ice cloud physics. This leads to more realistic parameterisations of microphysical processes as well a radiative properties of ice and mixed-phase clouds to be used in future climate and weather predictions. 

How to cite: Järvinen, E., Schnaiter, M., Xu, G., and Wagner, S.: Advancements in Cold Cloud Physics: Insights from a Decade of Airborne In-Situ Measurements with the PHIPS Instrument , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4086, https://doi.org/10.5194/egusphere-egu24-4086, 2024.

EGU24-4359 | ECS | Orals | AS1.11

The competing effect of aerosols on stratiform mixed-phase clouds 

Diego Villanueva

Due to limited in-situ observations, spaceborne retrievals of cloud top phase are often used to study the behaviour of mixed-phase clouds and their sensitivity to aerosols. By stratifying 35 years of cloud observations by temperature and cloud thickness, we gained valuable insights into the interplay between aerosols and mixed-phase clouds.

First, there is evidence that the ice-to-liquid frequency (ILF) is dominated by two sources of cloud ice: For thin clouds, a cirrus-origin due to ice sedimentation from temperatures colder than -38 dgC, and for thick clouds, a glaciation-origin due to aerosol-driven droplet freezing. These different sources of ice may explain differences in the ILF from different retrieval methods. For example, active instruments, which are more sensitive to thin cirrus, may estimate a higher ILF compared to passive instruments, which are more sensitive to thick clouds.

Second, we find that in extratropical thick mixed-phase clouds, aerosols have two dominant effects on the ILF: For liquid clouds, aerosols increase cloudiness at warm temperatures, but they decrease cloudiness at cold temperatures. Our results suggest that precipitation inhibition (by increasing the number of droplets) and enhanced cloud glaciation (by increasing the rate of droplet freezing at cold temperatures) can explain this behaviour. As a result, we find that the indirect effect of aerosols through mixed-phase clouds is strongly temperature dependent.

Third, at cold temperatures, both dust aerosol and organic aerosols are temporally correlated with higher ILF on a monthly basis. Spatially, this correlation coincides with regions downwind of deserts and highly biologically productive regions in the ocean. We also find that the ILF increases logarithmically with increasing aerosol concentrations, at a rate consistent with the behaviour reported from laboratory studies. Thus, for the first time, we provide a link between laboratory studies of droplet freezing and space-based studies of cloud glaciation.

How to cite: Villanueva, D.: The competing effect of aerosols on stratiform mixed-phase clouds, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4359, https://doi.org/10.5194/egusphere-egu24-4359, 2024.

Past investigations have shown that gravity waves can affect both when/where cirrus form in the Tropical Tropopause Layer (TTL) and the ice concentrations produced by homogeneous freezing nucleation.  Here, we use high-resolution two-dimensional simulations to investigate the impacts of wind shear and gravity waves on TTL cirrus evolution after the nucleation stage is complete.  We use a bin microphysics model to simulate the physical processes of ice crystal growth/sublimation, advection, and sedimentation.  Gravity wave temperature and wind perturbations are calculated using a Fourier series of wave frequencies with periods ranging from 1 day to near the Brunt Vaisala period, with amplitudes based on aircraft and superpressure balloon measurements.  The simulations are initialized based on high-altitude aircraft measurements of a case just after a homogeneous-freezing ice nucleation event has produced numerous small crystals in a supersaturated environment.  We show that wind shear alone rapidly alters the structure of the cloud, and strong shear can significantly reduce the cloud lifetime.  High-frequency gravity wave temperature oscillations accelerate the reduction of ice concentration as the cloud evolves.  Gravity waves can temporarily increase or decrease cloud optical depth (depending on the initial wave temperature tendencies), but the overall lifetime of the cloud is reduced by the waves.  We will further discuss the relative importance of different wave frequencies on the evolution of TTL cirrus.

How to cite: Jensen, E., Ueyama, R., and Pfister, L.: How do wind shear and gravity waves affect the evolution of optically thin cirrus in the tropical tropopause layer?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4453, https://doi.org/10.5194/egusphere-egu24-4453, 2024.

EGU24-5684 | Posters on site | AS1.11

Improved ice cloud phase function for passive remote sensing 

Romain Joseph, Emmanuel Fontaine, and Jérôme Vidot

As part of the NWCSAF (Nowcasting Satellite Application Facility), the CNRM participates in the retrieval of cloud properties from geostationary satellite observations. These retrievals include the Cloud Mask and Cloud Types classification, thermodynamics properties at the macroscopic scales (Cloud Top Temperature and Height) as well as microphysical cloud properties (effective radius, optical thickness, liquid and ice water path). The cloud optical properties (including scattering, absorptions and emissions) are derived from cloud microphysical model in order to perform radiative transfer simulations. In this study, I combine cloud microphysical properties retrieved from DARDAR and in-situ observations with ERA-5 reanalysis to perform radiative transfer simulations with RTTOV. Hence, these simulation are compared with Meteosat Second Generation observations. Our goal is to identify the cloud properties that can affect the difference between observations and simulations in order to propose a new parameterization of the ice cloud scattering phase function in the radiative transfer model RTTOV (Radiative Transfer for TOVS).

How to cite: Joseph, R., Fontaine, E., and Vidot, J.: Improved ice cloud phase function for passive remote sensing, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5684, https://doi.org/10.5194/egusphere-egu24-5684, 2024.

EGU24-6010 | ECS | Posters on site | AS1.11

Ice crystal images classification using semi-supervised contrastive learning 

Yunpei Chu, Huiying Zhang, Xia Li, and Jan Henneberger

Ice crystals play a crucial role in precipitation formation and radiation budget, with their various shapes influencing these processes differently. The shape of ice crystals is related to the environmental conditions (i.e. temperature) under which the ice crystal forms and the microphysical processes that ice crystal experiences. Therefore, ice crystal shape classification is important for understanding conditions and microphysical processes in cloud. However, current methods are mainly supervised learning algorithms like convolutional neural networks (CNNs), heavily relying on extensive manual labelling, which requires substantial labor. Moreover, the limitations in human’s knowledge of ice crystals and the bias of human subjectivity in classification hinder the generalization ability of these networks. In response to these challenges, we propose a semi-supervised algorithm for ice crystal classification. We use data from the 2019 Ny-Ålesund NASCENT campaign, collected by a holographic imager mounted on the balloon-borne platform HoloBalloon, which includes 18,864 ice crystal images. In our algorithm we initially extract key features from ice crystal images using an unsupervised learning network, prioritizing generalization rather than dependence on labelled data, which ensures unbiased feature identification. Subsequently, a small subset of images is manually labelled into nineteen categories based on a multi-label classification scheme that consider both basic habits and microphysical processes. The classification accuracy of our hybrid algorithm on nineteen categories is similar to the performance supervised learning algorithm. This hybrid algorithm not only reduces the labor needed for manual labelling but also incorporates physics-based constraints, which prevents the network from making unfounded assumptions, thus offering a robust and efficient framework for ice crystal classification.

How to cite: Chu, Y., Zhang, H., Li, X., and Henneberger, J.: Ice crystal images classification using semi-supervised contrastive learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6010, https://doi.org/10.5194/egusphere-egu24-6010, 2024.

The distribution of ice particles strongly affects the microphysical processes in mixed-phase clouds, but the inhomogeneity of ice distribution is not well understood. In this presentation the inhomogeneity and clustering of ice distribution in a stratiform cloud system is quantitatively analyzed using the pair correlation function (PCF) method, based on airborne in-situ measurements from northeast China. The results show that ice clusters on scales of a few kilometers dominate the inhomogeneity of the ice distribution. Due to the cumulative impact of ice clusters on different scales, the probability of finding relative high ice concentration within a lag of 80 m can be enhanced by 0.1 to 3.5 times. On average, the scale of ice cluster is ~100 m for a sampling distance of 1 km, and increases to 3.2 km for a sampling distance of 20 km. It is also found that the ice growth is not fast enough to cluster the ice water content (IWC), and the inhomogeneity of IWC is strongly influenced by ice generation in addition to ice growth in the observed clouds. The results provide potentially important information to improve the parameterizations of microphysics in numerical weather prediction and climate models.

How to cite: Yin, Y., Yang, J., Deng, Y., and Jing, X.: Quantifying the spatial inhomogeneity of ice concentration in mixed-phase stratiform clouds using airborne observations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6934, https://doi.org/10.5194/egusphere-egu24-6934, 2024.

EGU24-7185 | ECS | Posters on site | AS1.11

Impact of Prognostic Graupel Density on Simulated Precipitating Convections 

Sun-Young Park, Kyo-Sun Sunny Lim, Kwonil Kim, Gyuwon Lee, and Jason A. Milbrandt

Ice particles in cloud microphysics schemes are traditionally categorized as ice crystals, snow, graupel, and/or hail. Each category is defined by static parameters that determine density, diameter-mass relationship, and diameter-fall speed relationship. Several previous studies have reported considerable sensitivity in simulated precipitation systems based on these fixed parameters. This study introduces a prognostic approach for graupel density in the Weather Research and Forecasting (WRF) Double-Moment 6-class (WDM6) microphysics scheme, based on the work of Milbrandt and Morrison (2013). This allows graupel density to vary from 100 to 900 [kg/m3]. The modified WDM6 is tested for idealized squall line and winter snowfall cases over the Korean Peninsula. In the idealized squall line case, simulation results reveal variant graupel density in time and space, according to the evolution of squall line. For winter snowfall cases, simulations using the modified WDM6 show improved statistical skill scores, such as the root mean square error and bias, compared to the original WDM6, mitigating the positive precipitation bias simulated in the original WDM6. The modified WDM6 increases surface graupel amounts and decreases graupel suspended in the atmosphere due to faster sedimentation of graupel. Therefore, the major microphysical processes that generate graupel are influenced, subsequently reducing surface snow and precipitation over mountainous regions. Importantly, the modified WDM6 adeptly captures the relationship between graupel density and fall velocity, as verified by 2D video disdrometer measurements. *This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (grant no. RS-2023-00272751).

How to cite: Park, S.-Y., Lim, K.-S. S., Kim, K., Lee, G., and Milbrandt, J. A.: Impact of Prognostic Graupel Density on Simulated Precipitating Convections, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7185, https://doi.org/10.5194/egusphere-egu24-7185, 2024.

EGU24-7194 | ECS | Posters on site | AS1.11

Double-moment approach for snow and graupel in the WDM6 scheme and its effects on simulated precipitation 

Juhee Kwon, Sun-Young Park, Kyo-Sun Sunny Lim, Kwonil Kim, and Gyuwon Lee

The Weather Research and Forecasting (WRF) Double-Moment 6-class (WDM6) microphysics scheme only predicts the number concentrations for CCN and liquid-phase hydrometeors such as cloud water and rain. Although the double-moment approach for the cloud ice is recently introduced in the WDM6 scheme by Park and Lim (2023), the single-moment approach, in which only mixing ratio is prognosed, is still employed for solid-phase precipitating hydrometeors such as snow and graupel. In this study, the double-moment approach is introduced to WDM6 for all hydrometeors by adding prognostic number concentration of snow and graupel. To evaluate the effects of prognostic snow and graupel number concentrations, simulated results between the new and original versions of WDM6 scheme are compared. The four summer-precipitating (cold-type and warm-type; Kim et al. 2019) and seven winter-precipitating convection cases (cold-low type and warm-low type; Ko et al. 2022) are selected to evaluate the new scheme. In comparison to the original WDM6 scheme, the new scheme exhibits increased snow mixing ratio, except for cold-type summer cases. Additionally, the new scheme reduces the graupel mixing ratio and rain number concentration for all cases. In the new scheme, the raindrop size becomes larger due to the reduced rain number concentration, which is more consistent results with the observation data from 2DVD. Furthermore, larger raindrop size in the new scheme makes the evaporation inefficient. Therefore, the new scheme produces more surface precipitation than the original one. Meanwhile, among total 11 cases, the new scheme improves the equitable treat score (ETS) for eight cases and probability of detection (POD) for seven cases.

 

*This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government. (MSIT) (RS-2023-00208394)

How to cite: Kwon, J., Park, S.-Y., Lim, K.-S. S., Kim, K., and Lee, G.: Double-moment approach for snow and graupel in the WDM6 scheme and its effects on simulated precipitation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7194, https://doi.org/10.5194/egusphere-egu24-7194, 2024.

EGU24-7424 | ECS | Orals | AS1.11

Characterizing the influence of riming on the spatial variability of ice water content in mixed-phase clouds using airborne data 

Nina Maherndl, Manuel Moser, Mario Mech, Nils Risse, Aaron Bansemer, and Maximilian Maahn

Observations show that ice water content (IWC) is not distributed homogeneously in mixed-phase clouds (MPC). Instead, high IWC tends to occur in clusters. However, it is not sufficiently understood, which ice crystal formation and growth processes play a dominant role in IWC clustering. Additionally, spatial scales of IWC clusters are not well known. This leads to uncertainties of atmospheric models in representing MPC.

Riming, which occurs when liquid water droplets freeze onto ice crystals, is an important ice crystal growth process. It plays a key role in precipitation formation in MPC by efficiently converting liquid cloud water into ice.

In this study, we analyze the influence of riming on IWC variability  and compare shallow Arctic MPC to mid-latitude winter storms. We use airborne data collected during the HALO-(AC)3 field campaign performed in spring 2022 west of Svalbard, and the IMPACTS field campaign, which took place over the eastern USA (winter 2020, 2022 and 2023). In both campaigns, two aircraft were flying in formation collecting closely spatially collocated and almost simultaneous in situ and remote sensing observations.

We quantify the amount of riming using the normalized rime mass M, which we retrieve from a closure of measured radar reflectivity Ze and measured in situ particle size distributions (PSD). As forward operators in the M retrieval, we use the Passive and Active Microwave radiative TRAnsfer tool (PAMTRA) and empirical relationships of M and particle properties. We calculate IWC from the retrieved M and the measured PSD. In addition, we calculate IWC assuming no riming (M = 0) and perform forward simulations of Ze for the (theoretical) unrimed case. 
Then, we quantify spatial variability of IWC and Ze with and without riming using autocorrelation, pair correlation, and power spectra. Further, we compare shallow Arctic MPC to mid-latitude winter storms and analyze the role of ice particle number concentration and size.

This will lead to a better understanding of the spatial scale and driver of IWC variability and thereby help to improve modeling of MPC.

How to cite: Maherndl, N., Moser, M., Mech, M., Risse, N., Bansemer, A., and Maahn, M.: Characterizing the influence of riming on the spatial variability of ice water content in mixed-phase clouds using airborne data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7424, https://doi.org/10.5194/egusphere-egu24-7424, 2024.

EGU24-7679 | ECS | Orals | AS1.11

Microphysical influence on cloud radiative effect during New Mexico deep convective cloud cases 

Declan Finney, Alan Blyth, Paul Field, Martin Daily, Benjamin Murray, and Steven Boeing

Cloud feedbacks associated with anvil cirrus are some of the most uncertain. The Deep Convective Microphysics EXperiment (DCMEX) aims to reduce this uncertainty by improving the representation of microphysical processes in climate models. In support of this aim, we present analysis of the cloud radiative properties from cloud-resolving simulations with the Met Office Unified Model (UM). We apply the Cloud AeroSol Interacting Microphysics (CASIM) module within the UM. 

Overall, the results suggest that an increase in cloud droplet number or ice nucleating particles can increase the reflectivity of anvil cloud. However, the magnitude of these effects shows a dependency on environmental conditions such as wind shear.

Our simulations are based upon a number of case studies from the DCMEX 2022 field campaign held over the Magdalena Mountains in central New Mexico. In the campaign, numerous cases of deep convective cloud formation were observed using the FAAM aircraft, radar,  ground-based aerosol instruments,  and automated cameras. A number of observation-informed, sensitivity simulations have been performed to explore the representation of cloud microphysics within the UM-CASIM model. 

With the model sensitivity simulations we explore the effect of a range of measured microphysical features. The features include: 1) Cloud droplet number concentration, 2) Temperature dependence of heterogeneous freezing, and 3) Secondary ice formation rate from the Hallett-Mossop process.

There is consistently higher outgoing radiation from high cloud, and across the whole domain, in experiments using higher cloud droplet concentration. This aggregate radiative effect manifests from changes in anvil cloud area and reflectivity. Experiments using the ice nucleating particle-temperature relationship derived from DCMEX observations are compared to a simulation using the widely-used Cooper curve. We find an increase in high cloud reflectivity in several cases, but the magnitude of the difference varies from 0-10%, depending on environmental conditions. Overall, the sensitivity experiments vary in all-domain mean outgoing radiation by greater than 10 Wm-2.

Our results offer an important contribution to the understanding of anvil cloud effects on climate through describing the potential effect of small-scale processes on radiation. These microphysical processes are not well represented in climate models. Our finding that their effect depends on environmental conditions encourages a focus on evaluation methods that take this into consideration.

How to cite: Finney, D., Blyth, A., Field, P., Daily, M., Murray, B., and Boeing, S.: Microphysical influence on cloud radiative effect during New Mexico deep convective cloud cases, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7679, https://doi.org/10.5194/egusphere-egu24-7679, 2024.

EGU24-7972 | Posters on site | AS1.11

Determination of Cirrus Occurrence and Distribution Characteristics Over the Tibetan Plateau Based on the SWOP Campaign 

Zhen Yang, Dan Li, Jiali Luo, Wenshou Tian, Zhixuan Bai, Qian Li, Jinqiang Zhang, Haoyue Wang, Xiangdong Zheng, Holger Vömel, Frand G. Wienhold, Thomas Peter, Dale Hurst, and Jianchun Bian

Balloon sounding with the Compact Optical Backscatter Aerosol Detector (COBALD) and Frost Point hygrometers (FPs) provides in situ data for a better understanding of the vertical distribution of cirrus clouds. In this study, eight summer balloon-borne measurements in Kunming (2012, 2014, 2015, and 2017) and Lhasa (2013, 2016, 2018, and 2020) over the Tibetan Plateau were used to show the distribution characteristics of cirrus clouds. Differences of cirrus occurrence were compared by different indices: the backscatter ratio (BSR) at a 455 nm/940 nm wavelength (BSR455 > 1.2/BSR940 > 2), the color index (CI > 7), and the relative humidity with respect to ice (RHice > 70%). Analysis of the profiles indicated that BSR455 > 1.2 was the optimal criterion to identify the cirrus layer and depict the distribution of the CI and RHice within cirrus clouds. The results showed that the median CI (RHice) within the cirrus clouds at both sites was mostly in the 18–20 (90%–110%) range at pressures below 120 hPa. Furthermore, the balloon-borne measurements combined with Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) measurements indicated a high frequency of cirrus occurrence near the tropopause in Kunming and Lhasa. The top height of cirrus occurrence at both sites was above the cold point tropopause and the lapse rate tropopause. Both Kunming and Lhasa had the highest frequency of thin cirrus clouds in the 0–0.4 km vertical cirrus thickness range.

How to cite: Yang, Z., Li, D., Luo, J., Tian, W., Bai, Z., Li, Q., Zhang, J., Wang, H., Zheng, X., Vömel, H., Wienhold, F. G., Peter, T., Hurst, D., and Bian, J.: Determination of Cirrus Occurrence and Distribution Characteristics Over the Tibetan Plateau Based on the SWOP Campaign, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7972, https://doi.org/10.5194/egusphere-egu24-7972, 2024.

EGU24-8065 | ECS | Posters on site | AS1.11

Measuring dynamical properties of atmospheric convection using C2OMODO: a tandem of microwave radiometers 

Thomas Lefebvre, Helene Brogniez, Laura Hermozo, and Frédéric Chevalier

Convective clouds serve as a primary mechanism for the transfer of thermal energy, moisture, and momentum through the troposphere. The lack of understanding of the convective updraft properties and their relationship to environmental factors limit our ability to represent deep convection and its feedbacks in large-scale circulation models. Satellites are the only viable means of efficiently sampling tropical convective clouds, predominantly found in ocean-covered regions.

The C2OMODO Project, proposed by CNES as contribution to the AOS NASA program scheduled for 2029, aims to target the vertical development of deep convective cells. The proposed concept is a tandem of identical microwave radiometers aboard two different satellites in the same orbit, separated by a small-time delay, between 1 and 2 minutes. Each radiometer will measure at 89 GHz, 183 GHz (6 Channels) and 325 GHz (6 Channels), with footprints of 10, 5, and 3 km, respectively. These observations inform about the vertical distribution of ice, thanks to the scattering of radiation (in the line of ICI, STERNA, SAPHIR instruments). The derivative-time measurements of the C2OMODO tandem will provide information on the updraft dynamics of growing convective cells. Furthermore, C2OMODO will contribute to enhance the understanding of the life cycle of convective systems and improve the representation of deep convection in both weather prediction and climate models.

The aim of the presented study is to introduce the inversion method developed to estimate convective mass flux of ice from C2OMODO measurements, based on the variational approach (1D-VAR). Assimilation approaches, based on Bayesian theory, are commonly applied to the inversion of satellite observations. To simulate C2OMODO measurements, the radiative transfer model, RTTOV, serves as the forward operator while the mesoscale model MESO-NH is used as nature-like representation for atmospheric state. Only growing convective cells are selected in this work. The general 1D-VAR approach is adapted to integrate derivative-time measurements, thereby directly incorporating the dynamical properties in the restitution process. In this presentation, we describe the ongoing development of the variational approach. Additionally, the restitution of vertical ice mass flux and the performance of the 1D-VAR be discussed.

The ongoing development of this method has yielded promising preliminary results, instilling optimism about the wealth of information that will be accessible through C2OMODO.

How to cite: Lefebvre, T., Brogniez, H., Hermozo, L., and Chevalier, F.: Measuring dynamical properties of atmospheric convection using C2OMODO: a tandem of microwave radiometers, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8065, https://doi.org/10.5194/egusphere-egu24-8065, 2024.

EGU24-8070 | ECS | Posters on site | AS1.11

The response of mixed-phase and ice clouds to volcanic eruptions- A model case study of the Raikoke eruption 2019 

Melina Sebisch, Corinna Hoose, Julia Bruckert, and Gholamali Hoshyaripour

Aerosol-cloud-interactions are one of the major causes of uncertainty in the radiative forcing as presented in the IPCC report WG1 (2021). The aerosols in the atmosphere can act as cloud condensation nuclei (CCNs) or ice nucleating particles (INPs) and affect cloud properties. A quantification of the impact of aerosols on these properties is difficult since clouds are also strongly affected by synoptic conditions.

Volcanic eruptions are an ideal testbed as they cause a local perturbance of aerosol concentrations in the atmosphere. The emitted aerosols such as SO2 reacting to sulfuric acid or ash can act as CCNs or INPs respectively. By comparing a simulation with and without the volcanic eruption the impact can be quantified directly. The simulation with an eruption can be validated by comparison to observations, e.g. satellite data.

In the presented work, the eruption of the Raikoke volcano in 2019 is simulated using the ICOsahedral Nonhydrostatic model (ICON) and the module for Aerosols and Reactive Trace gases (ART). The model is run in limited area mode on a R2B10 grid with about 2.5 km horizontal resolution for a time span of 3 days. The volcanic plume is simulated using a setup provided by J. Bruckert. During this time, the plume overlaps with cloud systems associated with a low-pressure system east of the volcano. The simulations with and without the eruption are compared to observational data to improve the implemented interaction mechanisms between cloud particles and volcanic aerosols with a focus on ice nucleation due to volcanic ash particles. Additionally, a new parameterization for volcanic ash formulated by Umo et al. (2021) based on laboratory experiments is implemented and compared to the commonly used ice nucleation parameterizations for mineral dust by e.g. Ullrich et al. (2017). First results of an offline calculation of the ice nucleation active site show a decrease in the ice nucleating efficiency for the parameterization for volcanic ash.

These first results on the interactions between the volcanic ash plume and mixed-phase and ice clouds will be presented.

How to cite: Sebisch, M., Hoose, C., Bruckert, J., and Hoshyaripour, G.: The response of mixed-phase and ice clouds to volcanic eruptions- A model case study of the Raikoke eruption 2019, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8070, https://doi.org/10.5194/egusphere-egu24-8070, 2024.

EGU24-9194 | ECS | Posters on site | AS1.11

Patterns in dusty cirrus cloud formation mechanisms revealed by LES modeling study 

Kasper Juurikkala, Tomi Raatikainen, and Ari Laaksonen

Dusty cirrus clouds, a rare phenomenon occurring approximately a few times a year globally, are associated with desert dust plumes in the upper troposphere. The formation of these clouds involves a high-humidity layer above a mineral dust-rich layer. In the intermediate layer between these two layers, initially, a thin cirrus cloud forms heterogeneously on the mineral dust particles. The latent heat release caused by the ice nucleation and radiative cooling above the thin cirrus cloud layer cause instability and convection to occur. This convection uplifts mineral dust particles even higher until the humid layer is fully mixed with the mineral dust, resulting in the dusty cirrus covering the humid layer.
The objective of this study is to investigate the formation mechanisms of dusty cirrus clouds. Addressing the challenges highlighted by Seifert et al. (2023), current atmospheric models struggle to predict these events. This work aims to validate the hypothesis presented by Seifert and further advance the understanding of the formation mechanisms. The study involves a simulation study conducted using the UCLALES-SALSA large-eddy model (Tonttila et al., 2017). A case study is performed using the atmospheric conditions present during Saharan dust plumes over Europe in recent years.
The simulated dusty cirrus clouds show that the upward transport of the mineral dust is not as effective as in the regional model study by Seifert et al. (2023). This is because the mineral dust which gets uplifted initially sediments down back to the original mineral dust layer with the sedimenting ice crystals. Also, the predominant mechanism for the instabilization of the air in the initial stages of the cloud formation is the latent heat release caused by the ice nucleation and the growth of the ice crystals, rather than the radiative cooling suggested by Seifert et al. (2023).
In the future, simulations will be conducted using idealized cases to comprehensively understand the most relevant mechanisms involved in the formation of dusty cirrus clouds.

References

Seifert, A., Bachmann, V., Filipitsch, F., Förstner, J., Grams, C. M., Hoshyaripour, G. A., Quinting, J., Rohde, A., Vogel, H., Wagner, A., and Vogel, B. (2023) Aerosol–cloud–radiation interaction during Saharan dust episodes: the dusty cirrus puzzle, Atmos. Chem. Phys., 23, 6409–6430

Tonttila, J., Maalick, Z., Raatikainen, T., Kokkola, H., Kühn, T. and Romakkaniemi, S. (2017).
UCLALES-SALSA v1.0: a large-eddy model with interactive sectional microphysics for aerosol,
clouds and precipitation. Geosci. Model Dev., 10, 169-188

How to cite: Juurikkala, K., Raatikainen, T., and Laaksonen, A.: Patterns in dusty cirrus cloud formation mechanisms revealed by LES modeling study, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9194, https://doi.org/10.5194/egusphere-egu24-9194, 2024.

EGU24-9345 | Posters virtual | AS1.11

Secondary Ice Processes during a Medicane Evolution 

Georgia Sotiropoulou, Foteini Floka, and Platon Patlakas

The Mediterranean basin is characterized by cyclonic activity that can often lead to adverse weather conditions. Lately, there is an increasing interest to specific types of cyclones, such as medicanes, due to their dynamic characteristics. However, these events can also lead to extreme precipitation, often resulting in flooding and causing severe damage, with potential human casualties. While there is continuous effort to understand the  dynamic evolution of these  systems, little is known about the underlying microphysical processes. Secondary Ice Production (SIP) processes are ice multiplication mechanisms that have been frequently linked to the onset of heavy precipitation and the generation of high concentrations of precipitation particles. In this study we investigate the impact of four SIP mechanisms (rime-splintering, collisional break-up, drop-shattering, sublimation break-up) on the evolution of medicane Qendresa using the Weather and Research Forecasting (WRF) model. Qendresa occurred in 2014 mainly in the vicinity of Italy and Malta, causing three fatalities and at least $250 million in damages in Italy.

 

 

 

How to cite: Sotiropoulou, G., Floka, F., and Patlakas, P.: Secondary Ice Processes during a Medicane Evolution, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9345, https://doi.org/10.5194/egusphere-egu24-9345, 2024.

EGU24-10252 | ECS | Orals | AS1.11

Cloud thermodynamic phase from spectral and multi-angle polarimetric imaging with specMACS 

Anna Weber, Veronika Pörtge, Tobias Zinner, and Bernhard Mayer

We present a method to retrieve cloud thermodynamic phase from multi-angle polarimetric and spectral imaging. Spectral absorption differences between water and ice in the near infrared are commonly used to discriminate between liquid, mixed, and ice clouds. For example, the spectral slope between 1500 and 1700 nm increases with decreasing liquid cloud fraction. These methods are very sensitive to small amounts of ice in liquid clouds. On the other hand, the polarization signal of clouds shows different features depending on the cloud thermodynamic phase. The cloudbow is formed by single scattering on liquid cloud droplets. Observation of the cloudbow indicates the presence of liquid water while its absence indicates pure ice clouds. In addition the slope of the Q component of the Stokes vector for scattering angles in the range of 60 to 100 degree depends on the partitioning between liquid and ice phase. The polarimetric method is much more sensitive to small amounts of liquid water compared to the spectral method and represents cloud thermodynamic phase at cloud top. In addition, polarization is dominated by single scattering and thus does not suffer from 3D radiative effects.

Both methods are applied to data of the airborne hyperspectral and polarized imaging system specMACS measured during the HALO-(AC)3 campaign. specMACS provides wide-field and high spatial resolution data with a horizontal resolution down to a few 10m. By a combination of the spectral and multi-angle polarimetric observations we will retrieve cloud thermodynamic phase partitioning of single layer mixed-phase clouds and investigate spatial and temporal scales of phase transitions in low-level arctic mixed-phase clouds.

How to cite: Weber, A., Pörtge, V., Zinner, T., and Mayer, B.: Cloud thermodynamic phase from spectral and multi-angle polarimetric imaging with specMACS, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10252, https://doi.org/10.5194/egusphere-egu24-10252, 2024.

EGU24-10554 | ECS | Posters on site | AS1.11

Fragmentation of atmospheric ice particles due to collision 

Sudha Yadav, Pierre Grzegorczyk, Lilly Metten, Florian Zanger, Subir Kumar Mitra, Alexander Theis, and Miklós Szakáll

Experiments were conducted in the cold room of the wind tunnel laboratory at Johannes Gutenberg University Mainz, encompassing collisions between bare graupel-graupel, bare graupel-ice sphere, bare graupel-graupel with dendrites and bare graupel-snowflake. This study addresses the underrepresented domain of secondary ice processes in clouds, focusing on fragmentation due to ice-ice collisions and their role in augmenting ice particle concentration. For this study, graupels were created using a setup that simulates the natural rotation and tumbling motion of freely falling graupels. The first set of experiments aimed to recreate previous collision experiments by producing more realistic nature-like graupels, while also improving the ice crystal fragment detection and counting process. 2mm and 4mm sized graupels were chosen based on previous observational studies.

This research contributes vital preliminary data, including fragment number and size distribution, as well as their dependency on collision kinetic energy. For this purpose, new coefficients fitted on our experiments following the theoretical framework have also been proposed, which can be used to parameterize the number of fragments resulting from ice-ice collisions. Our study attempts to bridge the gap between laboratory observations and numerical simulations, advancing the accuracy of atmospheric models.

How to cite: Yadav, S., Grzegorczyk, P., Metten, L., Zanger, F., Kumar Mitra, S., Theis, A., and Szakáll, M.: Fragmentation of atmospheric ice particles due to collision, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10554, https://doi.org/10.5194/egusphere-egu24-10554, 2024.

EGU24-10561 | Orals | AS1.11

Impact of radiation, water vapour and ice clouds on the tropopause region 

Peter Spichtinger and Philipp Reutter

In the tropopause region, the thermal structure is strongly influenced by the interaction of radiation, ice clouds and water vapour. Features as the tropopause inversion layer as well as potentially unstable layers are suspected to be (partly) driven by radiative effects in connection with the frequently occurring large concentrations of water vapour (i.e. supersaturations with respect to ice) and ice clouds. Since there is a high variability of water vapour and ice clouds in terms of microphysical properties and vertical layers, it is still unclear under which conditions clouds and their precursor (i.e. water vapour) lead to a stronger or a weaker stratification, respectively.

In this study we investigate the interaction of radiation and clouds within an idealized framework of a combined cloud-radiation scheme within a vertical column. Using different environmental conditions in terms of water vapour concentrations, ice cloud properties, and thermal stratification we investigate the temporal evolution of the thermodynamic properties of the tropopause region. The results are statistically investigated for characterizing dominant impacts and feedbacks.

How to cite: Spichtinger, P. and Reutter, P.: Impact of radiation, water vapour and ice clouds on the tropopause region, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10561, https://doi.org/10.5194/egusphere-egu24-10561, 2024.

EGU24-10694 | ECS | Orals | AS1.11

Airborne and ground measurements for vertical profiling of secondary ice production during ice pellet  

Mathieu Lachapelle, Kenny Bala, Cuong Nguyen, Natalia Bliankinshtein, Keyvan Ranjbar, Margaux Girouard, Julie M. Thériault, Justin Minder, David Kingsmill, Jeffrey French, Mengistu Wolde, and Leonid Nichman

Predicting the accurate type of precipitation during winter storms is crucial for the implementation of mitigation measures such as aircraft deicing in commercial aviation or the spreading of salt and abrasives on roads. For this reason, a better understanding of the microphysical processes leading to winter precipitation types is essential. During freezing rain events, secondary ice produced by the freezing of supercooled raindrops via the fragmentation of freezing drops (FFD) process can initiate a chain reaction, potentially transitioning freezing rain into ice pellets. However, including this process in numerical weather prediction models is challenging due to the uncertainty in the efficiency of this mechanism. To bridge this gap, this study aims to evaluate the efficiency of the FFD process during ice pellet precipitation using measurements collected onboard the NRC Convair-580 research aircraft during the WINTRE-MIX field campaign, in February 2022. Specifically, measurements from two missed-approaches conducted in the Saint Lawrence Valley, Quebec, Canada during an ice pellet storm are analyzed. These missed-approaches provide unique datasets collected above, within, and below the ice pellet freezing altitude using in-situ and remote sensing instruments. In the region characterized by completely frozen ice pellets, a bi-modal particle size distribution, indicative of secondary ice production, was measured. Observations from imaging and optical-array probes suggest that particles smaller than 200 µm in diameter were, likely, non-spherical ice crystals, whereas the particle size mode with the larger diameters was associated with ice pellets. The observations of fractured ice pellets and ice pellets with bulges and spicules on most large particles suggested the occurrence of the FFD process. Subsequently, the measured number concentration of small ice particles, which was of the order of 500 L-1, was compared with the number concentration of ice particles simulated through existing parametrizations of secondary ice production. This analysis  will be valuable for selecting the most accurate FFD process parametrization to use for freezing rain and ice pellets simulation. 

How to cite: Lachapelle, M., Bala, K., Nguyen, C., Bliankinshtein, N., Ranjbar, K., Girouard, M., M. Thériault, J., Minder, J., Kingsmill, D., French, J., Wolde, M., and Nichman, L.: Airborne and ground measurements for vertical profiling of secondary ice production during ice pellet , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10694, https://doi.org/10.5194/egusphere-egu24-10694, 2024.

EGU24-11022 | Posters on site | AS1.11

Sensitivity of Microphysical Properties of Mixed-Phase Clouds on Model Resolution and Microphysics Scheme in ICON 

Corinna Hoose, Deepak Waman, Behrooz Keshtgar, Christian Barthlott, and Gabriella Wallentin

Microphysical processes in the mixed-phase clouds play an important role in modulating the earth’s weather and climate. However, uncertainties in both observational data and model parameterization of microphysical properties (e.g., number concentrations of ice particles) constrains our ability to accurately simulate mixed-phase clouds and their impact with weather and climate models. Model configuration, such as one- or two-moment microphysical schemes, horizontal and vertical resolution of the model can affect the representation of cloud and precipitation processes and cloud radiative effects. For simplicity, many numerical models use 1-moment microphysical scheme to represent clouds. However, this scheme may not represent microphysical and precipitation processes accurately as it only predicts the mass or number mixing ratios of hydrometeors. To address this issue, the present study uses the Icosahedral Non-hydrostatic (ICON) model to assess the sensitivity of model configuration by comparing the predicted microphysical properties with the observations. In ICON, the one-moment microphysical scheme represents mass fractions of five cloud as well as precipitation particles such as: cloud water and ice, snow, graupel, and rain. Furthermore, the two-moment microphysical scheme in predicts both mass and number mixing ratios of hail and the five prognostic variables mentioned above. For the above discussed purpose, a case of observed mixed-phase clouds will be simulated with ICON. The profiles of the simulated cloud microphysical properties will be compared with the coincident aircraft and ground-based observations. Furthermore, various simulations will be performed by varying the vertical as well as horizontal resolution to analyse the changes in model predicted microphysical properties.

How to cite: Hoose, C., Waman, D., Keshtgar, B., Barthlott, C., and Wallentin, G.: Sensitivity of Microphysical Properties of Mixed-Phase Clouds on Model Resolution and Microphysics Scheme in ICON, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11022, https://doi.org/10.5194/egusphere-egu24-11022, 2024.

EGU24-11218 | Orals | AS1.11

Midlatitude cirrus cloud investigations from ground-based lidar and ERA-5 re-analysis 

Florian Mandija, Dunya Alraddawi, Philippe Keckhut, and Sergey Khaykin

Cirrus as high-altitude clouds are formed at the highest layers of the troposphere, usually at the altitude range 5,000 – 14,000m. Cirrus clouds are composed mainly by asymmetric ice crystals, which are formed during the freezing process of the water vapor at the regions of very low temperature. In global scale, over land, their frequency of occurrence ranges between 28 and 42%, depending on the geographic location and season.

Cirrus clouds are classified with respect to optical thickness into four major classes; thick cloud (τ > 3), opaque cirrostratus (0.3 < τ < 3), transparent or thin cirrus (0.03 < τ < 0.3), and subvisible cirrus (τ < 0.03). Another classification of cirrus comes from their origin; in-situ and liquid origins.

This cloud type plays a key role in the Earth’s radiation budget. In general, cirrus has a net warming effect (21 W/m2), due to the warming LR and cooling caused by SR reflection. However, difficulties to investigate optically very thin cirrus clouds with satellite observations, don’t allow to have the whole picture of the cirrus radiative forcing. Local investigations, engaging  ground-based lidar measurements enable the detection of cirrus clouds of optical depths down to 10-3 and hence a better quantification of the effect of the thin clouds.

In this study, we have investigated the cirrus cloud geometrical properties, during the period 2020 – 2023, based on the nocturnal measurements of the high-resolution Rayleigh/Mie lidar at the  Observatory of Haute Provence (OHP) in France (43.9°N, 5.7°E). The analysed parameters are the top/base/mid- cloud heights, mid-cloud altitude and geometrical thickness .

Coincident meteorological parameters Data, such as  mid-cloud temperature and relative humidity are provided by ERA-5 (climate reanalysis produced by ECMWF).

Clouds are then considered as cirrus based on the following  criterias: In-cloud temperature must be as lower than −25 C,  the Scattering Ratio SR, must be above its average plus three times its standard deviation in the 17–19 km altitude range.

Multivariate analysis combining the principal component analysis and cluster methods are used to classify cirrus cloud with respect of their geometrical properties. Overall results of these analysis indicate three major cirrus cloud classes; mid-troposphere thin cirrus, thick upper-troposphere cirrus and thin-tropopause cirrus. These cirrus classes have different geometrical thickness and mid-cloud altitude. These classes differ also in terms of meteorological parameters, such as relative humidity and In-cloud temperature.

This study is done in the framework of the project CONTRAILS funded by MEFR/BPI France under the contract number DOS0182436/00. 

How to cite: Mandija, F., Alraddawi, D., Keckhut, P., and Khaykin, S.: Midlatitude cirrus cloud investigations from ground-based lidar and ERA-5 re-analysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11218, https://doi.org/10.5194/egusphere-egu24-11218, 2024.

EGU24-11933 | Orals | AS1.11

IceCloudNet: 3D reconstruction of cloud ice from Meteosat SEVIRI input 

Kai Jeggle, Mikolaj Czerkawski, Federico Serva, Bertrand Le Saux, David Neubauer, and Ulrike Lohmann

Remote sensing observations of cloud ice in cirrus and mixed-phase clouds have been playing a crucial role in advancing our understanding of cloud processes and validating climate models. On the one hand, many studies have used polar-orbiting active satellite instruments like CALIPSO’s lidar and CloudSat’s radar to analyze microphysical properties of ice clouds. These instruments are able to provide a vertical profile of cloud structures and thus allow a detailed view on cloud microphysical properties. But, due to their long revisiting times it is impossible to study the evolution of individual clouds. On the other hand, passive geostationary satellite instruments such as SEVIRI onboard the Meteosat satellites retrieve every 15 minutes a top-down view of Earth’s surface by measuring intensities of the reflected solar radiation and terrestrial infrared radiation but only in 2D.

IceCloudNet is a novel machine learning model that fuses the benefits of passive geostationary and polar-orbiting active satellite instruments to create a new vertically resolved (3D) data set of cloud ice in cirrus and mixed-phase clouds with high spatio-temporal coverage and resolution. To this end, we train IceCloudNet to predict the vertical structure of cloud ice from SEVIRI input data and co-located vertically resolved cloud ice retrievals from DARDAR as target data. Despite being only supervised with sparsely available DARDAR reference data, IceCloudNet shows good performance in predicting complex cloud structures including multi-layer clouds, when tested on independent validation data. The new data set created by IceCloudNet will enable the scientific community to conduct novel research on ice cloud formation and improve the understanding of microphysical processes by tracking and studying cloud properties through time and space.

How to cite: Jeggle, K., Czerkawski, M., Serva, F., Le Saux, B., Neubauer, D., and Lohmann, U.: IceCloudNet: 3D reconstruction of cloud ice from Meteosat SEVIRI input, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11933, https://doi.org/10.5194/egusphere-egu24-11933, 2024.

EGU24-12059 | ECS | Orals | AS1.11

Exploring ice cloud formation mechanisms through satellite observations and integrated Lagrangian transport with microphysical models 

Athulya Saiprakash, Martina Krämer, Patrick Konjari, Christian Rolf, Jérôme Riedi, and Odran Sourdeval

Understanding the formation mechanisms of ice clouds has been hindered by the complexity of their composition and the diversity of their growth processes. Previously, observational constraints have been limited, leading to substantial gaps in our comprehension and representation of ice clouds. Satellite measurements face a significant challenge due to the lack of essential environmental context information, that is necessary to identify and understand the cloud's formation mechanism and evolution. Indeed, these representations only capture a snapshot of the state of a cloud and its microphysical properties at a given time. This study addresses this limitation by providing additional metrics on ice cloud history and origin along with operational satellite products.

Here, we present a novel framework that combines satellite observations with Lagrangian transport and ice microphysical models, to obtain information on the history and origin of air parcels that contributed to their formation. The air mass transport model CLaMS (Chemical LAgrangian Model of the Stratosphere) was employed to track the trajectory of air parcels along the DARDAR-Nice track. CLaMS-Ice model is jointly used to simulate cirrus clouds along trajectories derived by CLaMS. This approach provides information on the cloud regime as well as the ice formation (in-situ vs liquid origin) pathway. Our findings, derived from case studies involving multiple cloud types, present a realistic representation of these complex processes. We explore the sensitivity of our methodology to initial conditions and thresholds. Additionally, a statistical analysis examines how satellite cloud microphysics are sensitive to CLaMS-Ice metrics. This comprehensive approach advances our understanding of ice cloud processes and helps to refine satellite-based representations of these atmospheric phenomena.

 

How to cite: Saiprakash, A., Krämer, M., Konjari, P., Rolf, C., Riedi, J., and Sourdeval, O.: Exploring ice cloud formation mechanisms through satellite observations and integrated Lagrangian transport with microphysical models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12059, https://doi.org/10.5194/egusphere-egu24-12059, 2024.

EGU24-12253 | ECS | Posters on site | AS1.11

Scattering properties generated from real shaped ice crystals and snowflakes for ICON’s Radar Forward Operator EMVORADO 

Soumi Dutta, Davide Ori, Jana Mendrok, and Ulrich Blahak

Radar Forward Operators (RFO) act as an important link between the physical properties of cloud and precipitation and the observed radar quantities. A major source of uncertainty in radar forward operators is identified in the scattering properties of frozen and mixed-phase hydrometeors. Appropriate modeling of the internal structures of complex-shaped hydrometeors plays a pivotal role in the simulation of their polarimetric scattering properties. When RFOs are applied to weather model output, it is also desirable to ensure the consistency between the properties of hydrometeors assumed in the weather model and those implemented in the scattering simulations. Failing to do so, would impede a correct interpretation of model-observation comparison studies. The present study aims to model the microphysical and scattering properties of realistic ice crystals and snowflakes using the snow particle aggregation and DDA scattering models. The aggregation model includes realistic monomer generators for various ice crystal shapes. The simulated scattering properties are implemented into the EMVORADO RFO of the ICON model. Simulated properties are primarily kept consistent with the ICON microphysical assumptions. The shapes of snowflakes and ice crystals (dendrites and plates) are generated from the aggregation model, and used as input to the DDA scattering model to compute multi-frequency polarimetric radar scattering properties. The derived scattering properties are expected to explain better the observed polarimetric radar signatures of ice crystals and snow aggregates. Nonetheless, when simulating the snowflake shapes, one must make some decisions regarding its monomer composition. This study also explores the use of the innovative Lagrangian-particle cloud model McSnow in combination with the snowflake aggregation simulator. McSnow is able to simulate the snowflake evolution based on the physical and thermodynamic profiles of clouds and thus informs the aggregation model about the snowflake composition in terms of monomer shapes, size, and number. The synergy of these models is expected to elucidate the link between ice cloud processes and the polarimetric properties of cold clouds.

How to cite: Dutta, S., Ori, D., Mendrok, J., and Blahak, U.: Scattering properties generated from real shaped ice crystals and snowflakes for ICON’s Radar Forward Operator EMVORADO, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12253, https://doi.org/10.5194/egusphere-egu24-12253, 2024.

EGU24-13078 | ECS | Posters on site | AS1.11

Temporal and Spatial Patterns of Ice Supersaturation: A 3D climatology over the North Atlantic Region 

Nils Brast, Yun Li, Susanne Rohs, Patrick Konjari, Christian Rolf, Martina Krämer, Andreas Petzold, Peter Spichtinger, and Philipp Reutter

As the most important greenhouse gas in the Earth's atmosphere, the presence of water vapor in the upper troposphere and lower stratosphere (UTLS) is essential for influencing global radiation patterns and surface climate conditions. Even minor changes in water vapor levels within the mostly dry lower stratosphere (LS) can impact the vertical water vapor gradient, making it a crucial factor in the decadal variability of surface temperature.
In condensed form, water holds significant importance for planetary radiation. Clouds play a dual role by reflecting incoming solar radiation into space and absorbing/emitting longwave radiation from the surface. Estimating the impact of cirrus clouds on the radiation budget is particularly challenging as it depends on a variety of factors, such as altitude, humidity and the microphysical properties of the cloud.
During the lifetime of a cirrus cloud, the radiative impact can even change from a warming to a cooling effect and vice versa. For the formation of cirrus clouds, ice supersaturated regions (ISSRs) play an important role. However, the required amount of supersaturation is dependent on the nucleation mechanism, with at least ∼ 45% supersaturation for homogeneous freezing and as low as ∼ 20% for heterogeneous freezing.
We present a statistical intercomparison of the In-service Aircraft for a Global Observing System (IAGOS) dataset with ERA5, the latest reanalysis product of the European Centre for Medium-Range Weather Forecasts (ECMWF). Furthermore, a machine learning based algorithm is developed to improve the accordance of relative humidity with respect to ice (RHi) of reanalysis data with in-situ measurements, enabling large scale analyses of water vapor in the UTLS region. 
With this tool, we build three-dimensional climatologies of RHi and ISSRs over the North Atlantic region and show their seasonal and regional variability. This will help foster a general understanding of the occurence of cirrus clouds and their impact on weather and climate.

How to cite: Brast, N., Li, Y., Rohs, S., Konjari, P., Rolf, C., Krämer, M., Petzold, A., Spichtinger, P., and Reutter, P.: Temporal and Spatial Patterns of Ice Supersaturation: A 3D climatology over the North Atlantic Region, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13078, https://doi.org/10.5194/egusphere-egu24-13078, 2024.

Large cirrus outflows detrained from deep convection play a vital role in modulating the radiative balance of the Earth’s atmosphere. The total cloud radiative effect (CRE) in the tropics is close to zero due to a cancellation between a large shortwave (SW) cooling from optically thick clouds and a longwave (LW) warming from high-altitude thin cirrus that spread over much of the tropics. Any small percentage changes to the LW or SW components of these large detrained cirrus in a future climate could, therefore, have significant impacts on the overall CRE in the tropics.

A crucial question is how the lifetime of these detrained cirrus impacts the total cloud radiative effects in the tropics. Characterising the detrained cirrus outflows, how they evolve over time, and how they might change in a future climate is vital in order to understand their role in the climate system and to constrain past and future climate change.

Building on the ‘Time Since Convection’ product used in Horner & Gryspeerdt (2023), this work investigates how the initial conditions of deep convection influence the radiative evolution and lifetime of the detrained cirrus. If we extend the lifetime of detrained cirrus, how does this change their total radiative effect and the radiative balance in the tropics? To answer this question, data from the DARDAR, ISCCP, and CERES products are used to build a composite picture of the radiative and microphysical properties of the clouds, which are investigated under varying initial conditions.

It is found that the initial conditions of the convection, in particular whether the convection occurs over land or ocean, play an important role in determining the lifetime and total CRE of the detrained cirrus clouds, due to the strong diurnal contrasts in convection over ocean and land. Furthermore, it is found that artificially extending the lifetime of the detrained cirrus increases the total CRE of high clouds in the tropics in all cases.

How to cite: Horner, G. and Gryspeerdt, E.: How does the lifetime of cirrus detrained from deep convection impact the cloud radiative effect of the tropics?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13338, https://doi.org/10.5194/egusphere-egu24-13338, 2024.

EGU24-14948 | Orals | AS1.11

Modelling secondary ice production in Arctic mixed-phase clouds 

Tomi Raatikainen, Silvia Calderon, Emma Järvinen, and Sami Romakkaniemi

Ice number concentration is a critical parameter for Arctic mixed-phase clouds. Several observations have shown that such relatively warm clouds can have orders of magnitude higher ice concentrations than expected based on typical Ice-Nucleating Particle (INP) concentrations. The most common explanation is that Secondary Ice Production (SIP) such as rime splintering (Hallett-Mossop ice multiplication) process causes the increase in ice concentration. Here we use observations from two field campaigns. In both campaigns the observations indicated that one or more SIP processes were actively producing ice. Due to the high temperatures around 265 K, the focus is on Hallett-Mossop process. Observed meteorological conditions and aerosol size distributions were used to initialize high-resolution large-eddy model UCLALES-SALSA simulations. Primary ice formation was modelled based on fixed INP concentrations so that the observed ice concentration was at least ten times larger than the INP concentration. Hallet-Mossop ice multiplication factors due to rime-splintering did not reproduce observed rates of secondary ice production. An increment of about one order of magnitude was needed to find agreement between modeled and observed ice number concentrations. This highlights the urgent need of laboratory and model studies that unveil the variable dependencies controlling SIP mechanisms. Secondary ice production can be increased by adjusting the simulated cloud temperature towards the optimal value and by increasing cloud water content. Extending simulation time up to 10 hours or more will also help. Although high ice concentrations can be obtained simply by increasing the INP concentrations, details such as vertical ice distribution and spatial variability will be different than in the case where SIP is used. Although this difference has a small impact on cloud dynamics during these 10-hour simulations, long-term impacts are likely.

How to cite: Raatikainen, T., Calderon, S., Järvinen, E., and Romakkaniemi, S.: Modelling secondary ice production in Arctic mixed-phase clouds, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14948, https://doi.org/10.5194/egusphere-egu24-14948, 2024.

Atmospheric aerosols can act as ice-nucleating particles (INPs) thereby influencing the formation and the microphysical properties of cirrus clouds. However, the knowledge on the atmospheric distribution of INPs is still limited and consequently the understanding of their climate impacts is highly uncertain. We perform model simulations with a global aerosol-climate model coupled to a two-moment cloud microphysical scheme and a parametrization for aerosol-induced ice formation in cirrus clouds and present a global climatology of INPs in the cirrus regime. In addition to the broadly considered mineral dust and soot INPs, this climatology also comprises crystalline ammonium sulfate and glassy organic particles. The simulated INP number concentrations range from about 1 to 100 L−1 and agree well with in-situ observations and other global model studies. Our model results show large ammonium sulfate INP concentrations, while the concentrations of glassy organic INPs are mostly low in the cirrus regime. By coupling the different INP-types to the microphysical cirrus cloud scheme, we analyze their ice nucleation potential under cirrus conditions, considering possible competition mechanisms between different INPs. The resulting radiative forcing of the total INP-cirrus effect, considering the difference between a simulation with all different INP-species and a simulation with purely homogeneous freezing, is simulated as −28 and −55 mW m−2, assuming a smaller and a larger ice-nucleating potential of INPs, respectively. While the simulated impact of glassy organic INPs is mostly small and not significant, ammonium sulfate INPs introduce a considerable radiative forcing, which is nearly as large as the combined effect of mineral dust and soot INPs. Assuming a larger ice-nucleating potential of INPs, the INP-cirrus effect due to anthropogenic INPs, considering the difference between present-day (2014) and pre-industrial (1750) conditions, is simulated as −29 mW m−2.

How to cite: Beer, C., Hendricks, J., and Righi, M.: The global distribution of ice-nucleating particles and their impacts on cirrus clouds and radiation derived from global model simulations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15251, https://doi.org/10.5194/egusphere-egu24-15251, 2024.

EGU24-15772 | Posters on site | AS1.11

Occurrence of multi-layer clouds and ice-crystal seeding in the Arctic observed by Radar and radiosondes 

Peggy Achtert, Torsten Seelig, Matthias Tesche, Gabriella Wallentin, and Corinna Hoose

While prior research on Arctic clouds has predominantly focused on single-layer clouds, the presence of multi-layer clouds in the Arctic holds significance. In such complex atmospheric systems, upper-level clouds can exert influence on the phase of lower clouds. A notable scenario occurs when ice crystals descend from higher altitudes into supercooled liquid water clouds, triggering the formation of mixed-phase clouds.

Our project is dedicated to investigating the occurrence of multi-layer clouds and their seeding, employing a combination of radiosonde and cloud radar observations. We will share findings from various locations, including research stations in Ny Alesund and the ARM North Slope of Alaska site, as well as insights from research cruises in the Arctic. Data from several research cruises were utilized in this study, namely MOSAiC (2019/20), Arctic Ocean 2018, and the ACSE 2014 campaign.

In addition, for the MOSAiC campaign, we employ back trajectories from various cloud levels and clear sky regions above the clouds to gain deeper insights into the occurrence and formation of multi-layer clouds. Our focus extends to different seasons, particularly emphasizing the Arctic melt and freeze-up periods.

How to cite: Achtert, P., Seelig, T., Tesche, M., Wallentin, G., and Hoose, C.: Occurrence of multi-layer clouds and ice-crystal seeding in the Arctic observed by Radar and radiosondes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15772, https://doi.org/10.5194/egusphere-egu24-15772, 2024.

EGU24-16916 | Orals | AS1.11

Perspectives on the Desert dust Contribution to Ice Nucleation in Mixed-phase Clouds and Associated Radiative Forcing 

Carlos Pérez García-Pando, Marios Chatziparaschos, Montserrat Costa-Surós, María Gonçalves Ageitos, Paraskevi Georgakaki, Athanasios Nenes, Maria Kanakidou, Twan van Noije, Philippe Le Sager, Zamin A. Kanji, Philip Brodrick, and Kathleen Grant

Wind-driven erosion of arid and semi-arid surfaces produces desert dust, the primary source of ice-nucleating particles (INP) in the atmosphere. These particles play a crucial role in the phase partitioning of mixed-phase clouds (MPCs) by influencing heterogeneous freezing processes. As global warming progresses, the shift from ice to liquid water in MPCs is anticipated to increase cloud reflectivity, potentially cooling the planet. However, the uncertainty surrounding this negative cloud-phase feedback is substantial, mainly due to uncertainties in the magnitude, spatiotemporal distribution, and trends of INP.

In dust-enriched environments, MPC glaciation is intricately linked to dust abundance and INP efficiency. Increased dust concentrations may enhance ice crystal formation, reducing overall cloud albedo and inducing a positive radiative effect, thereby diminishing the negative cloud-phase feedback. Currently, significant knowledge gaps impede the accurate representation of INP abundance, trends, and physical/chemical properties, hindering our understanding of its impact on ice formation in MPCs and climate.

This review assesses the current state-of-the-art in representing and quantifying the contribution of desert dust to ice nucleation in MPCs and its associated radiative forcing. Additionally, we offer a perspective on how new observational constraints, such as historical dust trends, satellite retrievals of quartz and feldspar surface abundances, recent measurements of mineral size distributions and mixing state at emission, and improved modeling with tailored ageing schemes, could help mitigate the existing uncertainties in estimating dust forcing via interactions with mixed-phase clouds.

How to cite: Pérez García-Pando, C., Chatziparaschos, M., Costa-Surós, M., Gonçalves Ageitos, M., Georgakaki, P., Nenes, A., Kanakidou, M., van Noije, T., Le Sager, P., Kanji, Z. A., Brodrick, P., and Grant, K.: Perspectives on the Desert dust Contribution to Ice Nucleation in Mixed-phase Clouds and Associated Radiative Forcing, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16916, https://doi.org/10.5194/egusphere-egu24-16916, 2024.

EGU24-16983 | ECS | Posters on site | AS1.11

Supercooled liquid water representation with the LIMA 2-moment microphysical scheme during the ICICLE field campaign 

Mareva July Wormit, Benoît Vié, and Christine Lac

Supercooled cloud water is the source of a meteorological phenomenon with significant societal challenges: icing. Icing occurs when supercooled water droplets freeze upon contact with a solid surface and is even more intense with larger drops, resulting in stronger accretion. Anticipating the icing risk is crucial to ensure aviation safety, as ice on the fuselage can lead to a loss of lift. Icing as well occurs on wind turbines and train catenaries, making it a concern for both energy and transport sectors.

Supercooled water is often underestimated in numerical models. Our objectives are first to assess the two-moment microphysical scheme LIMA (Vié et al., 2016), second to identify the physical processes which are responsible for the lesser supercooled water before improving them.

To this end, numerical simulations of the research model Meso-NH (Lac et al., 2018) are compared to the observations of the ICICLE measurements campaign (https://www.eol.ucar.edu/field_projects/icicle). This airborne campaign was launched in February 2019 from Rockford (USA) by the USA’s Federal Aviation Administration. During 29 flights, microphysical parameters as the mixing ratio and the size of liquid and icy hydrometeors have been measured. These observations form an exceptional data set for studying the microphysical behaviour of models.

19 days, including all the 23 research flights of the campaign, were simulated. An extensive evaluation of the simulations was carried out, both on a flight-by-flight basis using Meso-NH’s flight simulator, and statistically combining observations from all flights. During the campaign, several cases of classical freezing rain, and lake effect situations, were sampled, allowing for a robust evaluation of model performance in these situations.

For lake effect cases, supercooled liquid water is forecast down to −30 °C, and mixed phase clouds are present between 0 °C and −10 °C, but cloud are almost completely icy around −20 °C. In freezing rain events, the precipitation tends to freeze again below the warm part of the cloud. To identify the sources of supercooled liquid water underestimation, a detailed analysis of microphysical processes budgets is performed. The impact of aerosols on forecasts is also investigated, using in-situ aerosol observations and CAMS reanalyses.

How to cite: July Wormit, M., Vié, B., and Lac, C.: Supercooled liquid water representation with the LIMA 2-moment microphysical scheme during the ICICLE field campaign, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16983, https://doi.org/10.5194/egusphere-egu24-16983, 2024.

EGU24-19522 | Posters on site | AS1.11

Investigating the Impact of Aerosols on Liquid-Origin Cirrus from Global Observations and Reanalysis Data 

Odran Sourdeval, Athulya Saiprakash, Quentin Coopman, Silvia Bucci, and Tuule Müürsepp

Complementarily to their formation mechanism, the origin of cirrus (liquid or in-situ) can substantially affect their microphysical and radiative properties. Liquid-origin cirrus, which stem from the freezing of water droplets at cirrus temperatures, are typically characterised by high ice crystal concentrations and associated with strong cooling effect. However, the global occurence and distribution of this cirrus type as well as the environmental conditions in which they originate. The role of aerosols on liquid-origin cirrus, through their influence on liquid clouds, is also not well understood but can be critical for understanding radiative forcings associated with aerosol-cloud interactions and in implications of potential geo-engineering strategies.

This study investigates cirrus by coupling satellite and reanalysis dataset. Observations from lidar-radar satellite instruments (DARDAR-Nice) provide detailed retrievals of cirrus microphysical properties, such as ice water content and crystal number concentration. To trace the origins of cirrus clouds, we employ Lagrangian air mass trajectories based on ERA5 reanalyses, using FLEXPART. The presence and role of aerosols during the formation phase of these clouds, either in mixed-phase or warm regions, are assessed by integrating these trajectories with aerosol reanalysis products, specifically from CAMS. 

This joint cloud-aerosol dataset from satellite and reanalysis tools is created for one year of satellite observations. The global occurence of liquid-origin cirrus is analysed. The role of aerosols on the formation of liquid-origin clouds is finally investigated, in particular to understand the relevance of low-level aerosols on cirrus properties. Associated radiative effects will also be explored.

How to cite: Sourdeval, O., Saiprakash, A., Coopman, Q., Bucci, S., and Müürsepp, T.: Investigating the Impact of Aerosols on Liquid-Origin Cirrus from Global Observations and Reanalysis Data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19522, https://doi.org/10.5194/egusphere-egu24-19522, 2024.

EGU24-20442 | ECS | Posters on site | AS1.11

Secondary ice production over the Southern Atlantic Ocean: linking satellite data with aircraft observations 

Yasmin Aboel Fetouh, Jan Cermak, Corinna Hoose, and Emma Järvinen

In the past, numerous airborne in-situ measurements of mixed-phase clouds have exhibited a clear discrepancy between the observed ice particle and ice nucleating particle (INP) number concentrations of up to four orders of magnitude, with the highest differences observed in marine clouds. This suggests that primary ice nucleation is not the dominant source of cloud ice and that secondary ice production (SIP) plays a significant role in governing the ice phase in these clouds. Based on laboratory and field observations a number of SIP mechanisms have been hypothesized. However, most of these mechanisms are not well quantified and, therefore, only a few SIP mechanisms are included in weather models so far.

In our research, we aim to spatially extend the observations from aircraft campaigns by linking them to satellite data. Here we will show the work done linking the albedo and brightness temperatures from the 16 available spectral bands of Himawari-8, ranging from 0.47 – 13.3 µm, with the ice particle number concentrations observed during the SOCRATES campaign in low-level boundary layer clouds over the Southern Ocean. Finally, we employed multiple linear regression machine learning techniques and also made use of the SOCRATES campaign lidar/radar onboard.

How to cite: Aboel Fetouh, Y., Cermak, J., Hoose, C., and Järvinen, E.: Secondary ice production over the Southern Atlantic Ocean: linking satellite data with aircraft observations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20442, https://doi.org/10.5194/egusphere-egu24-20442, 2024.

EGU24-20507 | ECS | Posters on site | AS1.11

Representation of Arctic mixed-phase clouds in the ECMWF Integrated Forecasting System during MOSAiC 

Luise Schulte, Linus Magnusson, Richard Forbes, Jonathan Day, Vera Schemann, and Susanne Crewell

Mixed-phase clouds are common in the Arctic atmospheric boundary layer and their representation is challenging for models. Recent studies suggest that the ECMWF Integrated Forecast System (IFS) shows too many cloudy periods in the Arctic in summer and generally misses the periods with clear skies, while in winter the cloudy state is underrepresented.
We use ground-based remote sensing data from the MOSAiC campaign to assess systematic errors in modelled liquid cloud water over the whole MOSAiC period and combine this with more detailed analyses of selected cases.
In addition, we perform sensitivity tests to identify ways to improve the parametrization for Arctic mixed-phase clouds in the IFS.
We run cases in the Single Column Model (SCM) version of the IFS and investigate the representativity of model sensitivities in the SCM for the 3D model.

How to cite: Schulte, L., Magnusson, L., Forbes, R., Day, J., Schemann, V., and Crewell, S.: Representation of Arctic mixed-phase clouds in the ECMWF Integrated Forecasting System during MOSAiC, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20507, https://doi.org/10.5194/egusphere-egu24-20507, 2024.

Mass loss of snow packs due to recrystallization processes and subsequent vapor fluxes are inherently difficult to measure experimentally. Present numerical advances enable new simulation tools to explore the otherwise invisible mass fluxes due to diffusive and convective water vapor transport. In this study we calculate the effective vapor fluxes as a function of the local mass transfer coefficient, snow depth, and a range of microstructure parameters given by porosity and specific surface area. A set of flow, heat transport and vapor transport equations re developed. Heat transport is characterized by the Rayleigh number while vapor transport depends on the Péclet and Damkhöler numbers. The latter measures the relative importance of vapor transfer to advective fluxes. For low Rayleigh numbers, the system behaves in a purely diffusive manner. however, convective transport mechanisms dominate for high Rayleigh values. Convection is found to enhance vapor transport. This is in agreement with previously unexplained mass losses in field observations. The effect of vapor mass transfer between the solid and gas phase is also analyzed. The results can be used for macroscale snow pack models to predict large scale mass loss due to sublimation for snow covered areas such as Antarctica, Greenland and seasonally covered Tundra.

How to cite: Hidalgo, J. J. and Krol, Q.: Effective vapor transport in snow: The role of convection and the local mass transfer coefficient, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3827, https://doi.org/10.5194/egusphere-egu24-3827, 2024.

EGU24-6861 | ECS | PICO | CR6.2

Aeolian snow transport induces airborne snow metamorphism with implications for snowpack physical properties 

Sonja Wahl, Benjamin Walter, Franziska Aemisegger, Luca Bianchi, and Michael Lehning

Aeolian transport of snow is a cryospheric process prevalent in all snow-covered areas. It influences the energy and mass balance of these cold regions. Apart from the direct effects during the process, aeolian transport alters the snow’s microstructure, leaving behind a wind-blown snow layer with different snowpack characteristics than before the wind event. For high-resolution climate modeling in snow-covered regions, it is thus important to incorporate the immediate and lasting effects of wind-induced aeolian snow transport for an accurate representation of the energy and mass balances of a snowpack. Apart from mechanical mechanisms such as fragmentation and aggregation of snow crystals, the metamorphic mechanism (sublimation and deposition of water molecules on the suspended snow particles) can alter the microstructure of snow during aeolian transport. It is difficult to predict the relative importance of the two mechanisms for the evolution of the microstructure of wind-blown snow, not least because the process is happening on the micro-scale but is unfolding on large spatial scales on the respective particle trajectories. Thus, it is difficult to observe.
However, metamorphic processes leave a fingerprint on the snow’s composition of stable water isotopes whereas the mechanical mechanisms do not. Hence, monitoring the evolution of the stable water isotope signal of the snow can act as a macro-scale tracer for the metamorphic micro-scale processes. The stable water isotope signal can thus help to differentiate the processes at play during aeolian snow transport.
Here we show through observations of cold laboratory ring-wind tunnel experiments that aeolian transport of snow involves airborne snow metamorphism. We monitored the evolution of the microstructure and the isotopic composition of airborne snow through repeated sampling of snow from the air stream. In a total of 19 experiments we varied the temperature in a range of -20°C to -3°C and the transport times varied between 50 - 180 minutes. We find a rapid exponential decay in specific surface area (SSA) with transport time which reduces the SSA value to 35-70% of its starting value by the end of the experiments. Further, we observe a shift in the particle size distribution towards larger snow particles, both for the most abundant and maximum particle sizes with aeolian transport time. Simultaneously, the water isotope signature shows mainly an enrichment in δ18O and a decrease in d-excess which is a strong indicator for isotopic fractionation and thus evidence for the presence of metamorphic processes. Combining the results, we attribute the change in snow microstructure to airborne snow metamorphism. The unique combination of information on the isotopic composition and microstructure of airborne snow under well-constrained laboratory conditions can be used to develop parameterizations for the incorporation of airborne snow metamorphism in snow-process models.

How to cite: Wahl, S., Walter, B., Aemisegger, F., Bianchi, L., and Lehning, M.: Aeolian snow transport induces airborne snow metamorphism with implications for snowpack physical properties, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6861, https://doi.org/10.5194/egusphere-egu24-6861, 2024.

EGU24-8976 | ECS | PICO | CR6.2

Spatiotemporal variability of turbulent fluxes over snow in mountain regions  

Rainette Engbers, Sergi González-Herrero, Nander Wever, Franziska Gerber, and Michael Lehning

Turbulent exchange of heat and moisture plays an important role in snow cover dynamics in mountain regions and governs the boundary layer dynamics. While these processes are subject to great spatiotemporal variability, particularly in complex terrain, virtually all measurements of heat, moisture and momentum fluxes are point observations. To quantify the spatial variability, and assess the representativeness of the observations, numerical modelling of the atmosphere and surface is a useful tool. Still, there is substantial uncertainty in the accuracy of how surface models capture this spatial variability, particularly in complex terrain with large spatial variability on small scales. These uncertainties can be partially attributed to (1) the use of Monin-Obukhov similarity theory (MOST) which has limitations in complex terrain due to the role of advection and (2) the errors in representing near-surface atmospheric gradients in the simulations. In this study, we analyse sources of errors in representing energy exchange over snow in mountain regions and look specifically at the spatiotemporal variability during different meteorological events in the region of Davos, Switzerland. To verify common modelling approaches with observations, we use model predictions of turbulent fluxes from CRYOWRF, the atmospheric model WRF coupled to the surface model SNOWPACK. The fluxes at different resolutions are compared to turbulent fluxes measured using the Eddy Covariance method (EC) and calculated with MOST. This model validation is done for different meteorological events representative of the local climate. Preliminary results indicate that the fluxes are highly spatially variable, being an order of magnitude higher on the leeside than on the windward side of a mountain ridge. This indicates that local heat fluxes are not representative of the whole mountain area, which has implications for the calculation of snow melt, sublimation and accumulation across mountainous terrain. The resolution of the model also plays a large role in representing the fluxes as the modelled fluxes differ greatly depending on the resolution. Our results quantify to what extent snow-atmosphere interactions and their spatial variability are correctly represented in state-of-the-art numerical weather and snow models. 

 

How to cite: Engbers, R., González-Herrero, S., Wever, N., Gerber, F., and Lehning, M.: Spatiotemporal variability of turbulent fluxes over snow in mountain regions , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8976, https://doi.org/10.5194/egusphere-egu24-8976, 2024.

EGU24-12325 | PICO | CR6.2

A comparison of snow depth scaling patterns from TLS, UAV and Pleiades observations  

Jesús Revuelto, Pablo Mendoza, Cesar Deschamps-Berger, Esteban Alonso-González, Francisco Rojas-Heredia, and Juan Ignacio López-Moreno

Understanding the evolution of snowpack in heterogeneous mountain areas is a highly demanding task and requires the application of suitable observation techniques to retrieve snow properties at distinct spatial scales. In turn, once the reliability of these techniques is established, the comprehension of snowpack scaling properties helps to determine which processes are more relevant on the control of snow distribution and its temporal evolution. Previous studies have reported detailed observational datasets and insights on the main drivers of snowpack distribution through variogram analysis up to 500-800 m, identifying scale break lengths and their anisotropies. Here, we examine scale breaks derived from variogram analysis applied to snow depth observations at the Izas Experimental Catchment (located in Central Spanish Pyrenees) and the surrounding area for the period 2019-2023. To this end, we use data retrieved with three observation techniques: Terrestrial Laser Scanning (TLS-LiDAR, 12 acquisitions), Unmanned Aerial Vehicles (UAV-SfM, 20 acquisitions), and satellite stereo images (4 Pléiades acquisitions), covering different domains around the experimental site. First, we analyze the consistency among the observational techniques, and then we explore possible drivers explaining detected scale breaks through variogram analysis up to 4000 m. Overall, similar results were obtained with the three observational techniques, with a very high temporal consistency for the first detected scale break length and little variations with direction. We also found good agreement between the search distance used to compute the topographic position index (TPI), the first scale break length, and the mean distance between peak snow accumulations, which vary between 15 and 25 m, not only for the entire study domain, but also in manually delineated Hydrological Response Units.

How to cite: Revuelto, J., Mendoza, P., Deschamps-Berger, C., Alonso-González, E., Rojas-Heredia, F., and López-Moreno, J. I.: A comparison of snow depth scaling patterns from TLS, UAV and Pleiades observations , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12325, https://doi.org/10.5194/egusphere-egu24-12325, 2024.

EGU24-12854 | ECS | PICO | CR6.2

Quantifying the Impact of Dynamic Lapse Regimes on Spatially-Distributed Snow Simulations 

Kristen Whitney, Sujay Kumar, John Bolten, Justin Pflug, Fadji Maina, Christopher Hain, David Mocko, and Melissa Wrzesien

Accurate characterization of surface meteorological distributions over coastal areas and complex terrain, especially the relationship between temperature and altitude, is essential for the accurate simulation of snowpack dynamics. This becomes increasingly difficult at spatial resolutions smaller than common gridded meteorological forcing datasets due to the sparsity of long-term temperature measurements and the influence of local factors like cool air pooling and inversions. Near-surface air temperatures (Ta) are often assumed to decrease with elevation at a constant rate of 6.5oC km-1, which could lead to large model errors in snow evolution and other processes key to snow hydrology, water resource management, and other applications. This study evaluates the impact of local dynamical adjustments to downscaled Ta on snow simulations over two coastal mountainous terrains using the Noah-MultiParameterization (NoahMP) land surface model. Forcings are derived from remote sensing and reanalysis precipitation products and the (Modern-Era Retrospective Analysis for Research and Applications, version 2) MERRA-2 atmospheric products (including Ta) at the downscaled 1-km resolution. Hourly lapse rates at each grid cell are calculated by applying linear regression to Ta and elevation from surrounding grid cells (within one grid lengths in the x or y direction) at the Ta native MERRA-2 resolution and applied to the downscaled 1-km Ta product. We will present the impact on simulated snow water equivalent, snow cover, and snow depth across simulations forced with the downscaled Ta (1) without lapse rate correction, (2) corrected with a constant lapse rate (6.5oC km-1), and (3) corrected with the dynamic hourly lapse rate. Results will be compared against remote sensing-based products.

How to cite: Whitney, K., Kumar, S., Bolten, J., Pflug, J., Maina, F., Hain, C., Mocko, D., and Wrzesien, M.: Quantifying the Impact of Dynamic Lapse Regimes on Spatially-Distributed Snow Simulations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12854, https://doi.org/10.5194/egusphere-egu24-12854, 2024.

To maintain computational efficiency and avoid adding too many uncertainties into Land Surface Models (LSMs) with fine-scale parameterization, many efforts have been made to improve sub-grid representations of heterogeneous landscapes. HydroBlocks LSM stands out as a model that employs advanced hierarchical clustering methods, utilizing field-scale satellite-derived data to construct sub-grid tiles or clusters. The Noah-MP land surface model is applied within each tile. Unlike conventional tiling approaches, knowing the spatial location of the clusters provides the opportunity to incorporate the interactions across the distinct clusters. Presently, they interact through the subsurface flow processes. Despite the comprehensiveness of these models, both Noah-MP and HydroBlocks lack consideration for the wind-induced snow transport which plays a pivotal role in snow-related hazards. Other than that, the sublimation and redistribution of wind-blown snow in exposed environments contributes significantly to variations in snow depth. It not only exerts local influence on surface water and energy balance, but also can have expansive impact since the snowmelt is critical for the water availability of downstream basins. To address this limitation, we propose the integration of a blowing snow module into HydroBlocks. This module, inspired by the Prairie Blowing Snow Model, consists of physical-based wind transport and sublimation algorithms. Clusters will be categorized into source and sink regions considering their topography and vegetation. The redistribution of snow mass at every timestep will be calculated based on the wind condition and the adjacent borders between clusters. This research seeks to pave the way for modeling other mass transport processes between tiles which considers the complex interactions within heterogeneous landscapes.

How to cite: Cai, J. and Chaney, N.: Integrating a Blowing Snow Module for Enhanced Representation of Snow Dynamics and Surface in the HydroBlocks modeling framework, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13274, https://doi.org/10.5194/egusphere-egu24-13274, 2024.

EGU24-15202 | PICO | CR6.2

Wind tunnel experiments to characterize snow densification and SSA reduction caused by aeolian snow transport 

Benjamin Walter, Sonja Wahl, Hagen Weigel, and Henning Löwe

Snow precipitation frequently occurs under moderate to strong wind conditions, resulting in drifting and blowing snow. Processes like particle fragmentation and airborne metamorphism during snow transport result in microstructural modifications of the ultimately deposited snow. Despite the relevance (optically and mechanically) of surface snow for alpine and polar environments, this effect of wind on the snow microstructure remains poorly understood and quantified. Available descriptions of snow densification due to wind are exclusively derived from field measurements where conditions are difficult to control. Information on the effect of wind on the specific surface area (SSA) is basically nonexistent. The goal of this experimental study was to systematically quantify the influence of wind on the surface snow density and SSA for various atmospheric conditions (temperature, wind speed, precipitation intensity), and to identify the relevant processes. 

We conducted experiments in a cold laboratory using a closed-circuit ring wind tunnel with an infinite fetch to investigate wind-induced microstructure modifications under controlled atmospheric, flow and snow conditions. Artificially produced dendritic nature-identical snow was manually poured into the ring wind tunnel for simulating precipitation during the experiments. Airborne snow particles are characterized by high-speed imaging, and deposited snow is characterized by density and SSA measurements resulting in a comprehensive dataset.

            The high-speed images support a snow particle transport scheme in the saltation layer similar to natural conditions. We measured an increase of the densification rate with increasing wind speed which differs from available model parameterizations. The SSA was found to decrease under the influence of wind, while increasing wind velocities intensified the SSA decrease. For higher air temperatures (Ta > -5°C), both the densification and SSA rates significantly differ from the rather constant rates at lower temperatures. We attribute this to the effects of enhanced cohesion or sintering (density) and intensified airborne snow metamorphism (SSA) at higher air temperatures. A sensitivity experiment revealed a strong influence of airborne snow metamorphism on the SSA decrease. Our results provide a first step towards an improved understanding and modeling of the effect of aeolian snow transport on optically and mechanically relevant microstructural properties of surface snow.

How to cite: Walter, B., Wahl, S., Weigel, H., and Löwe, H.: Wind tunnel experiments to characterize snow densification and SSA reduction caused by aeolian snow transport, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15202, https://doi.org/10.5194/egusphere-egu24-15202, 2024.

The snowpack plays a fundamental role in regulating the global climate thanks to its high albedo and thermal insulation properties, and for many regions of the world it also has very local and important impacts. Indeed, the snow is an important water reservoir, storing the water in solid state during cold months, and releasing it in liquid state during warmer months. But the snow is also the necessary condition for the development of rural places which base their economy on winter sports. However, a certain risk is always associated with snow when it deposits on the ground, since the snow can slide down, creating avalanches which may cause several damages to the local flora, fauna, buildings and infrastructures. Typically, the conditions that allow the occurrence of snow avalanches span from the point scale to the slope scale, and depend on the snowpack properties. Kilometer-resolution numerical models are not able to reproduce the slope-scale variability of the snowpack properties because of the complex interaction between the atmospheric flows and the topography at finer scale. To address this limitation, we apply several algorithms to downscale 1 km horizontal resolution WRF atmospheric simulations to 500 m horizontal resolution in order to force the snow cover model Alpine3D with more representative weather data. Additionally, we train a fully convolutional neural network to downscale 10 km resolution IMERG precipitation data to 1 km horizontal resolution, further downscaled to 500 m. Furthermore, 2m temperature point observations are interpolated at 500 m resolution using geostatistical techniques. Finally, we force Alpine3D with a combination of forecasted and observed data, obtaining improved simulation results compared to using only forecasted weather data. This implies that the use of a combination of simulated and observed weather data is particularly promising for the estimation of the snowpack properties at slope-scale resolution in regions characterized by complex topography, providing more reliable information for risk mitigation, and sustainable development of snow-prone areas.

How to cite: Raparelli, E. and Tuccella, P.: Improving snowpack simulation at slope-scale resolution with machine learning and geostatistical downscaling of observed and forecasted weather data., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15809, https://doi.org/10.5194/egusphere-egu24-15809, 2024.

Mountain snowpack serves as a vital water source for both high-altitude regions and adjacent lowlands, significantly impacting local economies through its influence on tourism, communication, logistics, and recreational risks. However, mid-elevation snow cover is diminishing due to climate change (IPCC-2021), emerging as a critical concern in water management. Despite its importance, a lack of comprehensive understanding stems from a scarcity of well-distributed mountain snowpack observations and specific simulation tools. This knowledge gap is more pronounced in Mediterranean mountainous regions, where intricate processes of growth and ablation, high spatial variability, and a high inter-annual variability pose obstacles for models. To address these challenges, hyper-high resolution models (<1 km) have been developed, but they often come with significant computational expenses. As an alternative, SnowCast has been introduced, which nests ERA5 atmospheric reanalysis (ECMWF), the Intermediate Atmospheric Research model (ICAR, NCAR), and the Flexible Snow Model (FSM2, University of Edinburgh), incorporating custom parametrizations and high-resolution topographic forcing models. This approach enables highly parallelized computations, enhancing the efficiency of simulating multiple years. This capability allows the application of such resolution for climate studies while managing computational costs effectively. Validation through extensive fieldwork, automated snowpack monitoring, and satellite imagery shows that the model provides a realistic temporal and spatial representation of snow cover. In-depth analysis of model performance will be presented, along with discussions on potential new processes for implementation, exploration of additional validation techniques, and future prospects for coupling with a hydrological model.

How to cite: González Cervera, Á. and Durán, L.: SnowCast: Hyper-high resolution downscaling model. Snowpack simulation in a mountainous region in Central Spain (Peñalara Massif), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15828, https://doi.org/10.5194/egusphere-egu24-15828, 2024.

EGU24-17057 | PICO | CR6.2

Snow on permafrost: the effect of spatial snow variability on soil temperature in Trail Valley Creek, NWT, Canada 

Inge Grünberg, Daniela Hollenbach Borges, Jennika Hammar, Nick Rutter, Philip Marsh, and Julia Boike

Snow is a potent insulator, influencing the temperature of the active layer and the permafrost in the Arctic region. However, our understanding of spatial patterns of snow properties and their interplay with vegetation remains limited due to scarcity of local and regional snow data. Furthermore, the duration, depth, and physical properties of the Arctic snow cover are changing with rising air temperature and new precipitation patterns. We study the spatial snow distribution and its drivers and consequences around the Trail Valley Creek research catchment in the Northwest Territories, Canada. Our dataset includes a 143 km² snow depth raster captured on April 2, 2023, at a 1-meter spatial resolution, as well as data from 13 spatially distributed loggers measuring air/snow temperature, soil surface temperature, and soil temperature at 8 cm depth from August 27, 2022, to August 9, 2023. Detailed information on vegetation types, structure, and soil properties at all locations is included. Our analysis covers the timing of soil freeze and thaw, snow and soil temperatures, and their correlation with vegetation characteristics, particularly focusing on April snow depth. Our findings underscore the pivotal role of snow in regulating soil temperature, making it a key driver for permafrost protection or thaw. The results reveal significant variability in April snow depth across the 13 study locations, ranging from no snow to 1.7 meters, resulting in winter minimum soil temperatures between -31°C and -4°C. The study confirms that thicker snow cover contributes to warmer soil temperatures. While the soil at 8 cm freezes uniformly in mid-October across all sites, snow patterns lead to high variability in soil thawing dates, which span one month between May 10 and June 08, 2023. Understanding the spatial patterns of snow depth, thermal properties, and timing is crucial for assessing the snow effect on soil temperature. The large range of winter soil temperatures, which we observed, may lead to differences in thaw depth development in the following summer and potentially to talik formation affecting permafrost stability.

How to cite: Grünberg, I., Hollenbach Borges, D., Hammar, J., Rutter, N., Marsh, P., and Boike, J.: Snow on permafrost: the effect of spatial snow variability on soil temperature in Trail Valley Creek, NWT, Canada, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17057, https://doi.org/10.5194/egusphere-egu24-17057, 2024.

EGU24-19751 | ECS | PICO | CR6.2

A continuum mechanics perspective on the rheology of firn in the context of firn densification 

Timm Schultz, Angelika Humbert, and Ralf Müller

While the complex nonlinear rheology of ice is well known and often discussed, for example in the context of large-scale ice sheet modeling, calving, and isotropy occurring at shear margins, the rheology of firn is often considered to be rather simple. According to Truesdell’s first metaphysical principle, which states that ”all properties of a mixture must be mathematical consequences of properties of the constituents” (Truesdell, C. (1984), Rational Thermodynamics, Springer-Verlag, p. 221), the material behavior of firn should be related to that of ice, since firn is primarily a mixture of ice and air. What distinguishes firn from ice is its microstructure. The field of continuum mechanics provides methods to relate the microstructural properties of a material to its macroscopic material behavior.

Here we review a homogenization method developed for the densification of nonlinear creeping metallic powders and first applied to the simulation of firn densification by Gagliardini and Meyssonnier (1997, Annals of Glaciology, 24, pp. 242–248). The method links the rheology of ice to that of firn by describing firn as a porous medium with an ice matrix. The advantage of this approach is that it is formulated in all three spatial dimensions, allowing the inclusion of horizontal divergence due to ice flow without additional parameterization. A large database of dated firn cores allows the determination of the governing model parameters using an optimization approach. We discuss the results, advantages, and limitations of this approach, as well as validation strategies.

How to cite: Schultz, T., Humbert, A., and Müller, R.: A continuum mechanics perspective on the rheology of firn in the context of firn densification, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19751, https://doi.org/10.5194/egusphere-egu24-19751, 2024.

EGU24-20320 | PICO | CR6.2

Monitoring snow depth by Integrating in an optimal way citizen science and other techniques 

David Pulido-Velazquez, Antonio Collados_Lara, Pedro Sánchez, Leticia Baena-Ruiz, Eulogio Pardo-Iguzquiza, Carlos Lorenzo-Carnicero, Juan Carlos García-Davalillo, Luis Carcavilla, Steven Fassnatch, Javier Herrero, Jose David Hidalgo, Victor Cruz Gallegos, Juan de Dios Gomez Gomez, Mónica Leonor Meléndez, Nemesio Heredia, Ignacio Lopez-Moreno, Jesús Revuelto, Helen Flynn, Amalia Romero, África de la Hera Portillo, Jorge Jódar, and Elisabeth Diaz-Losada

The snow depth (SD) is an excellent indicator of climate, yet a poorly monitored variable in many mountain ranges. A novel integrated approach is proposed for optimal monitoring of SD dynamics in the 5 National Parks located in Alpine (NPA) zones of Spain (i.e., Sierra Nevada, Guadarrama, Picos de Europa, Ordesa y Monte Perdido, and Aigüestortes i Estany de Sant Maurici). It will leverage the existing infrastructure of snow poles installed by the Snow Monitoring National Program in Spain (ERHIN). This program obtains SD measurements by direct observation from helicopter flights (1-3 per year). This monitoring activity has been drastically reduced in some mountain ranges during the economic crisis. The objective of this current work is to avoiding potential gaps in the valuable long-term SD timeseries of the pole measurements. An innovative Citizen Science Activity (CSA) methodology is being implemented to engage volunteers to collect the maximum number of photos of the snow poles. It is designed as a sports challenge, in which ranking and awards will be given to the most active participants. It aims to enhance the project with a minimum economic cost, and has the additional objective of raising awareness and encouraging responsible visits to these NPA. It has been tested in Sierra Nevada National Park, where we have identified the necessity to combine the information obtained from this CSA with other approaches to maximize the amount of useful information collected, and in order to reduce the uncertainty in snow distribution.

A number of automatic point sensors have been installed to collect additional snow depth data at snow poles with a high number of days with snow, as identified from a historical analyses of snow cover area (SCA). These locations also have higher uncertainty SD measurements, and thus far, there have been less opportunity for the citizen science collection of photos. In order to identify the most relevant snow poles, we have used a regression model that estimates the spatial distribution of snow depth and its uncertainty from snow cover area and snow depth data. since the high cost of this complementary monitoring actions needs to be considered. a multi-sensors experiment is being performed to identify the best cost-benefit automatic sensors (ultrasound sensors, time-lapse cameras, etc). Drone field campaigns will be also performed, together with distributed information from airborne LIDAR and high resolution Pléiades satellite imagery. Such field campaigns there are costly, and thus the CSA has been also extended to the other 4 NPA. We are using a variety of media (e.g., social networks, TV, radio, and newspapers) to disseminate and communicate the CSA activity in order to maximize participation.

Acknowledgements:
This research has been partially supported by the projects: STAGES-IPCC (TED2021-130744B-C21), SIGLO-PRO (PID2021-128021OB-I00), from the Spanish Ministry of Science, Innovation and Universities, SER-PM (2908/22) from the National Park Research Program, RISKYEARTH (Recovery funds), and SIERRA-CC (PID2022-137623OA-I00) funded by MICIU/AEI/10.13039/501100011033 and by FEDER, UE.

How to cite: Pulido-Velazquez, D., Collados_Lara, A., Sánchez, P., Baena-Ruiz, L., Pardo-Iguzquiza, E., Lorenzo-Carnicero, C., García-Davalillo, J. C., Carcavilla, L., Fassnatch, S., Herrero, J., Hidalgo, J. D., Cruz Gallegos, V., Gomez Gomez, J. D. D., Meléndez, M. L., Heredia, N., Lopez-Moreno, I., Revuelto, J., Flynn, H., Romero, A., de la Hera Portillo, Á., Jódar, J., and Diaz-Losada, E.: Monitoring snow depth by Integrating in an optimal way citizen science and other techniques, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20320, https://doi.org/10.5194/egusphere-egu24-20320, 2024.

Extratropical cyclone (EC) is a main source of precipitation at midlatitudes, but its contribution to the Antarctic surface mass balance (SMB) still remains uncertain. Based on five global climate model simulations, we propose that it probably exists a tipping point of the SMB during the evolution of the Antarctic Ice Sheet (AIS), and EC greatly contributes to the tipping point. Before the tipping point, decreasing elevation of the AIS and warming sea surface temperature promote southward movement of ECs, leading to increased precipitation and inhibiting the AIS melting. However, EC becomes a negative contribution to SMB due to increased AIS surface temperature, runoff and rainfall. This study highlights that EC contributes to the tipping point of the AIS evolution.

How to cite: Xu, D. and Lin, Y.: A tipping point in the contribution of extratropical cyclones to Antarctic surface mass balance, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-234, https://doi.org/10.5194/egusphere-egu24-234, 2024.

EGU24-788 | ECS | Posters on site | AS1.13

Characterization of cirrus clouds in the arctic depending on ambient conditions 

Georgios Dekoutsidis, Silke Groß, Martin Wirth, Christian Rolf, Andreas Schäfler, and Florian Ewald

The increase of the average global temperature of the Earth’s atmosphere has been measured with various methods dating back to the 19th century. In the past few decades scientists have shown that the arctic regions are warming even faster than the global average. This phenomenon has been labeled Arctic Amplification. Cirrus clouds are a potential contributor to this phenomenon. They reflect only a small part of the incoming solar radiation and can absorb and reemit earth’s long-wave radiation, thus potentially having a warming effect. Warm Air Intrusion (WAI) events transport warm, water-vapor- and aerosol-rich airmasses from the mid-latitudes into the arctic and can also contribute to arctic amplification. On the one hand the transported airmasses are already warm and contain significant amounts of water vapor which is a strong greenhouse gas. On the other hand, the cirrus clouds that form during such an event might have different and potentially stronger effects on the radiation budget of the atmosphere. Since it has also been shown that WAI events in the arctic are becoming more frequent or long-lasting, it is important to study the effects these events have on the macrophysical and optical properties of cirrus clouds in the arctic.

The HALO-(AC)3 field campaign took place in March and April of 2022. One of the central goals of the campaign was to study WAI events in the arctic regions of the Northern Hemisphere. Among others, the German research aircraft HALO was used to perform remote sensing measurements. In this study we use data collected during this campaign by the combined water vapor differential absorption and high spectral resolution lidar system WALES and the HAMP cloud radar. We selected two research flights: RF03, performed during an active warm air intrusion event (WAI case) and RF17, performed during undisturbed arctic conditions (AC case). For these flights we calculated the relative humidity over ice (RHi) and the backwards trajectories using the Lagrangian analysis tool LAGRANTO and the CLaMS-Ice model, which combines the Chemical Lagrangian Model of the Stratosphere (CLaMS) with two-moment ice microphysics. Our aim is to provide an in-depth analysis of the two types of cirrus clouds and find potential differences between them.

The clouds of the WAI case had a greater mean geometrical and optical depth as well as a slightly higher linear depolarization ratio, as measured by WALES. The distributions of RHi for the WAI case had its maximum slightly over saturation and a small negative skewness, while the AC case had its maximum at saturation with a bigger negative skewness. The supersaturations within and at close proximity to the WAI clouds reached high values over 127% more frequently than for the AC case. Surprisingly, the backwards trajectories revealed that the AC case had a significant part being of liquid origin and formed via heterogeneous nucleation, whilst the WAI case was predominantly of in-situ origin with homogeneous nucleation being the dominant process.

How to cite: Dekoutsidis, G., Groß, S., Wirth, M., Rolf, C., Schäfler, A., and Ewald, F.: Characterization of cirrus clouds in the arctic depending on ambient conditions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-788, https://doi.org/10.5194/egusphere-egu24-788, 2024.

EGU24-914 | ECS | Posters on site | AS1.13

Intense precipitation events during polar winter over the Academic Vernadsky station: clouds, precipitation and temperature extremes 

Anastasiia Chyhareva, Svitlana Krakovska, Irina Gorodetskaya, and Liudmyla Palamarchuk

West Antarctica and the Antarctic Peninsula are considered to be climate tipping point regions where climate change processes can cause irreversible impacts. The Antarctic Peninsula region has a unique ecosystem, which can be harmfully affected by these changes. In the past decades have from Pacific mid-latitudes and specifically atmospheric rivers, accompanied by mixed-phase clouds and precipitation, can lead to surface melt on both sides of the Antarctic Peninsula.

This study focused on intense precipitation events during the winter in the Southern Hemisphere in 2022 in the Antarctic Peninsula observed during the Year of Polar Prediction targeted observing periods. Polar WRF (v. 4.5) simulation data with grid step 1km and temporal resolution 10 minutes were analysed for the region of Academic Vernadsky station, Antarctic Peninsula mountains and former glacier Larsen B bay.

Distributions of clouds and precipitation were analysed, as well as their concentrations and phases in the cross-section of the mountains. Also, temperature profiles were examined in the cross-sections, specifically for the 2km profile.

According to the simulations data, based on Thompson’s microphysical scheme found that mixed phased and liquid clouds and precipitation could occur up to 3km even in August, which is climatically the coldest month over the coastal areas and mountains. Maximum concentrations of ice crystals and liquid droplets could exceed 1g/kg. After the intense precipitation that occur on the western Antarctic Peninsula slopes, strong warming up to 6°C in a 2km layer is simulated for the eastern slopes of AP (Larsen B ice shelf embayment).

Simulation results were compared with radiosounding data and instrumental measurements at the Akademic Vernadsky station. According to the radiosounding that were held during all events, Polar WRF underestimated the temperature in the lower troposphere (up to around 950hPa), which can impact the surface precipitation phase and temperature simulations. However, as far as Polar WRF simulations for wind speed, direction, temperature, and vertical movements are correlated with radiosounding data, we can assume that the distribution of considered microphysical and thermodynamical characteristics gained from Polar WRF simulations are trustable.  

How to cite: Chyhareva, A., Krakovska, S., Gorodetskaya, I., and Palamarchuk, L.: Intense precipitation events during polar winter over the Academic Vernadsky station: clouds, precipitation and temperature extremes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-914, https://doi.org/10.5194/egusphere-egu24-914, 2024.

EGU24-1678 | ECS | Posters on site | AS1.13

Shortwave cloud warming effect observed over Greenland 

Haotian Zhang, Chuanfeng Zhao, Annan Chen, Xin Zhao, and Yue Zhou

Clouds play a pivotal role in regulating the Earth's energy budget, primarily by exerting a global net cooling effect through the competing effects of shortwave radiation shading and longwave radiation trapping. However, here we report a shortwave warming effect by clouds over Greenland, contrary to the conventional belief of a cooling effect. Utilizing satellite observations from the Greenland region during the summers from 2013 to 2022, we identify a positive shortwave cloud radiative forcing when the ratio of surface albedo to top-of-atmosphere (TOA) reflectivity surpasses 1.42, implying that cloud induced warming can occur in any place when the surface is bright enough compared with TOA. Moreover, we find that the shortwave cloud warming effect on the Earth-atmosphere system is particularly prominent for optically thin clouds. These findings are crucial for understanding the radiation budget over polar regions and improving the prediction of polar ice melting.

How to cite: Zhang, H., Zhao, C., Chen, A., Zhao, X., and Zhou, Y.: Shortwave cloud warming effect observed over Greenland, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1678, https://doi.org/10.5194/egusphere-egu24-1678, 2024.

EGU24-1691 | ECS | Posters on site | AS1.13

The vertical structure of atmospheric rivers in Antarctica in the present-day and future 

Marlen Kolbe, Richard Bintanja, Eveline C. van der Linden, and Raul R. Cordero

Recent extremes in Antarctic temperature, surface melt and sea ice loss have been robustly linked to the occurrence of atmospheric rivers (ARs). However, the precise mechanisms that generate variations in the surface impacts of ARs are poorly understood, especially in the Antarctic region. Based on Arctic evidence that the vertical and horizontal advancement of ARs over sea ice strongly depends on the sea ice-preceding surface type, the season, as well as meteorological conditions, we investigate the vertical structure and propagation of extreme ARs reaching sea ice and the Antarctic ice sheet, and further quantify the associated surface impacts. We further link the wind speed and surface vertical structure and proximity of ARs to variations in turbulent mixing and radiative fluxes, which ultimately determine the impact on the surface and subsequent AR pathway. While previous studies have mostly detected ARs based on  observations and reanalyses, we additionally assess AR characteristics based on 6 CMIP6 models under present-day and future conditions (SSP5-8.5) to robustly study their propagation and impacts when reaching Antarctic sea ice and the ice sheet. 

How to cite: Kolbe, M., Bintanja, R., van der Linden, E. C., and Cordero, R. R.: The vertical structure of atmospheric rivers in Antarctica in the present-day and future, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1691, https://doi.org/10.5194/egusphere-egu24-1691, 2024.

EGU24-3671 | ECS | Orals | AS1.13 | Highlight

Antarctic Atmospheric Rivers in Present and Future Climates 

Michelle Maclennan, Andrew Winters, Christine Shields, Léonard Barthelemy, Rudradutt Thaker, and Jonathan Wille

Atmospheric rivers (ARs) are long, narrow bands of moisture that propagate poleward from the midlatitudes and occasionally reach the Antarctic Ice Sheet. Despite occurring only ~1% of the time, Antarctic ARs contribute 10% of the annual precipitation and are major drivers for heatwaves, foehn events, and surface melting on ice shelves. While snowfall is currently the dominant impact of ARs over the grounded Antarctic Ice Sheet, the relative contribution of ARs to snowfall, rainfall, and surface melt may change in a warming climate, along with the frequency and intensity of AR events themselves. Here, we use the Community Earth System Model version 2 (CESM2) Large Ensemble to detect ARs during the current period (1980–2014) and future climate (2015–2100) under the SSP370 radiative forcing scenario. We use an AR detection threshold for the current period based on the 98th percentile of the meridional component of integrated vapor transport (vIVT). To account for projected future increases in atmospheric moisture content (Clausius-Clapeyron effect) and its impacts on vIVT, we scale our AR detection threshold for the future period by the relative change in integrated water vapor compared to the present-day climatology. We then describe how the frequency, intensity, and year-to-year variability in Antarctic ARs changes by the end of the 21st century by region, with links to changes in the large-scale atmospheric circulation accompanying ARs. Finally, we quantify AR-attributed precipitation, precipitation variability, and trends in the future climate, ultimately providing an early assessment of future AR-driven changes to Antarctic surface mass balance.

How to cite: Maclennan, M., Winters, A., Shields, C., Barthelemy, L., Thaker, R., and Wille, J.: Antarctic Atmospheric Rivers in Present and Future Climates, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3671, https://doi.org/10.5194/egusphere-egu24-3671, 2024.

EGU24-4327 | ECS | Orals | AS1.13 | Highlight

Ground-based Remote Sensing of Aerosol, Clouds, Dynamics, and Precipitation in Antarctica - First results from a one-year campaign at Neumayer Station III in 2023 

Martin Radenz, Ronny Engelmann, Silvia Henning, Holger Schmithüsen, Holger Baars, Markus M. Frey, Rolf Weller, Johannes Bühl, Cristofer Jimenez, Johanna Roschke, Lukas Muser, Nellie Wullenweber, Sebastian Zeppenfeld, Hannes Griesche, Ulla Wandinger, and Patric Seifert

Novel ground-based remote sensing observations of aerosols and clouds have been carried out in Antarctica at the German Neumayer Station III (70.67°S, 8.27°W) for a whole year. The deployment of the mobile exploratory platform OCEANET-Atmosphere brought full ACTRIS aerosol and cloud profiling capabilities next to meteorological, radiation, and air chemistry in-situ observations at the Antarctic station. Neumayer III is currently the only station on a floating ice shelf that is manned throughout the year, providing excellent conditions for studying atmospheric effects on the Antarctic ice shelf.

For that deployment the standard instrumentation of OCEANET-Atmosphere (PollyXT Raman polarization Lidar, a HATPRO microwave Radiometer, a Cimel sun and lunar photometer, and Radiation sensors) was extended by a Mira-35 cloud radar, a scanning LITRA-S Doppler lidar and a Parsivel² optical disdrometer. Together, these instruments brought the full ACTRIS aerosol and cloud profiling capabilities to a region where sophisticated ground-based observations were not available. The synergy of the different instruments allows for detailed retrievals of aerosol and cloud properties, such as cloud-relevant aerosol properties, liquid droplet properties and ice crystal concentrations.

While data analysis is ongoing, three scientific highlights have already been identified during austral fall and winter, namely:

  • Observations of a persistent shallow mixed-phase cloud embedded in a plume of advected marine aerosol. State of the art microphysical retrievals are used to obtain aerosol and cloud microphysical properties. Closure between cloud-relevant aerosol particles and precipitating ice crystals was achieved, demonstrating that the cloud formed in an aerosol-limited environment.
  • Two extraordinary warm air intrusions: One with intense snowfall produced the equivalent of 10% of the yearly snow accumulation, a second one with record high temperatures and heavy icing due to supercooled drizzle.
  • Omnipresent aerosol layers in the stratosphere, contributing almost 50% to the aerosol optical depth of around 0.06 at 500nm. Lidar-derived optical signatures revealed sulphate aerosol in the stratosphere - most likely linked to the Hunga Tonga eruption in 2022.

We will present an overview of the campaign, the three highlights and provide an outlook on potential future usage of the dataset.

How to cite: Radenz, M., Engelmann, R., Henning, S., Schmithüsen, H., Baars, H., Frey, M. M., Weller, R., Bühl, J., Jimenez, C., Roschke, J., Muser, L., Wullenweber, N., Zeppenfeld, S., Griesche, H., Wandinger, U., and Seifert, P.: Ground-based Remote Sensing of Aerosol, Clouds, Dynamics, and Precipitation in Antarctica - First results from a one-year campaign at Neumayer Station III in 2023, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4327, https://doi.org/10.5194/egusphere-egu24-4327, 2024.

EGU24-4752 | ECS | Orals | AS1.13

Open Water in Sea Ice Causes High Bias in Polar Low-Level Clouds in GFDL CM4 

Xia Li, Zhihong Tan, Youtong Zheng, Mitchell Bushuk, and Leo Donner

Global climate models (GCMs) struggle to simulate polar clouds, especially low-level clouds that contain supercooled liquid and closely interact with both the underlying surface and large-scale atmosphere. Here we focus on GFDL's latest coupled GCM–CM4–and find that polar low-level clouds are biased high compared to observations. The CM4 bias is largely due to moisture fluxes that occur within partially ice-covered grid cells, which enhance low cloud formation in non-summer seasons. In simulations where these fluxes are suppressed, it is found that open water with an areal fraction less than 5% dominates the formation of low-level clouds and contributes to more than 50% of the total low-level cloud response to open water within sea ice. These findings emphasize the importance of accurately modeling open water processes (e.g., sea ice lead-atmosphere interactions) in the polar regions in GCMs.

How to cite: Li, X., Tan, Z., Zheng, Y., Bushuk, M., and Donner, L.: Open Water in Sea Ice Causes High Bias in Polar Low-Level Clouds in GFDL CM4, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4752, https://doi.org/10.5194/egusphere-egu24-4752, 2024.

EGU24-5220 | ECS | Posters on site | AS1.13

Clouds and precipitation in the initial phase of marine cold air outbreaks as observed by airborne remote sensing 

Imke Schirmacher, Sabrina Schnitt, Marcus Klingebiel, Nina Maherndl, Benjamin Kirbus, and Susanne Crewell

During Arctic marine cold air outbreaks (MCAOs), cold and dry air flows from the central Arctic southward over the open ocean. There, cloud streets form that transform to cellular convection downstream under extreme surface heat fluxes. MCAOs strongly affect the Arctic water cycle through large-scale air mass transformations and can lead to extreme weather conditions at mid-latitudes. The description of air mass transformations is still challenging partly because previous observations do not resolve fine scales and lack information about cloud microphysical properties. Therefore, we focus on the crucial initial phase of development within the first 170 km over open water of two MCAO events with different strengths observed during the HALO-(AC)3campaign. Both times the POLAR 5 and 6 aircraft flew several legs along the same track perpendicular to the cloud streets crossing the sea ice edge several times to allow a quasi-Lagrangian perspective. Based on high-resolution remote sensing and in-situ measurements, the development of the boundary layer, formation of clouds, onset of precipitation, and riming are studied. We establish a novel approach based on radar reflectivity measurements only to detect roll circulation that forms cloud streets.

For the event with the stonger contrast between surface and 850 hPa potential temperature (MCAO index), cloud tops are higher, more liquid-topped clouds exist, the liquid layer at cloud top is wider, and the liquid water path, mean radar reflectivity, amount of rime mass, precipitation rate and occurrence are larger compared to the weaker event. However, the width of the roll circulation is similar for both MCAO events. All parameters, moreover, evolve with distance over open water, as the boundary layer deepens and cloud top heights rise. Cloud streets form after traveling 15 km over open water. After 20 km, cloud cover increases to just below 100 % and after around 30 km, precipitation forms. We find that maxima in the rime mass have the same horizontal scale as the roll circulation. The presentation will highlight how cloud macro- and microphysical parameters vary with distance over open water and explain the differences between both MCAO events.

How to cite: Schirmacher, I., Schnitt, S., Klingebiel, M., Maherndl, N., Kirbus, B., and Crewell, S.: Clouds and precipitation in the initial phase of marine cold air outbreaks as observed by airborne remote sensing, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5220, https://doi.org/10.5194/egusphere-egu24-5220, 2024.

EGU24-6156 | ECS | Posters on site | AS1.13

Ice crystal numbers in Arctic clouds over sea ice and ocean: satellite retrievals and cloud-resolving modelling 

Iris Papakonstantinou Presvelou and Johannes Quaas

Mixed-phase and ice clouds are prominent parts of the Arctic climate system. In particular, boundary layer clouds and their interactions with local aerosols may play an important role in the amplified warming that has been observed in the Arctic during the recent years. These aerosols which are known as ice nucleating particles (INPs) are necessary for the heterogeneous ice formation in temperatures above -38oC. Several in-situ observations have measured a high number of effective ice nucleating particles, possibly related to biological activity in the open ocean. In contrast, in our previous study analyzing the novel active remote sensing dataset DARDAR-Nice for ten years in the Arctic region (Papakonstantinou-Presvelou et al., 2022), we found an increased ice number in low-level clouds over sea ice compared to the open ocean, suggesting other possible factors that might contribute to this difference. Here we perform several sensitivity experiments with the ICON model at kilometer-scale resolution in order to investigate the effect of these factors to the ice number, namely the contribution of local INPs, blowing snow and secondary ice production.

How to cite: Papakonstantinou Presvelou, I. and Quaas, J.: Ice crystal numbers in Arctic clouds over sea ice and ocean: satellite retrievals and cloud-resolving modelling, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6156, https://doi.org/10.5194/egusphere-egu24-6156, 2024.

EGU24-6664 | Orals | AS1.13 | Highlight

Antarctic precipitation: distributed observations during the POPE and AWACA campaigns 

Alexis Berne and Alfonso Ferrrone

Although the deployment of ground-based remote sensing instruments has made possible significant progress, Antarctic precipitation remains poorly understood, in particular away from the scientific stations where most field campaigns have taken place in the past. The PEA Orographic Precipitation Experiment (POPE) campaign took place at the Princess Elisabeth Antarctica station (Queen Maud Land, East Antarctica) during the austral summer 2019-2020. In this framework, a transect of three Doppler vertically profiling precipitation radars (MRR-PRO) was deployed from 20 to 30 km away from the station, in complete autonomy in the complex terrain of the Sor Rondane Mountains. The measurements collected during this campaign highlighted the complex interactions between the terrain and a dry layer likely due to katabatic winds, modulating the occurrence of precipitation in the area.
This POPE campaign also served as a test of the idea of deploying complex instruments dedicated to cloud and precipitation monitoring in complete autonomy to access relevant information away from stations, in areas poorly covered so far. This is a strong motivation for the AWACA project (ERC Synergy), which aims to study the atmospheric branch of the water cycle over Antarctica. AWACA started in September 2021 with the design and construction of autonomous observation platform units (4 in total) sheltering various sensors: surface meteorology, isotopic composition of water vapor and precipitation, and remote sensing of clouds and precipitation. The main deployment along a 1100-km transect between the Dumont d'Urville station at the coast and the Concordia station on the inner Plateau, is scheduled for the austral summer 2024-2025.
In this presentation, I will summarize the main results about precipitation from the POPE campaign as well as the main objectives of the AWACA project.

How to cite: Berne, A. and Ferrrone, A.: Antarctic precipitation: distributed observations during the POPE and AWACA campaigns, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6664, https://doi.org/10.5194/egusphere-egu24-6664, 2024.

EGU24-8702 | ECS | Posters on site | AS1.13

Assessing the Performance of the Weather Research and Forecasting (WRF) Model in Simulating Atmospheric In-Cloud Icing Over Fagernesfjellet, Norway 

Pravin Punde, Yngve Birkelund, Muhammad Virk, and Xingbo Han

Atmospheric icing ensues when water droplets in the atmosphere freeze upon interacting with diverse objects, presenting substantial hazards to infrastructure and leading to disruptions in both road and air traffic. 

This study introduces a detailed analysis of in-cloud icing conducted specifically over Fagernesfjellet, Norway. Utilizing the Weather Research and Forecasting (WRF) model, ERA-5 data was employed for both initial and lateral boundary conditions. The simulation covers a three-month period from October 1, 2022, to December 31, 2022, with a grid spacing of 9,3,1 km.

Acknowledging the substantial influence of local terrain on icing conditions, the analysis prioritizes the highest model resolution. The determination of the icing load involves the utilization of a Makkonen ice accretion model, and the resultant values, alongside surface parameters, undergo validation against field measurements taken at Fagernesfjellet, Norway. The representation of supercooled liquid water (SLW) in numerical weather prediction (NWP) models is crucial for precise atmospheric icing forecasts. Hence, we conduct a comprehensive evaluation of the Thompson scheme's performance in simulating liquid water content (LWC) and, consequently, the icing load, along with general weather parameters associated with icing.

From our preliminary analysis, the WRF model showcases effectiveness in simulating in-cloud icing conditions. WRF adeptly reproduces crucial surface parameters such as temperature, pressure, relative humidity, wind speed, and direction. Nevertheless, there are discernible differences between the observed data and WRF results, particularly noticeable in the case of wind speed and direction.

How to cite: Punde, P., Birkelund, Y., Virk, M., and Han, X.: Assessing the Performance of the Weather Research and Forecasting (WRF) Model in Simulating Atmospheric In-Cloud Icing Over Fagernesfjellet, Norway, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8702, https://doi.org/10.5194/egusphere-egu24-8702, 2024.

EGU24-9122 | Posters on site | AS1.13

Microphysical cloud properties in the initial phase of Arctic cold air outbreaks 

Marcus Klingebiel, Evelyn Jäkel, Michael Schäfer, André Ehrlich, and Manfred Wendisch

Cloud streets are a common feature of cold air outbreaks in the Arctic region. These are long, parallel bands of cumulus clouds that form perpendicular to the wind direction. They are caused by the interaction between the cold air mass and the warm ocean surface. Within the framework of (AC)³, the HALO-(AC)³ campaign was performed in spring 2022 involving several research aircraft to study cold air outbreaks and their belonging cloud streets. In this study we use a spectral imaging instrument, called AISA Hawk, to retrieve cloud microphysical properties in the very initial phase of these cloud streets and therefore focus on their development over the leads in the marginal sea ice zone. 

How to cite: Klingebiel, M., Jäkel, E., Schäfer, M., Ehrlich, A., and Wendisch, M.: Microphysical cloud properties in the initial phase of Arctic cold air outbreaks, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9122, https://doi.org/10.5194/egusphere-egu24-9122, 2024.

A fundamental divide exists between previous studies which conclude that polar amplification does not occur without sea ice and studies which find that polar amplification is an inherent feature of the atmosphere independent of sea ice. We hypothesise that a representation of climatological ocean heat transport is key for simulating polar amplification in ice-free climates. To investigate this we run a suite of targeted experiments in the slab ocean aquaplanet configuration of CESM2-CAM6 with different profiles of prescribed ocean heat transport, which are invariant under CO2 quadrupling. In simulations without climatological ocean heat transport, polar amplification does not occur. In contrast, in simulations with climatological ocean heat transport, robust polar amplification occurs in all seasons. What is causing this dependence of polar amplification on ocean heat transport? Energy-balance model theory is incapable of explaining our results and in fact would predict that introducing ocean heat transport leads to less polar amplification. We instead demonstrate that shortwave cloud radiative feedbacks can explain the divergent polar climate responses simulated by CESM2-CAM6. Targeted cloud locking experiments in the zero ocean heat transport simulations are able to reproduce the polar amplification of the climatological ocean heat transport simulations, solely by prescribing high latitude cloud radiative feedbacks. We conclude that polar amplification in ice-free climates is underpinned by ocean-atmosphere coupling, through a less negative high latitude shortwave cloud radiative feedback that facilitates enhanced polar warming. In addition to reconciling previous disparities, these results have important implications for interpreting past equable climates and climate projections under high emissions scenarios.

How to cite: England, M. and Feldl, N.: Robust polar amplification in ice-free climates relies on ocean heat transport and cloud radiative effects , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9946, https://doi.org/10.5194/egusphere-egu24-9946, 2024.

EGU24-10621 | ECS | Orals | AS1.13 | Highlight

Arctic Warm and Moist Air Intrusions in ICON Simulations 

Jan Landwehrs, Sofie Tiedeck, Sonja Murto, and Annette Rinke

Warm and moist air intrusions (WAI) contribute strongly to extreme warm events in the central Arctic and deliver a major part of the moisture transport into this region, with significant impacts on cloud formation and the surface energy balance. Within the PolarRES EU-project we use the ICON model to study such events both in case studies for the MOSAiC expedition and climate simulations.

MOSAiC provided comprehensive observations of two WAIs in mid-April 2020 when near-surface air temperatures reached the melting point for the first time in this spring. We evaluate different ICON-LAM set-ups, including a pan-Arctic domain with 11km horizontal resolution, as well as more confined domains at convection-permitting 2.5km resolution with varying cloud microphysics settings. A better agreement with local observations is found on the smaller model domains at higher resolution. Additionally, the representation of liquid water is improved by using a more complex two-moment cloud microphysics scheme, where a scenario with higher CCN (cloud condensation nuclei) concentration is found to be more suitable for the aerosol-rich intrusion around April 16.

In a climatological perspective we demonstrate the tracking of moisture intrusion events in decadal-scale climate simulations with ICON-LAM at 11km resolution in a pan-Arctic domain. We drive the regional model with ERA5 and selected CMIP6 GCMs to obtain vertically integrated water vapor transport at high spatial and temporal resolution. This is then used to identify, track and classify WAIs, to study their climatological characteristics, impacts and long-term trends under climate change.

How to cite: Landwehrs, J., Tiedeck, S., Murto, S., and Rinke, A.: Arctic Warm and Moist Air Intrusions in ICON Simulations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10621, https://doi.org/10.5194/egusphere-egu24-10621, 2024.

EGU24-11947 | ECS | Orals | AS1.13

Boundary-layer cloud modeling challenges on the North Slope of Alaska 

Kyle Fitch, Zachary Cleveland, McKenna Stanford, and Lindsay Dedrickson

The accurate modeling and prediction of cloud base heights is critical for energy balance calculations and aviation operations, alike. Low-level (i.e., boundary-layer) Arctic clouds can be difficult to model, making prediction of formation and dissipation challenging. Primarily mixed-phase, these clouds typically contain low quantities of supercooled liquid water and often slowly precipitate relatively small amounts of moderately and heavily rimed snow particles. While this appears to be the predominant cloudy state on the North Slope of Alaska (NSA), the delicate balance of microphysical, dynamical, radiative, surface coupling, and advective processes can rapidly shift to heavy snow (with various degrees of riming) or to a complete dissipation of the cloud layer without any precipitation, depending on the dominant processes. Here we strive to disentangle these various processes. First, we compare the predictive performances of four different numerical weather models in forecasting the presence and base-heights of low-level clouds: the High-Resolution Rapid Refresh - Alaska (HRRR-AK) model, the Polar Weather Research and Forecasting (Polar WRF) model, the Unified Model (UM), and the European Centre for Medium-range Weather Forecasting (ECMWF) model.  Initial results comparing model output at two U.S. Department of Energy Atmospheric Radiation Measurement (AMT) NSA sites, during the fall season in 2019 and 2022, show that the UM slightly outperforms the HRRR-AK in terms of accurately forecasting the presence of a low-level cloud layer (89% of the time). All models have a significant bias of 300 to 800 meters in forecasting cloud base height (lower than is observed); however, the UM and ECMWF models have the lowest biases. Finally, a case study for a particularly challenging April 2017 thin-cloud event is presented, wherein we compare the performance of four different bulk microphysical parameterization schemes using a higher-resolution large eddy simulation (LES) model, the WRF-LES. Initial results show that the Thompson scheme was the only one able to reproduce and sustain a substantial supercooled liquid layer, but it was unable to reproduce the transition from a deep, liquid-rich cloud to a thin layer with moderately and heavily rimed precipitation. This is the first step in linking simulated LES-scale riming processes with those parameterized at a coarser mesoscale model scale. This has important implications for forecasting low-level clouds in an operational environment, given the efficiency of the riming process.

How to cite: Fitch, K., Cleveland, Z., Stanford, M., and Dedrickson, L.: Boundary-layer cloud modeling challenges on the North Slope of Alaska, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11947, https://doi.org/10.5194/egusphere-egu24-11947, 2024.

EGU24-13193 | Posters on site | AS1.13

Atmospheric Rivers vis-à-vis the Summer Seasonal Cycle and Regional Greenland Surface Melt 

William Neff, Christopher Cox, Mathew Shupe, and Michael Gallagher

Recent analysis [Mattingly et al., 2023] suggests that Atmospheric Rivers (ARs) in combination with planetary scale dynamics and coupled orographic processes (e.g., foehn effect), could lead to enhanced melting in northeast Greenland and could, in turn, be linked to increasing mass loss from outflow glaciers there [Khan et al., 2022]. The importance of large-scale dynamics, which is supported by other studies too (e.g., Neff et al., 2014), led us to examine more generally the patterns of summer melt over the whole of Greenland as influenced by factors such as the seasonal cycle, the frequency of ARs, and general synoptic influences.

Our AR detection method used ERA-5 reanalysis daily data at 65oN, 55oE and 850 hPa from 2000 through 2022, JJA, and for wind directions between 112.5o and 225.0o.  We carried out linear analysis correlation between integrated water vapor, IWV; tropospheric temperature, T850 hPa; tropospheric wind speed, WS 850 hPa; and melt fraction (MF) in an area over the southwest coast near where the typical AR track first encounters the ice sheet between 62-67oN and 50-47o E.  We found high correlation between high IWV and temperature; good correlation between IWV, coastal MF and T850 hPa; and  weak dependence of MF on southerly wind speed.

A consideration in quantifying the effects of ARs on total surface melt is the fact that their influence can extend over multi-day periods. The effect continues along the west coast after the warm front has passed over the ice sheet at the end of the AR life cycle when residual moist, warm air remains trapped in the downstream low along the 3-km high ice sheet, affecting surface energy budgets and where smaller less-ordered mesoscale circulations remain. In addition, because the initial northward transport occurs in concert with a strong ridge centered just east of the center of the ice sheet.  In our analysis we will show results associated with four melt areas: 1) near coastal to the west, 2) over the lower accumulation region such as in the area of the old Dye-2 radar site, 3) at the Summit of Greenland where melt is historically low but of increasing frequency of late, and 4) in the far northeast which was of interest in Mattingly et al. (2023). ARs directly affect the southwest ice sheet and their frequency can modulate MF near the shoulder seasons. Secondary effects along the east coast as the ridge passes, which may include subsidence (Mattingly et al. 2023), are weak but detectable. The frequency of ARs is less influential in the southwest in mid-summer when mean temperatures are warmer throughout the region. Melting in the northeast is only weakly related to ARs and generally follows to the seasonal cycle of warming.

 

Neff, W., et al. (2014, JGR, doi:10.1002/2014JD021470).

Khan, S. A., et al. (2022), E, Nature, doi:10.1038/s41586-022-05301-z.

Mattingly, K. S.,  et al.(2023), , Nature Communications, 14(1), 1743, doi:10.1038/s41467-023-37434-8.

How to cite: Neff, W., Cox, C., Shupe, M., and Gallagher, M.: Atmospheric Rivers vis-à-vis the Summer Seasonal Cycle and Regional Greenland Surface Melt, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13193, https://doi.org/10.5194/egusphere-egu24-13193, 2024.

EGU24-13345 | ECS | Posters on site | AS1.13 | Highlight

Precipitation in the Arctic and Southern Ocean: new insights from aircraft and ship-borne measurements 

Larry Ger Aragon, Yi Huang, Peter May, Jonathan Crosier, Paul Connolly, Estefania Montoya Duque, and Keith Bower

Precipitation is an important component of the hydrologic cycle and sea ice mass balance in polar regions. However, precipitation products in high latitudes constitute the highest uncertainties among satellite retrievals and numerical models. These uncertainties arise from limited in-situ observations of high-latitude precipitation and the fundamental differences between the Arctic and Southern Ocean/Antarctic environments that complicate the key precipitation properties and associated processes. To help address this knowledge gap, this study uses recent aircraft and ship-borne measurements to understand better the microphysical properties of precipitation over the Arctic and Southern Ocean/Antarctic regions. For the Arctic case, select summertime precipitation events are examined using aircraft measurements from precipitation imaging probes. We present the microphysical properties of Arctic precipitation in terms of the dominant ice precipitation type, particle size distributions, and important bulk properties. For the Southern Ocean/Antarctic case, we use recent measurements from ship-borne disdrometer and dual-polarimetric radar and present the distinctive polarimetric signatures and surface precipitation properties of seven synoptic types across the Southern Ocean. We also demonstrate an improved radar rainfall retrieval algorithm for the region, considering the dominance of small raindrop sizes of less than one millimeter in Southern Ocean rainfall. This research is leading toward more accurate, high-resolution estimates of precipitation properties in high-latitude regions, crucial in advancing the understanding of a range of climatological and meteorological processes as well as in evaluations of weather and climate models.

How to cite: Aragon, L. G., Huang, Y., May, P., Crosier, J., Connolly, P., Montoya Duque, E., and Bower, K.: Precipitation in the Arctic and Southern Ocean: new insights from aircraft and ship-borne measurements, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13345, https://doi.org/10.5194/egusphere-egu24-13345, 2024.

EGU24-14866 | Orals | AS1.13

Coordinated observations of the water cycle of marine cold-air outbreaks in the European Arctic during the ISLAS 2022 field campaign 

Harald Sodemann, Iris Thurnherr, Andrew Seidl, Alena Dekhtyareva, Aina Johannessen, Marvin Kähnert, Mari B. Steinslid, Sander Løklingholm, Lars R. Hole, Paul Voss, Lukas Papritz, Marina Dütsch, Robert O. David, Tim Carlsen, David M. Chandler, Patrick Chazette, Julien Totems, Alfons Schwarzenboeck, Franziska Hellmuth, and Julien Delanoe and the ISLAS2022 Team

Marine cold-air outbreaks (mCAOs) are a characteristic type of high-impact weather in the European Arctic and are characterized by an intense water cycle where polar cloud processes play an important role. Model simulations and weather forecasts of mCAO events are challenging and associated with poor predictability. One reason is that processes related to the water cycle interact with one another on a wide range of scales. In regional models, some of these processes are resolved and others are fully or partly parameterised. To test and improve numerical weather prediction models, additional observations and novel types of measurements of water vapour are highly demanded. Stable water isotopes are an increasingly available measurement, allowing to trace sub-grid scale processes, and providing the potential to constrain the mass budget of the atmospheric water cycle during mCAO events. During the ISLAS2022 field experiment (21 March to 10 April 2022), the stable isotope composition of water vapour and liquid samples, cloud structures, and other meteorological parameters were collected between Svalbard and Northern Scandinavia on various measurement platforms. Airborne survey flights to Svalbard provided the ocean evaporation signature and subsequent processing of water vapour during mCAO conditions. During a number of flights, mCAO airmasses were repeatedly sampled over a course of hours to days, allowing to characterize their thermodynamic evolution as clouds were first forming, then glaciating and precipitating. In addition, vapour isotope and sea water isotope measurements were taken continuously onboard R/V Helmer Hanssen between Tromsø and the Greenland west coast. Finally, coordinated land-based measurement activity over Northern Norway and Sweden allowed collection of precipitation samples, thus closing the mass budget of the mCAO events. Furthermore, using buoyancy-controlled meteorological balloons launched from Ny Ålesund, we additionally obtained continuous in-situ measurements of the boundary-layer evolution during the mCAO. We provide an overview over the airborne and ground-based measurement activities during the campaign and provide several examples to highlight the potential of the stable water isotope measurements to constrain the water budget of mCAOs in conjunction with traditional meteorological observations.

How to cite: Sodemann, H., Thurnherr, I., Seidl, A., Dekhtyareva, A., Johannessen, A., Kähnert, M., Steinslid, M. B., Løklingholm, S., Hole, L. R., Voss, P., Papritz, L., Dütsch, M., David, R. O., Carlsen, T., Chandler, D. M., Chazette, P., Totems, J., Schwarzenboeck, A., Hellmuth, F., and Delanoe, J. and the ISLAS2022 Team: Coordinated observations of the water cycle of marine cold-air outbreaks in the European Arctic during the ISLAS 2022 field campaign, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14866, https://doi.org/10.5194/egusphere-egu24-14866, 2024.

EGU24-14968 | ECS | Orals | AS1.13

Insights into cloud biases over high-latitude oceans from a cloud-controlling factor framework 

Joaquin Blanco, Rodrigo Caballero, Steven Sherwood, and Lisa Alexander

A long-standing and pervasive problem within the modelling community is the proper representation of cloud albedo over the Southern Hemisphere (SH) oceanic region. Errors persist despite the extensive evidence that these are related to the unique microphysical characteristics of the austral clouds. In this study we investigate additional causes of cloud albedo biases over the 50˚–65˚ oceanic band using CMIP6 simulations and a cloud-controlling factor (CCF) approach on daily timescales. We gain further insight by replicating our method over the equivalent oceanic region in the Northern Hemisphere (NH).

Cloud albedo, computed from upwelling and downwelling shortwave radiation at surface and top of the atmosphere, is averaged into bins of vertical velocity, surface wind, and sea-surface temperature. The performance of fifteen models in both atmospheric-only and ocean-coupled configurations is evaluated against CERES satellite retrievals in combination with ERA5 reanalysis for the 2000–2014 period.

When averaging cloud albedo by vertical velocity bins, we find that shallow boundary-layer (deep convective) clouds are consistently underpredicted (overpredicted) over the high-latitude oceans of the SH. We repeat the method for the 50˚–65˚ band in the North Atlantic and Pacific oceans and find that similar compensating errors exist.

Another important result is that the SH cloud biases occur for sea-surface temperatures below 4°C. We show that a connection exists between this empirical finding and the biases as determined from microphysical effects, i.e.: a deficit of cloud albedo is due to models producing glaciated rather than supercooled liquid water clouds. Our CCF method allow us to see that in such cases, models tend to simulate NH clouds for the SH.

We also find that the positive sign of the cloud albedo hemispheric asymmetry (SH-NH difference over the 50°–65° band) is consistently predicted by nearly all models, many of which also predict a similar magnitude to observations. However, this is a consequence of compensating errors as individually most models tend to either overpredict or underpredict cloud albedo in both hemispheres.

How to cite: Blanco, J., Caballero, R., Sherwood, S., and Alexander, L.: Insights into cloud biases over high-latitude oceans from a cloud-controlling factor framework, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14968, https://doi.org/10.5194/egusphere-egu24-14968, 2024.

Cold air outbreaks (CAOs) are a key component of the Arctic climate system, featuring intense convective cloud fields embedded in cold, dry air masses over relatively warm surfaces. Large-Eddy Simulation (LES) is a technique often used to investigate CAOs at high spatial and temporal resolutions, resolving the intricate processes involved and providing a wealth of virtual data. A complication with LES studies of CAOs is the typical absence of suitable observational data to fully constrain the simulations, and thus anchor them in reality. This study aims to use observational data from the recent airborn HALO-(AC)³ campaign in the Atlantic sector of the Arctic to drive LES experiments exclusively with observations. To this purpose data from Research Flights 10 and 11 are used, which probed a weak CAO in the Fram Strait on 29 and 30 March 2022. A Lagrangian model framework is adopted, making use of observations along the two-day low-level trajectory that stretched from close to the North Pole to the sea-ice free area Southwest of Svalbard. HALO observations are integrated into the reanalysis-based model forcing in an incremental way, yielding a suite of forcing datasets. These observational data consist of vertical soundings of thermodynamic state, pressure gradients, mesoscale divergence and advective tendencies, as
well as surface properties to act as boundary conditions. The LES code incorporates advanced representations for mixed-phase microphysical processes and radiative transfer, to allow a realistic representation of clouds and turbulence in the transforming low-level airmass. LES results obtained with
this setup are evaluated against independent HALO datasets on clouds and other boundary-layer properties. Inter-comparing the suite of LES runs with different forcing datasets elucidates the impacts of individual forcing components on the air mass transition and associated cloud evolution. 

How to cite: Paulus, F. and Neggers, R.: Studying Cloud Transformations in Cold Air Outbreaks using Large-Eddy Simulations Exclusively Driven by HALO-(AC)³ Campaign Data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15625, https://doi.org/10.5194/egusphere-egu24-15625, 2024.

EGU24-16011 | ECS | Posters on site | AS1.13

Differential absorption G-band radar for Arctic clouds and water vapor observations 

Sabrina Schnitt, Mario Mech, Jens Goliasch, Davide Ori, Thomas Rose, and Susanne Crewell

The Arctic climate is changing at fast pace. The contribution of low-level clouds to Arctic amplification feedback processes remains challenging to quantify as model evaluation requires continuous, high-quality observations in a demanding environment. Advancing the understanding of governing processes in mixed-phase clouds, ubiquitous in the Arctic, calls for temporally high-resolved measurements of cloud and precipitation microphysical properties as well simultaneous quantification of water vapor amount and profiles in all-weather conditions.

We present the novel and worldwide unique G-band Radar for Water vapor profiling and Arctic Clouds (GRaWAC) system, suitable to deliver these measurements. GRaWAC is a FMCW G-band radar with Doppler-resolving capabilities and simultaneous dual-frequency operation at 167 and 175GHz. The Differential Absorption Radar technique is applied to the measurements to derive temporally continuous water vapor profiles in cloudy and precipitating conditions, which closes a current gap in observational state-of-the-art instrumentation.

We reveal first measurements from a mid-latitudinal ground site and airborne test flights to illustrate GraWAC’s potential for water vapor, cloud and precipitation profiling. Based on instrument simulations, we outline the benefits of such observations at an Arctic ground-based supersite, such as AWIPEV station, Ny-Alesund, Spitsbergen. There, the G-band radar measurements will be embedded in a synergy of remote sensing instruments, including an operational microwave radiometer and a Ka- and W-band cloud radar, respectively. We highlight future applications of these synergistic measurements, and therein especially the multi-frequency radar space, for model evaluation studies targeting an improved representation of mixed-phase clouds in the Arctic.

How to cite: Schnitt, S., Mech, M., Goliasch, J., Ori, D., Rose, T., and Crewell, S.: Differential absorption G-band radar for Arctic clouds and water vapor observations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16011, https://doi.org/10.5194/egusphere-egu24-16011, 2024.

EGU24-16088 | ECS | Orals | AS1.13 | Highlight

Investigating potential sources of Ice Nucleating Particles around the Antarctic peninsula 

Floortje van den Heuvel, Mark Tarn, Benjamin Murray, and Thomas Lachlan-Cope

Clouds are a major source of uncertainty in climate model projections, especially in the Southern Ocean where the large model biases in short and long wave radiative fluxes affect the model representation of sea surface temperatures, sea ice and ultimately large scale circulation in the Southern Hemisphere. Evidence suggests that the poor representation of mixed phase clouds and the role of Ice Nucleating Particles (INPs) in these clouds are likely to be responsible for the model biases in this region. To understand how clouds will respond in a future climate we need to both better understand the effects and sources of INPs in the present, and attempt to anticipate the importance of new sources of INPs which could be revealed in a warming climate and by a reduction in glacial coverage.

In order to achieve this, we have dispersed samples of dusts from the Antarctic peninsula and James Ross Island in the Leeds aerosol chamber to characterise the size-resolved ice-nucleating activity of Southern high latitude dusts and to determine the heat lability of the INPs as a potential indicator for biogenic ice nucleators. We’ve also created suspensions from a number of Antarctic mosses and lichen to measure the ice-nucleating activity of these potential sources of INPs. Preliminary results indicate that the collected dusts nucleated ice at temperatures between -18 ºC and -14 ºC while mosses and lichen nucleated ice at temperatures ranging from -18 ºC to -6 ºC, depending on the source. Future work will include a comparison with ambient air filter samples collected around Rothera (Antarctic peninsula) and in the Arctic.

How to cite: van den Heuvel, F., Tarn, M., Murray, B., and Lachlan-Cope, T.: Investigating potential sources of Ice Nucleating Particles around the Antarctic peninsula, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16088, https://doi.org/10.5194/egusphere-egu24-16088, 2024.

EGU24-16503 | ECS | Posters on site | AS1.13

Investigating Arctic Clouds and Water Vapor over Sea Ice: Airborne Passive Microwave Observations during HALO-(AC)3 

Nils Risse, Mario Mech, Catherine Prigent, and Susanne Crewell

Clouds and water vapor play a critical role in the water and energy balance of the Arctic. However, few field observations of these quantities over sea ice exist. Passive microwave observations provide high sensitivity to clouds and water vapor with high spatial and temporal coverage in polar regions. However, retrievals of atmospheric quantities from satellites and aircraft require a description of the variable sea ice emissivity, which depends on the properties of sea ice and snow. Recently, improved retrieval methods that derive sea ice and atmospheric properties simultaneously allowed for improved exploitation of the information from passive microwave observations.

This work presents liquid water path (LWP), ice water path (IWP), and integrated water vapor (IWV) retrieved from the HALO Microwave Package (HAMP) operated onboard the HALO aircraft during the HALO-(AC)3 field campaign in spring 2022 in the Fram Strait. The nadir-viewing HAMP measures along two water vapor bands (22.24 and 183.31 GHz), two oxygen bands (50-60 and 118.75 GHz), and the atmospheric windows at 31 and 90 GHz over different surface types. The retrieval accounts for variable surface emission through a joint surface-atmosphere optimal estimation scheme with the Passive and Active Microwave Radiative Transfer (PAMTRA) model.

The high spatial coverage of the HALO flights allows for assessing the spatial and temporal variability of the retrieved IWV, LWP, and IWP under various atmospheric and surface conditions. A particular focus lies on the warm air intrusion events and their related poleward changes in cloud properties and water vapor over sea ice that HALO captured. Furthermore, the hectometer-scale airborne observations allow statistical comparison with operational satellite products, reanalysis, and model simulations along the flight track. The HAMP observations will improve the characterization of clouds and water vapor in the Arctic and potentially improve the use of passive microwave satellite observations over sea ice.

How to cite: Risse, N., Mech, M., Prigent, C., and Crewell, S.: Investigating Arctic Clouds and Water Vapor over Sea Ice: Airborne Passive Microwave Observations during HALO-(AC)3, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16503, https://doi.org/10.5194/egusphere-egu24-16503, 2024.

EGU24-17876 | Posters on site | AS1.13 | Highlight

How can the proposed  WIVERN satellite mission improve global snowfall measurements? 

Maximilian Maahn, Alessandro Battaglia, Anthony Illingworth, Pavlos Kollias, Stef Lhermitte, Filippo Emilio Scarsi, and Frederic Tridon

Snowfall is an important climate change indicator affecting surface albedo, glaciers, sea ice, freshwater storage, and cloud lifetime. Accurate snowfall measurements at high latitudes are particularly important for the mass balance of ice sheets and for sustaining healthy ecosystems, including fish and wildlife populations. Yet, snowfall remains a quantity which is hard to measure due to high spatial variability, the remoteness of polar regions and challenges associated with in situ measurements of snowfall. The recently decommissioned NASA CloudSat mission provided invaluable information about global snowfall climatology from 2006 to 2023. The CloudSat-based estimates of global snowfall are considered the reference for global snowfall estimates, but these data sets suffer from poor sampling and the inability to see shallow precipitation, which limits their use, for example, as input to surface mass balance models of the major ice sheets. WIVERN (WInd VElocity Radar Nephoscope) is one of the two remaining ESA Earth Explorer 11 candidate missions equipped with a conical scanning 94 GHz radar and a passive 94 GHz radiometer. The main objective of the mission is to measure global in-cloud winds using the Doppler effect, but can also quantify cloud ice water content and precipitation rate. 

 

This presentation discusses the potential of the WIVERN mission to provide improved estimates of global snowfall measurements. Compared to CloudSat, WIVERN's 800 km swath provides 70 times better coverage and its 42 degree angle of arrival significantly reduces the radar blind zone near the surface (especially over the ocean). In addition, WIVERN's radar is accompanied by a radiometer, which can further improve the estimation of snowfall rates. The improved sampling is demonstrated for specific regions ( Antarctica, Greenland) by computing the sampling error at different spatial and temporal scales via simulations of WIVERN vs. CloudSat orbits based on the snowfall rates produced by ERA5 reanalysis. Clutter and signal to clutter ratio simulations are performed for oceanic surfaces and orographic terrains by using a geometric–optics approach and the WIVERN illumination geometry.  Our results show that the WIVERN sampling strategy significantly reduces the uncertainty in polar snowfall estimates, making it a valuable product for climate model evaluation and as an input to surface mass balance models of the major ice sheets.

How to cite: Maahn, M., Battaglia, A., Illingworth, A., Kollias, P., Lhermitte, S., Scarsi, F. E., and Tridon, F.: How can the proposed  WIVERN satellite mission improve global snowfall measurements?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17876, https://doi.org/10.5194/egusphere-egu24-17876, 2024.

EGU24-18277 | ECS | Posters on site | AS1.13

Investigating the role of air mass history of Arctic black carbon in GCMs 

Roxana S. Cremer, Paul Kim, Sara M. Blichner, Emanuele Tovazzi, Ben Johnson, Zak Kipling, Thomas Kühn, Duncan Watson-Parris, David Neubauer, Phillip Stier, Alistair Sellar, Eemeli Holopainen, Ilona Riipinen, and Daniel G. Partridge

Black Carbon (BC) aerosols are known to be important for the Earth’s climate, yet their exact role to the changing of the Earth’s climate and Arctic amplification remains unclear. An accurate description of the BC life cycle in general circulation models (GCMs) can help reduce the uncertainties due to BC aerosols and specify BC's role in the Arctic.

In this study, several GCMs (ECHAM6.3-HAM2.3, ECHAM6.3-HAM2.3-P3, ECHAM6.3-HAM2.3-SALSA2 and UKESM1.0) are compared in terms of their representation of BC mass in the Arctic within the AeroCom project GCM Trajectory. A novel Lagrangian framework is employed to examine the history of air masses reaching the observational station Zeppelin, Svalbard. Therfore the removal processes were analysed along the trajectory and the GCMs compared with each other. The analysis emphasises the impact of remote emissions on local BC concentrations in the Arctic, indicating a longer BC lifetime compared to the global average. This underlines the importance of dry and wet scavenging parametrisations in the GCMs.

 

 

 

How to cite: Cremer, R. S., Kim, P., Blichner, S. M., Tovazzi, E., Johnson, B., Kipling, Z., Kühn, T., Watson-Parris, D., Neubauer, D., Stier, P., Sellar, A., Holopainen, E., Riipinen, I., and Partridge, D. G.: Investigating the role of air mass history of Arctic black carbon in GCMs, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18277, https://doi.org/10.5194/egusphere-egu24-18277, 2024.

EGU24-18940 | Posters on site | AS1.13

Liquid water path derived from airborne observations over the sea-ice-free Arctic ocean 

Mario Mech, Maximilin Ringel, Nils Risse, and Susanne Crewell

Arctic Amplification is most evident in the rise of the near-surface air temperature observed in the last decades, which has been at least twice as strong as the global average. The mechanisms behind that are widely discussed. Many processes and feedback mechanisms still need to be better understood, especially those connected to clouds and their role in the water and energy cycle. Thereby, the cloud liquid water path (LWP) is an important cloud parameter, and it is important to know its occurrence and spatial variability. However, observing LWP is prone to high uncertainties, especially in the Arctic, leading to about a factor of two difference in satellite retrievals between microwave and near-infrared retrievals. Moreover, weather and climate models show significant differences in Arctic regions.

Within this contribution, we will present LWP observations over the sea-ice-free Arctic ocean from measurements conducted during four airborne campaigns conducted within the framework of the "Arctic Amplification: Climate relevant atmospheric and surface processes and feedback mechanisms (AC)3" during the last years over the Fram Strait West of Svalbard. The LWP has been derived by statistical retrieval approaches based on brightness temperature measurements of the Microwave Radar/radiometer for Arctic Clouds (MiRAC) operated onboard the Polar 5 research aircraft of the Alfred-Wegener Institute for Polar and Marine Research (AWI). The consistent LWP product has been used in a comparison study to validate satellite estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Microwave Scanning Radiometer 2 (AMSR2) and the one from the ERA5 reanalyses. It could be seen that the various products reveal a characteristic shape of the LWP distribution, but their overall performance varies with season and synoptic situations, i.e., ERA5 does not produce larger LWP values and an over- or under-estimation for specific flights and too high LWP values for MODIS and too low for AMSR2 during cold air outbreak events.

How to cite: Mech, M., Ringel, M., Risse, N., and Crewell, S.: Liquid water path derived from airborne observations over the sea-ice-free Arctic ocean, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18940, https://doi.org/10.5194/egusphere-egu24-18940, 2024.

EGU24-22016 | ECS | Posters virtual | AS1.13

Snowfall particle size distribution and precipitation observations in the Southern Ocean and coastal Antarctica 

Claudio Durán Alarcón, Irina Gorodetskaya, Diogo Luis, Alexis Berne, Michael Lehning, and Katherine Leonard

Snowfall is a key component to the Antarctic region, contributing significantly to the surface mass balance and influencing mean sea level changes. The intricate nature of ice particle microphysics, encompassing type, size, and structure, presents a great challenge in comprehending the processes of solid precipitation in Antarctica. The characteristics of individual ice crystals as they fall from clouds are crucial for understanding their formation and evolution along the vertical profile. Mechanisms such as aggregation, fragmentation, and riming play a pivotal role in accurately representing precipitation in numerical weather prediction models [1]. Despite their importance, the scarcity of observations for evaluating and validating these processes, particularly in the Southern Ocean and Antarctica, adds complexity. To address this gap, a comprehensive set of precipitation observations occurred during the Antarctic Circumnavigation Expedition (ACE) in the austral summer of 2016-2017 was carried out, utilizing diverse sensors aboard the research vessel Akademik Tryoshnikov. The observational toolkit included a snow particle counter (SPC), two total particle counters (Wenglors), vertical precipitation profiles from 24-GHz micro rain radar (MRR) observations, and manually collected Formvar samples. The Formvar technique, preserving ice particle shapes, offers insights into microphysical properties of ice crystals and snowflakes. SPC and Formvar were employed for particle size distribution (PSD) characterization and quantitative precipitation estimations (QPE) [2]. Precipitation was derived from MRR using the existing reflectivity (Ze)-snowfall (S) relationship for Antarctica [3,4,5]. During ACE, primary observations related to snowfall were near the coasts of the Antarctic Peninsula, Western Antarctica, and Adélie Land (Eastern Antarctica). In the last region, a large-scale event was observed by both the ACE expedition and a Multi-angle Snowflake Camera (MASC) at Dumont d’Urville station. Results showed good agreement between Formvar, SPC (size < 500µm), and MASC (size > 500µm) PSDs. Notably, the 20-µm resolution Formvar images exhibited significantly better performance for particles smaller than 500µm compared to MASC (35-µm resolution). Regarding QPE, all sources exhibited a large spread, particularly MRR estimations, sensitive to Ze-S relationship parameters. The use of PSD observations proved useful in making informed choices about these parameters. In monitoring snowfall precipitation, developing a multi-instrumental approach to overcome individual system limitations is crucial, reducing uncertainty.

References:

[1] Grazioli, J. et al. MASCDB, a database of images, descriptors and microphysical properties of individual snowflakes in free fall. Sci Data 9, 186 (2022).

[2] Sugiura, K. et al., Application of a snow particle counter to solid precipitation measurements under Arctic conditions. CRST, 58: 77-83, 2009.

[3] Grazioli, J. et al., Measurements of precipitation in Dumont d'Urville, Adélie Land, East Antarctica. TC 11, 1797–1811, 2017.

[4] Souverijns, N. et al., Estimating radar reflectivity – snowfall rate relationships and their uncertainties over Antarctica by combining disdrometer and radar observations. AR, 196: 211–223, 2017.

[5] M.S. Kulie and R. Bennartz, Utilizing Spaceborne Radars to Retrieve Dry Snowfall. JAMC, 48, 2564-2580.

Acknowledgements: PROPOLAR APMAR-2024, FCT ATLACE (CIRCNA/CAC/0273/2019) and ANR-APRES3. ACE was made possible by funding from the Swiss Polar Institute and Ferring Pharmaceuticals.

How to cite: Durán Alarcón, C., Gorodetskaya, I., Luis, D., Berne, A., Lehning, M., and Leonard, K.: Snowfall particle size distribution and precipitation observations in the Southern Ocean and coastal Antarctica, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22016, https://doi.org/10.5194/egusphere-egu24-22016, 2024.

EGU24-529 | ECS | Posters on site | NH1.5

The Attachment Process of Negative Connecting Leader to the Lateral Surface of Downward Positive Leader in a +CG Lightning Flash 

Qi Qi, Bin Wu, Weitao Lyu, Ying Ma, Lyuwen Chen, Fanchao Lyu, and Yan Gao

In the lightning attachment process, the leader connecting behavior is an interesting topic. In the attachment process of a negative cloud-to-ground lightning flash, the “Tip to the lateral surface” connection type has been widely observed, and researchers have carried out a series of studies and discussions on the characteristics and the physical mechanisms of the leader connecting behavior. However, is there also a “Tip to the lateral surface” connecting behavior in the attachment process of the positive cloud-to-ground lightning flash? In this study, using high-speed video cameras operating with framing rates of 20 and 50 kiloframes per second, we captured an attachment process during a positive cloud-to-ground flash, which demonstrates the connection of the negative connecting leader (NCL) to the lateral surface of the downward positive leader (DPL) for the first time. When the NCL was initiated, the tip of the DPL had passed the initiation position of the NCL for about 50 m. A common streamer zone (CSZ) was observed when the three-dimensional distance between the NCL tip and the lateral surface of DPL was about 30 m. It is remarkable to note that a luminous segment (space stem/leader) with a length of about 7 m was captured within the CSZ during the attachment process. The connection between the NCL tip and the lateral surface of the DPL was caused by the development of the CSZ and its inner space leader.

How to cite: Qi, Q., Wu, B., Lyu, W., Ma, Y., Chen, L., Lyu, F., and Gao, Y.: The Attachment Process of Negative Connecting Leader to the Lateral Surface of Downward Positive Leader in a +CG Lightning Flash, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-529, https://doi.org/10.5194/egusphere-egu24-529, 2024.

EGU24-1238 | ECS | Posters on site | NH1.5

Optical observations of needles evolving into negative leaders in a positive cloud-to-ground lightning flash 

Bin Wu, Qi Qi, Weitao Lyu, Ying Ma, Lyuwen Chen, and Vladimir Rakov

High-speed video records of a single-stroke positive cloud-to-ground (+CG) flash were used to examine the evolution of eight needles developing more or less radially from the +CG channel. All these eight needles occurred during the later return-stroke stage and the following continuing current stage. Six needles, after their initial extension from the lateral surface of the parent channel core, elongated via bidirectional recoil events, which are responsible for flickering, and two of them evolved into negative stepped leaders. For the latter two, the mean extension speed decreased from 5.3 × 10^6 to 3.4 × 10^5 and then to 1.3 × 10^5 m/s during the initial, recoil-event, and stepping stages, respectively. The initial needle extension ranged from 70 to 320 m (N = 8), extension via recoil events from 50 to 210 m (N = 6), and extension via stepping from 810 to 1,870 m (N = 2). Compared with needles developing from leader channels, the different behavior of needle flickering, the longer length, the faster extension speed, and the higher flickering rate observed in this work may be attributed to a considerably higher current (rate of charge supply) during the return-stroke and early continuing-current stages of +CG flashes.

How to cite: Wu, B., Qi, Q., Lyu, W., Ma, Y., Chen, L., and Rakov, V.: Optical observations of needles evolving into negative leaders in a positive cloud-to-ground lightning flash, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1238, https://doi.org/10.5194/egusphere-egu24-1238, 2024.

EGU24-1639 | Orals | NH1.5

Modelling the collision of streamers using the AMReX framework 

Christoph Köhn, Angel Ricardo Jara, Morten Jung Westermann, Mathias Gammelmark, and Elloise Fangel-Lloyd

Streamers, precursors of the hot, long lightning leaders, are small filamentary discharges with high electric fields at their tips. Experiments of laboratory discharges have shown that streamers in their corona can approach each other and it has been suggested that such collisions enhance the electric field in-between beyond the thermal runaway electric field accelerating electrons to the runaway regime thus generating X-rays. Streamer collision also plays a role in the interaction of wind turbine blades with lightning when streamers locally incept from the surface of blades and attract the downward moving lightning leader. Despite the relevance of streamer collisions in the runaway process or their role in the interaction of lightning with wind turbine blades, there have only been a few numerical studies due to computational limitations. We have therefore developed a novel 3D fluid model for streamer propagation implemented in the AMREX framework. AMREX allows us to solve drift-diffusion and Poisson equation using parallelization and GPU support to accelerate the block structured adaptive mesh refinement. We will present details of the implementation as well as a parameter study on typical streamer parameters (electron density, electric field, tip width and velocity,…) during streamer collision in various ambient fields and for various initial electron densities. We will also study various geometries with different displacements of the initial electrons perpendicular to the ambient electric field. Finally, we will interpret our results with respect to the runaway process and wind turbine-lightning interaction.

How to cite: Köhn, C., Jara, A. R., Westermann, M. J., Gammelmark, M., and Fangel-Lloyd, E.: Modelling the collision of streamers using the AMReX framework, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1639, https://doi.org/10.5194/egusphere-egu24-1639, 2024.

The ground-level potential gradient (PG) or the atmospheric electric field, the air-Earth current density as well as the main Global Electric Circuit (GEC) parameters such as the ionospheric potential, global resistance and the total current, can be obtained from the EGATEC engineering model of the GEC (Odzimek et al. 2010) at the resolution of 3 hours. The model input data based on satellite cloud and lightning observation datasets from the period 1998-2006 for evaluating the activity of the GEC cloud generators, and the summer/winter and low/high solar activity conductivity model of Tinsley and Zhou (2006) allow calculating the GEC parameters in the summers and winters of the period. In this work we compare the modelling results to observations from the Stanislaw Kalinowski Geophysical Observatory in Świder, Poland (52°07' N, 21°14' E) of the ground-level potential gradient and conduction current density calculated from the newly digitised PG and positive conductivity data from 1965-2005. We also look for connections in the time variations of the model meteorological input and atmospheric electricity observational data. The work is supported by the Polish National Science Centre grant no 2021/41/B/ST10/04448.

How to cite: Odzimek, A., Tacza, J., Pawlak, I., and Kępski, D.: Analysis of time variations in the Global Electric Circuit parameters from the EGATEC model and Świder atmospheric electricity data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1658, https://doi.org/10.5194/egusphere-egu24-1658, 2024.

The different morphologies of lightning channels are caused by different electrical environments within the cloud, the charge distribution determines the lightning channel morphology, and the lightning morphology can reflect the charge structure to some extent. The distribution of charges is mainly determined by the dynamics and microphysical conditions in clouds, and turbulence plays a significant role in the distribution of charges. Due to the dependence of lightning morphology on the distribution of thunderstorm charges, which is regulated by thunderstorm dynamic effects, a relationship can be established between lightning morphology and thunderstorm dynamic effects.

In this study, the lightning channel was obtained from three-dimensional radiation source localization data from the Lightning Mapping Array at the Langmuir Laboratory of the New Mexico Institute of Mining and Technology. The fractal dimension was used to characterize the complexity of lightning channels, which was calculated by the box-counting method. The S-band dual-polarization Doppler radar data was used to estimate the cube root of the eddy dissipation rate (EDR, the EDR was estimated using the Python Turbulence Detection Algorithm). The EDR and radar radial velocity were used to represent the thunderstorm dynamic characteristics.

Superimposing EDR and radar radial velocities with LMA radiation sources, our analysis shows that the overall morphology and detailed morphology of the lightning channel correspond to different EDR characteristics. Lightning with complex channel morphology has a larger average FD and occurs in regions with large EDRs. In single lightning events, channels that extend directly within a certain height range without significant bifurcation and turning tend to propagate in the direction of decreasing EDRs, while channel bifurcations and turns usually occur in regions with large radial velocity gradients and large EDRs. This study shows the relationship between channel morphology and thunderstorm dynamics and provides a new method for the direct application of channel-level localization data to understand thunderstorm dynamics characteristics.

How to cite: Li, Y., Zhang, Y., Zhang, Y., and Krehbiel, P. R.: Analysis of the Relationship between the Morphological Characteristics of Lightning Channels and Turbulent Dynamics Based on the Localization of VHF Radiation Sources, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2144, https://doi.org/10.5194/egusphere-egu24-2144, 2024.

EGU24-2536 | Posters on site | NH1.5

Lightning Activities near the Red Sea: Effects of Aerosols Morphology and Local Meteorology 

Ashraf Farahat and Maher Dayeh

Lightning activity is one of the global natural hazards that pose significant risks to human life and numerous aspects of society's technological infrastructure. Understanding the linkage between aerosols present in the atmosphere and lightning activity is important to further advance our knowledge of the global lightning activity cycle.

Saudi Arabia and Yemen host one of the world’s largest desert areas namely the Empty Quarter (al-Rubea Al-Khali). Moreover, Saudi Arabia is one of the world’s largest oil exporters with many water desalination, petrochemical, and cement industrial plants, while large cities in both Saudi Arabia and Yemen have large construction projects and vehicle emissions. This increases both natural and anthropogenic aerosol loading in both countries.  Meanwhile, the inland regions close to the Red Sea are one of the 500 hottest lightning regions in the world. This work identifies a possible correlation between lightning activity and aerosol loading.

Using data of individual lightning strokes from the Global Lightning Detection Network (GLD360), in conjunction with remote sensing measurements of the aerosol optical depth (AOD) obtained at 500 nm from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument onboard the Terra and Aqua satellites during active lightning days, we examine the evolution of lightning activity in two geographically and topologically different regions over Saudi Arabia and Yemen. One region extends inland to the desert (R1) and the other is in the southwest mountainous region that is close to the Red Sea (R2). In both regions, results from thunder days only indicate that lightning is strongly and positively correlated with the AOD loading, up to AOD ~ 0.8, after which the trend flattens or reverses direction. Results suggest the two opposite effects that aerosols could indirectly have on lightning activity are at play. The mountainous region exhibits a much stronger linear relation compared to the inland region. Furthermore, both regions exhibit seasonal and asynchronous lightning activity and AOD loading. The year 2018 in R1 shows very high lightning activity, likely linked to the 2018 intense dust storms in the region.

How to cite: Farahat, A. and Dayeh, M.: Lightning Activities near the Red Sea: Effects of Aerosols Morphology and Local Meteorology, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2536, https://doi.org/10.5194/egusphere-egu24-2536, 2024.

EGU24-3358 | Orals | NH1.5

Investigation of the Electric Fields Related to Elves Simulations 

Petr Kaspar, Ivana Kolmasova, Ondrej Santolik, and Martin Popek

Elves are transient luminous events occurring above thunderclouds. They appear as an expanding ring of light at altitudes of 85 – 95 km with diameters of more than 200 km and lasting less than 1 ms. The elves are produced by electromagnetic pulses emitted by underlying high-peak current lightning discharges, which excite nitrogen molecules at the bottom of the ionosphere. We develop an electromagnetic model of elves, which consists of two steps. As the first step, we compute the horizontal part of the electric field at a height of 15 km from transmission line return stroke (RS) models without damping, with linear, and/or exponential damping of the current wave. Subsequently, we solve Maxwell’s equations self consistently for altitudes from 15 km to 95 km, including finite neutral and electron densities, and nonlinearities related to heating, ionization, and attachment of free electrons caused by the RS transient electric field. We show computed electric fields and optical emission rates at the heights of the development of elves. This procedure allows us to distinguish between the electrostatic, induction, and radiation part of the electric field and to investigate their role in the evolution of elves in the full wave simulations.

How to cite: Kaspar, P., Kolmasova, I., Santolik, O., and Popek, M.: Investigation of the Electric Fields Related to Elves Simulations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3358, https://doi.org/10.5194/egusphere-egu24-3358, 2024.

EGU24-3628 | Orals | NH1.5

Glow-terminating terrestrial gamma-ray flashes observed during the ALOFT Campaign 

Steven Cummer, Yunjiao Pu, Andrew Mezentsev, Marni Pazos, Morris Cohen, Nikolai Ostgaard, Mark Stanley, Timothy Lang, Martino Marisaldi, J. Eric Grove, Mason Quick, Hugh Christian, Christopher Schultz, Richard Blakeslee, Ian Adams, Phillip Bitzer, Martin Fullekrug, Bilal Qureshi, Bendik Husa, and Gerald Heymsfield and the additional members of ALOFT team

The ALOFT campaign targeted aircraft measurements of terrestrial gamma-ray flashes (TGFs) through NASA ER-2 overflights of strong thunderstorms.  We report here the analysis of glow-terminating TGFs (GT-TGFs) that occur at the end of some gamma-ray glows.  GT-TGFs were generated by most of the observed storms during the campaign and were prolifically generated by two specific storms that were particularly active in gamma ray production.  One unique feature of GT-TGFs is that they always occur within several tens of microseconds of a narrow bipolar event (NBE).  The characteristics of GT-TGFs and the associated NBE radio emissions will be described in detail.

How to cite: Cummer, S., Pu, Y., Mezentsev, A., Pazos, M., Cohen, M., Ostgaard, N., Stanley, M., Lang, T., Marisaldi, M., Grove, J. E., Quick, M., Christian, H., Schultz, C., Blakeslee, R., Adams, I., Bitzer, P., Fullekrug, M., Qureshi, B., Husa, B., and Heymsfield, G. and the additional members of ALOFT team: Glow-terminating terrestrial gamma-ray flashes observed during the ALOFT Campaign, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3628, https://doi.org/10.5194/egusphere-egu24-3628, 2024.

EGU24-3670 | ECS | Orals | NH1.5

Using meteorological reanalysis to identify weather conditions for classifying atmospheric electricity data  

Hripsime Mkrtchyan, Giles Harrison, and Keri Nicoll

Atmospheric electricity Potential Gradient (PG) data has typically been classified by local weather conditions, such as by identifying data recorded during “fair weather” (FW) or in the absence of rainfall “no hydrometeors” (NH), to try and obtain globally representative values. In general, this approach is essential in obtaining global atmospheric circuit (GEC) signals. The weather information needed to do this is, however, only available from some of the sites providing atmospheric electricity measurements. For other sites, meteorological reanalysis – of which there are many products available, spanning different times and scales - may provide a data source for such classification of PG data. This study investigates the integration of ERA5 meteorological reanalysis data to identify FW and NH conditions and improves the quality of data used in long-term atmospheric electricity studies.  

Initial findings investigating the meteorological quantities show a strong correlation between wind speed, total cloud coverage and total precipitation from ERA5 and observed ground-based measurements at the Eskdalemuir and Lerwick sites. This is to be applied to classifying past atmospheric electricity data, specifically of the hourly potential gradient (PG), which were obtained at the Lerwick observatory from 1925 to 1984, and Eskdalemuir observatory, which made atmospheric electricity measurements from 1911-1981 (Harrison & Riddick, 2022; Märcz & Harrison, 2003). 

Identified criteria from ERA5 which best match for FW and NH conditions are implemented in historical data from the Lerwick and Eskdalemuir observatories, enhancing the reliability of past studies which is important for atmospheric electricity analyses. This supports the potential of ERA5 data for providing information to identify FW and NH conditions. From this, we are evaluating a range of methods to use the meteorological reanalysis, with the aim of recovering representative FW data at sites lacking meteorological measurements. 

How to cite: Mkrtchyan, H., Harrison, G., and Nicoll, K.: Using meteorological reanalysis to identify weather conditions for classifying atmospheric electricity data , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3670, https://doi.org/10.5194/egusphere-egu24-3670, 2024.

EGU24-4116 | Orals | NH1.5

Stream Machine Learning for Lightning Nowcasting - Harnessing the Power of Continuously Updated Data 

Cesar Beneti, Luis Pavam, Luiz Oliveira, Marco Alves, Leonardo Calvetti, and Fernanda Verdelho

Uninterrupted access to electricity is a fundamental feature of civilization. In its absence, an all-embracing cessation of activities occurs, ranging from essential services to more frivolous activities. The maintenance of the energy supply is critical for society's day-to-day functions. The Brazilian state of Paraná (PR) is home to the world's second-largest hydropower plant, Itaipu, which, in conjunction with other power plants in the state, provides almost one-third of the power energy production in Brazil. The transmission lines that pervade PR are essential to Brazil's power distribution system, for hydropower generation is typically made far away from the regions that most demand it, being transported by transmission lines in an interconnected power grid. This type of asset mainly depends on the forecast of Cloud-to-Ground (CG) lightning, as it is one of the leading weather-related causes of power outages. Lightning and wind gusts are the two leading weather-related causes of disruptions, representing at least 23% of the known causes of energy disruption, as declared by the local power distribution company. Our study of lightning incidence and power outages from 2017-2021 indicates a correlation of 0.98 between these events, denoting that more outages must be lightning-related. Reliable CG lightning forecasts are crucial for proactive hazard mitigation. This work expounds on developing a Machine Learning (ML) model for CG lightning forecasting for PR. Our ML model predicts the occurrence or lack of CG lightning near power company assets in PR, defining a binary classification task. The model makes its predictions based on the past spatio-temporal conditions of lightning occurrences, requiring only past lightning data to forecast lightning. We chose to use a stream ML method, i.e., the model is continuously trained as new data arrives. Using a stream ML, we intend to harness the machine's capacity to continuously learn the patterns of lightning occurrence and power outages in real-time -- thus constructing an ever-updating model capable of adapting to transient weather conditions. Given its rapid training time and aptitude for classification tasks, the chosen algorithm was a Very Fast Decision Tree. The stream ML classifier outperforms a classic static ML model by 30% regarding the ROC AUC metric (stream: 71.80%, static: 40.85%) and 50% considering the Micro-f1 score (stream: 91.05%, static: 40.91%). These results arise from the highly dynamic nature of lightning, defining an ideal phenomenon for prediction based on a constantly updated stream of data. An automatic system for CG lightning forecasting for power company assets is helpful for risk management and operational planning. Future steps include increasing the lead time from ten min. to up to one hour, allowing for more time to prepare and anticipate hazards, preventing power outages, and optimizing personnel allocation.

How to cite: Beneti, C., Pavam, L., Oliveira, L., Alves, M., Calvetti, L., and Verdelho, F.: Stream Machine Learning for Lightning Nowcasting - Harnessing the Power of Continuously Updated Data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4116, https://doi.org/10.5194/egusphere-egu24-4116, 2024.

EGU24-4214 | Posters on site | NH1.5

Bottom-heavy charge structure and lightning discharges in Tibetan Plateau thunderstorms 

Xiushu Qie, Zhuling Sun, Fengquan Li, Lei Wei, Chunfa Sun, Kexin Zhu, Shanfneg Yuan, Dongxia Liu, and Rubin Jiang

The main charge region in thunderstorms over Lhasa city with an elevation of 3700 m is investigated by using a VHF interferometer, incorporating with fast antenna, weather radar and cloud-to-ground lightning location. The evolution of charge structure and its effects on lightning discharges were discussed in a bottom-heavy thunderstorm. During the early developing stage, the thunderstorm exhibited an inverted dipolar charge structure with negative charge center over the positive, and lower negative intracloud (IC) lightning occurred in between. Then an upper positive charge region appeared as the convection intensifying, and the charge structure exhibited obvious tripolar pattern and with large lower positive charge center (LPCC), and fewer positive IC discharges occurred in the upper dipole but lower negative IC lightning still dominated. As the thunderstorm entered the later mature stage, both negative IC between the lower dipole and positive IC between the upper dipole observed simultaneously. With gradually depleting of the positive charge carriers by precipitation, the LPCC weakened, the positive IC lightning between the upper dipole dominated, and two negative CG flashes were able to occur. In the later stage, positive IC dominated, although not much.  The study further confirms the previous conclusion (Qie et al., GRL, 2005) that weak thunderstorms are characterized by a bottom-heavy charge structure, and in the vigorous stage of thunderstorm, it may exhibit tripolar charge structure with a large LPCC, which has a significant impact on lightning types.

How to cite: Qie, X., Sun, Z., Li, F., Wei, L., Sun, C., Zhu, K., Yuan, S., Liu, D., and Jiang, R.: Bottom-heavy charge structure and lightning discharges in Tibetan Plateau thunderstorms, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4214, https://doi.org/10.5194/egusphere-egu24-4214, 2024.

The evolution of charge structure plays a crucial role in thunderstorm electrification. In this paper, the signatures related to the upper charge regions consisting of charged ice crystals are analyzed in an isolated thunderstorm, observed by an X-band dual-polarized phased array weather radar (DP-PAWR), which operates in its normal operational mode which performs a volume scan with 110 elevations at a temporal resolution of 30 seconds. The radar data quality control is applied to polarized parameters of DP-PAWR, including the horizontal reflectivity ZH, differential propagation phase shift, and specific differential phase. The lightning data was obtained by a lightning detection system called LIDEN (LIghtning DEtection Network system) operated by the JMA. A flash group algorithm is employed to group lightning discharges into flash branches according to a spatial range, azimuth interval, and time criterion.

 

To explore the mean structure of upper charge regions in the convective part of the thunderstorms, an expanded quasi-vertical profile method is applied to examine the temporal evolution of microphysical processes of upper charge regions. The convective part in the isolated thunderstorm is defined as one separated from nearby storms by an area of composite ZH larger than 40 dBZ at and above -10℃ layer, and a criterion of correlation coefficient ρHV greater than 0.8 is used to remove poor quality radar data. Meanwhile, only the lightning flashes within the given volume are used to calculate the IC lighting flash rate and explore the signatures with the upper charge regions.

 

The results indicate that during the different stages from the early developing stage of isolated thunderstorms to the end of the mature stage, the upper charge regions above the -10 ℃ layer experienced an evolution process from initiation to development accompanied by the rise of the charge region in the updraft and the enhancement of charge concentration. In the mature stage of thunderstorm, the upper charge regions extended from the -30℃ layer to the cloud top, followed by a decay process in the upper charge region at the end of the mature stage, in which the IC lightning flash rate is larger than 60 flashes/min. At the same time, the mean structure evolution of the upper charge regions exhibited a good relationship with the in-cloud lightning flash rate.

How to cite: Wang, S.: Analysis of the Signatures Related to the Upper Charge Regions in an Isolated Thunderstorm Observed by Dual-Polarized Phased Array Weather Radar, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4254, https://doi.org/10.5194/egusphere-egu24-4254, 2024.

Winter thunderstorms often exhibit compact vertical dimensions and lower heights of the major charge centers and are often accompanied by strong wind shear, with a propensity for positive cloud-to-ground strokes that can produce mesospheric transient luminous events (e.g. sprites, haloes, elves and jets). There are many optical observations confirming this over the Sea of Japan and the Mediterranean Sea, which are known to be the most convectively active regions during Northern Hemisphere winter.

We use a 3D quasi-electrostatic model (Haspel et al., 2022) with wintertime thunderstorm charge configurations to evaluate sprite inception regions in the mesosphere under various conditions typical of the Eastern Mediterranean. This is a is a relatively new, numerically robust model based on an analytical solution to Poisson’s equation that was developed specifically to handle non-symmetric charge configurations in a large 3D domain.  We address several key questions related to the onset of sprites in winter: (a) the minimum charge that enables sprite inception under the compact thunderstorm structures, (b) the effect of wind shear (lateral offsets of 3-5 km between the cloud charge centers) on the electric field and the location of the area of possible sprite inception, and (c) how the time difference between consecutive strokes in adjacent cumulonimbus clouds affects the size and location of the area of possible sprite inception. Additionally, we will present results of sensitivity studies on the discharge time and profile, showing how the area of possible sprite inception depends on this factor.

 

Reference

Haspel, C., G. Kurtser and Y. Yair (2022). The feasibility of a 3D time-dependent model for predicting the area of possible sprite inception in the mesosphere based on an analytical solution to Poisson's equation. Jour. Atmos. Sol. Terr. Phys.,230, 105853, doi:10.1016/j.jastp.2022.105853.

How to cite: Haspel, C. and Yair, Y.: Numerical simulations of the mesospheric region for sprite inception in winter thunderstorms over the Eastern Mediterranean, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4618, https://doi.org/10.5194/egusphere-egu24-4618, 2024.

EGU24-4634 | ECS | Orals | NH1.5

Regional differences in thunderstorm intensity driven by monsoon and westerlies over the Tibetan Plateau 

Lei Wei, Xiushu Qie, Zhuling Sun, and Chen Xu

Thunderstorms are weak but frequent, and exhibit unique charge structures over the Tibetan Plateau (TP) where the average elevation is higher than 4 km. In this study, all detected thunderstorms over the TP between 1998 and 2013 by TRMM were divided into four intensity categories: weak, median, severe and extreme. This classification was based on the 75%, 90%, and 99% values of flash rate, maximum 40 dBZ height, minimum 85 GHz polarization-corrected temperature (PCT), and minimum 37 GHz PCT, respectively. The monthly distributions of thunderstorm intensity show that all categories mostly occur in summer over most regions of the TP, and in spring near the Himalayas. Although the peaks of thunderstorms occur during 1300-1600 LT, the thunderstorms occurring in the early morning and evening have a high probability of developing into severe and extreme thunderstorms. This is distinct from the thunderstorms over the Sichuan Basin, the surrounding areas, and the middle and lower reaches of the Yangtze River at the same latitude. On the basis of westerlies- and monsoon-dominated regions, as well as the altitude, the TP was divided into four regions: the eastern, northern, southern and western regions of the TP (namely ETP, NTP, STP and WTP, respectively). The ETP and STP are primarily influenced by the monsoon, with the ETP at a lower altitude than the STP. Conversely, the WTP and NTP are affected by the westerlies, with the WTP situated at a higher altitude than the NTP. Thunderstorms over the ETP are more likely to be severe and extreme than those over the NTP. The percentage of weak thunderstorms is highest over the WTP. It is found that the maximum top height, development depth, horizontal development area, and development volume at 20 dBZ, 30 dBZ, and 40 dBZ echoes are largest over the ETP, followed by the NTP and STP, while being smallest over the WTP. The results imply that thunderstorms influenced by the monsoon are larger and more likely to be severe and extreme than those influenced by the westerlies.

How to cite: Wei, L., Qie, X., Sun, Z., and Xu, C.: Regional differences in thunderstorm intensity driven by monsoon and westerlies over the Tibetan Plateau, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4634, https://doi.org/10.5194/egusphere-egu24-4634, 2024.

EGU24-5346 | Posters on site | NH1.5

Spectral Analysis of High-Energy Radiation Events Observed during the ALOFT 2023 Campaign 

David Sarria, Nikolai Østgaard, Martino Marisaldi, Timothy Lang, Eric Grove, Mason Quick, Hugh Christian, Chris Schultz, Richard Blakeslee, Ian Adams, Rachael Kroodsma, Gerald Heymsfield, Andrey Mezentsev, Ingrid Bjørg Engeland, Anders Fuglestad, Nikolai Lehtinen, Kjetil Ullaland, Shiming Yang, Bilal Hasan Quresh, and Jens Søndergaard and the ALOFT Team

The Airborne Lighting Observatory for FEGS and TGFs (ALOFT) is equipped with a comprehensive set of instruments on-board a NASA ER-2 research aircraft for observing Terrestrial Gamma-ray Flashes (TGFs) and gamma-ray glows from thunderclouds. The ER-2 research aircraft flew at about 20 km altitude, above thunderstorms, from July 1st to July 30th, 2023, for a total flight time of about 60 hours.  The onboard instrument suite comprised several X/gamma-ray detectors, which spanned a dynamic range of four orders of magnitude in flux and covered the entire energy spectrum associated with the gamma-ray transients.

    During the campaign, we observed over 130 short gamma-ray transients, along with hundreds of gamma-ray glows. Several of these detections consisted of thousands of photon counts, allowing precise and unprecedented spectral analyses.

    In this study, we present a comprehensive spectral analysis of various events using a forward modeling technique and Monte-Carlo simulations. This approach enables us to constrain the source characteristics of these events, including their source energy spectrum, production altitude and offset, spatial extension, and the brightness (fluence) of the source RREA electrons.

How to cite: Sarria, D., Østgaard, N., Marisaldi, M., Lang, T., Grove, E., Quick, M., Christian, H., Schultz, C., Blakeslee, R., Adams, I., Kroodsma, R., Heymsfield, G., Mezentsev, A., Bjørg Engeland, I., Fuglestad, A., Lehtinen, N., Ullaland, K., Yang, S., Hasan Quresh, B., and Søndergaard, J. and the ALOFT Team: Spectral Analysis of High-Energy Radiation Events Observed during the ALOFT 2023 Campaign, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5346, https://doi.org/10.5194/egusphere-egu24-5346, 2024.

Lightning now has designated as an Essential Climate Variable in the Global Climate Observing System to understand the climate change. Lightning detection from geostationary satellites enables continuous monitoring of lightning activity. The satellite-borne lightning imagers take advantage of optical imaging technology combined with multiple filtering methods to extract the weak signals of lightning from very strong background signals and eventually clustering to reconstruct the original lightning flashes. By using the observation data of Fengyun-4A Lightning Mapper Imager (LMI), the first geostationary satellite-borne lightning imager developed in China, the lightning activity and the optical characteristics of lightning flashes in China were analyzed. The lightning activity observed by LMI exhibits obvious regional, seasonal and diurnal variation properties. The flashes are mainly concentrated in the southeastern coastal region in China and the southwestern China. During the pre-monsoon period (March-May), LMI detected lightning outbreaks in southwestern China and its surrounding areas, while during the monsoon period (June-September), both eastern southwestern China and southeastern coastal region in China show a significant dense distribution of lightning flashes. The climatic characteristics of lightning activity and the simultaneous observations of Lightning Imaging Sensor (LIS) on the International Space Station (ISS) confirm the LMI observations. However, there is a difference between the absolute amounts of the LMI and LIS observations. The overall number of lightning flashes observed by LMI is relatively lower than that observed by LIS. In addition, the detection capability of LMI is higher at low latitudes compared to mid-latitudes, and is higher during daytime hours than that during nighttime hours. As for the flash properties, which mainly refer to the optical radiance, area, and duration of lightning flashes, there are also regional differences for these properties observed by LMI. The high values of flash properties are concentrated in southern China. The LMI observations are related to the radiometric response of its detector and the difference in spatial resolution within the large field of view of geostationary orbit observations.

How to cite: Hui, W. and Zhang, W.: Lightning Activity in China and Its Optical Characteristics Observed by Geostationary Satellite, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5363, https://doi.org/10.5194/egusphere-egu24-5363, 2024.

EGU24-5400 | ECS | Posters on site | NH1.5

The intensity distribution of Terrestrial Gamma-ray Flashes from the ALOFT flight campaign 

Anders Fuglestad and the ALOFT team

In July 2023, the Airborne Lightning Observatory for FEGS and TGFs (ALOFT) flight campaign took place using a NASA ER-2 research aircraft flying over the Gulf of Mexico and the Caribbean Sea. The campaign consisted of about 60 flight hours at a cruise altitude of 20 km, using live telemetry to target gamma-ray glowing thunderclouds.

The payload consisted of several instruments including gamma-ray detectors with a dynamic range spanning four orders of magnitude in flux, an imaging array of optical photometers, electric field change meters, radiometers, and radar systems. In addition to several ground stations measuring very low frequency, low frequency, and very high frequency radio signals.

96 TGFs were detected by ALOFT. For 44 of these events, it was possible to get an estimate of the location of the source using both correlated optical pulses and lightning detection networks.

With the estimate of the source location and the gamma-ray observation from ALOFT. Monte Carlo simulations were used to get an estimate of the source intensity of the TGFs.

Based on the results it was determined that the vast majority of the 44 TGFs investigated have source intensities below the threshold needed to be observed from current satellite instruments, which indicates a large population of low intensity TGFs that has gone previously undetected. These results contribute to the open debate on the rarity of TGFs.

How to cite: Fuglestad, A. and the ALOFT team: The intensity distribution of Terrestrial Gamma-ray Flashes from the ALOFT flight campaign, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5400, https://doi.org/10.5194/egusphere-egu24-5400, 2024.

EGU24-6398 | ECS | Orals | NH1.5

Side discharges on positively charged lightning leaders 

Shanfeng Yuan, Xiushu Qie, Rubin Jiang, and Dongfang Wang

Recent observations unveiled two types of side discharges associated with positive leaders: needle discharges and nearby bidirectional leaders. The formation mechanism and connections of two phenomena remained unclear due to the lack of synchronous optical detection and radio mapping data. Here we present the first high-speed video and low-frequency lightning mapping results. Negative branches of nearby bidirectional leaders can propagate after connecting to the parent positive channel, and needle discharges act as positive connecting leaders. Our research shows that positive leaders exhibit unconventional channel extensions, maintained by frequent recoil leaders, sharing characteristics with streamer discharges. Notably, when two approaching positive leaders develop in this manner, they can eventually collide. These findings significantly advance our understanding of side discharges on positive leaders, offering fresh insights into these intriguing phenomena.

How to cite: Yuan, S., Qie, X., Jiang, R., and Wang, D.: Side discharges on positively charged lightning leaders, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6398, https://doi.org/10.5194/egusphere-egu24-6398, 2024.

EGU24-6468 | Posters on site | NH1.5

On the impact of thunder on cloud droplets and ice crystals  

Konstantinos Kourtidis and Stavros Stathopoulos

In the lightning channel pressures can be of the order of 100 atm and hence in the produced thunder, sound pressure levels (SPL) can be very high. Additionally, the thunder frequency spectra have peaks for peal and claps at around 100 Hz and around 50 Hz for rumble sounds, with intracloud lightning having peaks at even fewer Hz. These low frequencies are ideal for acoustically induced orthokinetic agglomeration of droplets. Thunder occurs in cloud environments where not only large numbers of droplets are present, but additionally the shockwave front expands at supersonic velocities and hence could cause near the lightning channel modulations of droplet size distributions and increase ice crystals numbers through e.g. vibrational breakup. We present calculations for the two mechanisms above (orthokinetic agglomeration and vibrational breakup) for typical cloud droplet sizes and concentrations, including also clouds containing desert dust. In thunderstorm conditions, it is found that acoustic orthokinetic agglomeration of droplets can be very effective and can produce very rapidly changes in the mean cloud droplet diameter. Also, it is found that the critical flow velocities, over which breakup occurs, is easily exceeded near the lightning channel and will lead to droplet and ice crystal breakup. We note that all models of ice crystal generation in clouds substantially underestimate the observed ice crystal numbers, and the mechanism presented here may be responsible for the discrepancy. We also note that these processes need further study to assess how they could interfere with the lightning generation process itself, through both charge redistribution in the modified droplet size distribution spectra, as well as the increase in vertical and turbulent transport velocities of the smaller ice crystals resulting from breakup. 

How to cite: Kourtidis, K. and Stathopoulos, S.: On the impact of thunder on cloud droplets and ice crystals , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6468, https://doi.org/10.5194/egusphere-egu24-6468, 2024.

EGU24-6523 | Posters on site | NH1.5

ESTHER: a small project to investigate gamma-ray emissions in thunderstorms and volcanic lightning 

Alessandro Ursi and Danilo Reitano

Detecting terrestrial gamma-ray flashes (TGFs) from the ground is a relatively new frontier in atmospheric science and has opened up new avenues for research. Also, the recent detection of a TGF produced during the massive Hunga Tonga–Hunga Ha'apai eruption, pointed out the possibility that even volcanic lightning might produce gamma-ray emissions at MeV energies.

In this context, we present the Experiment to Study Thunderstorm High-Energy Radiation (ESTHER), a small project funded by the Italian National Institute for Astrophysics (INAF), aimed at monitoring from the ground gamma-ray emissions produced during thunderstorms and, possibly, by volcanic lightning. The ESTHER set-up consists of a gamma-ray detection system and a VLF radio receiver, to be installed on the top of the Etna volcano (Italy). The selected installation site is the Etnean Observatory of the Italian National Institute of Geophysics and Volcanology (INGV), located at 2,818 m altitude and laying less than 2.7 km from the main volcano craters.

An extensive analysis of the flash rate recorded at Mt. Etna in the last eight years pointed out that the mountain top is interested by strong lightning activity in the summer months, making it a suitable location for the investigation of lightning and associated high-energy phenomena. In particular, the largest fraction of discharges turned out to cluster nearby the mountain peak and right above the main volcano craters, where the frequent presence of volcanic ashes possibly increases the electrical conductivity, under conditions of humid air typical of thunderstorms, making the region above the volcano's top a natural trigger for lightning. Moreover, as for other volcanoes around the world, Etna has been documented to produce volcanic lightning (last times in 2015 and 2022). As a consequence, given the proximity of the Etnean Observatory to the main craters, ESTHER will enjoy a privileged location for investigating potential gamma-ray emissions produced either by thunderstorms and volcanic lightning. In conditions of clear sky, ESTHER will also provide an as much as possible continuous monitoring of the environmental gamma-ray background, allowing to point out potential variations of it before, during, or after volcanic eruptions. The ESTHER set-up will be installed and start its first data acquisitions in spring 2024.

How to cite: Ursi, A. and Reitano, D.: ESTHER: a small project to investigate gamma-ray emissions in thunderstorms and volcanic lightning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6523, https://doi.org/10.5194/egusphere-egu24-6523, 2024.

EGU24-7273 | Orals | NH1.5

Observation of positive Narrow Bipolar Events in the Mediterranean region 

Ivana Kolmašová, Ondřej Santolík, Serge Soula, Eric Defer, Yanan Zhu, Radek Lán, Stéphane Pedeboy, and Andrea Kolínská

Narrow Bipolar Events (NBEs) are brief intracloud (IC) discharge processes that generate powerful radiation in the HF and VHF radio bands. NBEs typically occur in isolation, but they have also been identified as initial events in IC lightning flashes. Their incidence is statistically correlated with the strength of convection. NBEs can exhibit both polarities and usually occur in the upper regions of the thundercloud.

We present, for the first time, properties of NBEs detected in the Mediterranean region. The dataset comprises 37 events recorded by broadband magnetic loops located at two sites in France. The events were identified using the list of NBEs from 2022 provided by the Earth Network. The frequency range of our broadband sensors enabled us to obtain detailed shapes of NBE pulses. We calculated rise times, full width at half maximum times, and zero-crossing times of NBE pulses to facilitate comparisons with observations of NBEs in other parts of the world. The majority of NBE pulses observed in the Mediterranean region were isolated events occurring above the land and displaying a simple bipolar waveform with an overshoot peak of the opposite polarity. For two events, we supplemented our observation with the data from the SAETTA (Suivi de l’Activité Electrique Tridimensionnelle Totale de l’Atmosphère) lightning mapping array. Additionally, we estimated the altitude of the NBE events and placed our observations in the meteorological contexts to determine why NBE occurrences in the Mediterranean region have been overlooked until now.

 

How to cite: Kolmašová, I., Santolík, O., Soula, S., Defer, E., Zhu, Y., Lán, R., Pedeboy, S., and Kolínská, A.: Observation of positive Narrow Bipolar Events in the Mediterranean region, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7273, https://doi.org/10.5194/egusphere-egu24-7273, 2024.

EGU24-7900 | Orals | NH1.5

TGF and gamma-ray glow highlights from the ALOFT 2023 flight campaign 

Nikolai Ostgaard, Timothy Lang, Martino Marisaldi, Eric Grove, Mason Quick, Hugh Christian, Cristopher Schultz, Richard Blakeslee, Ian Adams, Rachael Kroodsma, Gerald Heymsfield, Andrey Mezentsev, David Sarria, Ingrid Bjorg Engeland, Anders Fuglestad, Nikolai Lehtinen, Kjetil Ullaland, Shiming Yang, Bilal Hasan Qureshi, and Jens Sondergaard and the ALOFT team

During the summer of 2023 the  Airborne Lighting Observatory for FEGS and TGFs (ALOFT) field campaign was performed. With a NASA ER-2 research aircraft, flying at 20 km altitude, ALOFT was searching for Terrestrial Gamma ray Flashes (TGF) and gamma-glowing thunderclouds in Central America and Caribbean. The ALOFT payload included a comprehensive number of instruments:

1) Several gamma-ray detectors covering four orders of magnitude dynamic range in flux as well as the full energy range for TGF/gamma-ray glow detection (UIB-BGO and ISTORM).

2) Fly’s Eye GLM Simulator (FEGS), an imaging array of photometers sensitive to different wavelengths, and electric field change meters.

3) Lightning Instrument Package (LIP), giving three component electric field measurements.

4) a suite of microwave radiometers and radars for cloud characterization: the Advanced Microwave Precipitation Radiometer (AMPR), Configurable Scanning Submillimeter-wave Instrument/Radiometer (CoSSIR), Cloud Radar System (CRS), and X-band Radar (EXRAD)

 

5) An extensive set of ground-based radio observations.

 

For all the 10 flights, 60 hours total, realtime gamma-ray detections were downlinked. Due to this simple but novel mission concept, we knew in real time if the aircraft was passing a gamma-glowing cloud and the pilot was instructed to return to the same thundercloud as long as the cloud was glowing. During the campaign ALOFT observed a total of 130 transient gamma-ray events and hundreds of gamma-ray glows. With the richness of the ALOFT observations we learned that thundercloud can glow for much longer than minute scale and over much larger areas than previously reported. We also learned that transient gamma-ray events come in a large variety and new types of events were discovered.  In this presentation we will give an overview of the main results and discoveries by the ALOFT campaign

 

How to cite: Ostgaard, N., Lang, T., Marisaldi, M., Grove, E., Quick, M., Christian, H., Schultz, C., Blakeslee, R., Adams, I., Kroodsma, R., Heymsfield, G., Mezentsev, A., Sarria, D., Bjorg Engeland, I., Fuglestad, A., Lehtinen, N., Ullaland, K., Yang, S., Hasan Qureshi, B., and Sondergaard, J. and the ALOFT team: TGF and gamma-ray glow highlights from the ALOFT 2023 flight campaign, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7900, https://doi.org/10.5194/egusphere-egu24-7900, 2024.

EGU24-7927 | Orals | NH1.5

A novel view of gamma-ray glows from the ALOFT 2023 flight campaign 

Martino Marisaldi, Nikolai Østgaard, Timothy J. Lang, J. Eric Grove, Mason Quick, Hugh Christian, Christopher J. Schultz, Richard Blakeslee, Ian S. Adams, Rachael A. Kroodsma, Gerald M. Heymsfield, Andrey Mezentsev, David Sarria, Ingrid Bjørge-Engeland, Anders Fuglestad, Nikolai Lehtinen, Kjetil Ullaland, Shiming Yang, Bilal Hasan Qureshi, and Jens Søndergaard and the ALOFT team

The Airborne Lightning Observatory for FEGS and TGFs (ALOFT) was a field campaign targeted at Terrestrial Gamma-ray Flashes (TGFs) and gamma-ray glows from thunderclouds. The campaign was successfully carried out during July 2023, for a total of 60 flight hours in the Gulf of Mexico and the Caribbean. The scientific payload was flown on a NASA ER-2 research aircraft, capable to fly at 20 km altitude above thunderclouds. The payload included a suite of gamma-ray detectors spanning four orders of magnitude dynamic range in flux, and a complete suite of instruments for the characterisation of the electrical and optical activity, and the thundercloud environment. A key asset of the mission was the real-time downlink of gamma-ray count rates, which enabled the immediate identification of gamma-ray glowing regions. The pilot was then instructed to turn and pass over the same glowing region to explore its spatial extension and duration.

ALOFT resulted in the detection of hundreds of gamma-ray glows, anticipating a revolution in our understanding of the phenomenon. Thunderclouds were observed to glow for hours and over several thousands of square kilometers, making glows a much more pervasive phenomenon than previously reported. Glows show significant time variability from seconds down to millisecond time scale, suggesting a relation to short transients such as TGFs more complex than previously thought. Glows are observed in association with the overpass of active convective cores, 20-25 km in size, yet their time variability and intensity modulation suggest a more complex spatial structure.

These observations challenge the current view of glows as quasi-stationary phenomena related to relatively stable electrification conditions. The observed glows show highly dynamic temporal and spatial structures and are closely related to the development phases of active thunderclouds. These observations call for a rethinking of the assumptions at the basis of current modeling efforts.

How to cite: Marisaldi, M., Østgaard, N., Lang, T. J., Grove, J. E., Quick, M., Christian, H., Schultz, C. J., Blakeslee, R., Adams, I. S., Kroodsma, R. A., Heymsfield, G. M., Mezentsev, A., Sarria, D., Bjørge-Engeland, I., Fuglestad, A., Lehtinen, N., Ullaland, K., Yang, S., Qureshi, B. H., and Søndergaard, J. and the ALOFT team: A novel view of gamma-ray glows from the ALOFT 2023 flight campaign, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7927, https://doi.org/10.5194/egusphere-egu24-7927, 2024.

EGU24-7940 | Orals | NH1.5

LOFAR Observations of the Initial Stage of IC Dart Leaders 

Brian Hare, Olaf Scholten, Paulina Ťureková, Steven Cummer, Joseph Dwyer, Ningyu Liu, Chris Sterpka, and Sander ter Veen

In previous work we have found that dart leaders quench needle activity; where dart leaders are charge pulses that re-trace previously established lightning leader channels, and needles are small repeating negative discharges that propagate away from positive lightning channels. We hypothesized that dart leaders could be quenching needles by carrying negative charge away from the region of needle activity. Therefore, in order to further explore the interactions between dart leaders and needles, we are investigating the beginnings of different dart leaders with the LOFAR radio telescope, which uses hundreds of antennas in northern Netherlands to image lightning in the 30-80 MHz band with meter and nanosecond level accuracy. We have found that, consistent with previous work, dart leaders start slow with weak radio emission and then accelerate over a period roughly around 50 µs in duration until they reach a maximum speed and radio intensity. However, we also observe that the power of the radio emissions from the dart leaders exhibits large, randomly-timed, variations. These variations do not appear to be a form of leader stepping. The time-differences between individual peaks in the time trace is significantly longer than the width of each peak (or pulse) that is dominated by the antenna function, (FWHM ~ 50 ns). One possible explanation could be that the power fluctuations are consistent with Poisson statistical variations of radio sources (possibly streamers), which would imply that at any point in time the radio emission is dominated by a small number of strong emitters, as opposed to millions of small streamers. A second possible explanation is that the fluctuations could be due to small-scale structural variations along the previously established plasma channel, which we have observed in previous work.

How to cite: Hare, B., Scholten, O., Ťureková, P., Cummer, S., Dwyer, J., Liu, N., Sterpka, C., and ter Veen, S.: LOFAR Observations of the Initial Stage of IC Dart Leaders, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7940, https://doi.org/10.5194/egusphere-egu24-7940, 2024.

EGU24-7982 | ECS | Posters virtual | NH1.5

Enhancement of Catastrophic Positive Cloud to Ground Lightning in recent years over Maharashtra (India): Role of Dust Aerosols 

Abhijeet Gangane, Sunil Pawar, Prajna Priyadarshini, and Venkatachalam Gopalakrishnan

Many studies have shown that aerosols can influence microphysical processes inside thunderclouds that could affect charge-generation processes. Cloud to Ground (CG) lightning data from Ground-based observations (IITM-LLN) over the State of Maharashtra, India, from 2014 to 2023, have been analyzed here to study the percentage and physical mechanism associated with the enhancement of catastrophic Positive CG in total CG lightning. Our analysis shows that the average positive CG percentage remains above 25% during the monsoon (July-September) and post-monsoon (October-November). This increased percentage of positive CG is attributed to elevated dust aerosol concentration over the study region during the monsoon and post-monsoon periods. An enormous amount of dust can be seen during the Indian Summer Monsoon (ISM) over the Arabian Desert and neighborhood extending up to the western Indian (Maharashtra) region. Dust aerosol intrusion into the thunderstorm acts as Ice nuclei (IN) as well as Cloud Condensation Nuclei (CCN) and can influence charge separation processes inside the cloud. In recent years, we observed an enhancement of Dust AOT over Maharashtra state, indicating that the increasing trend in Positive CG lightning is closely linked to the transport of desert dust from the Middle East and elevated aerosol content during the post-monsoon season. Here, we propose that these high concentrations of dust aerosols near the cloud base acting as IN produce a high concentration of ice crystals in the lower portion of the cloud, which can form a strong positive charge region in the lower part of the mixed-phase region by non-inductive charging mechanism. This strong positive charge region in the lower portion of the mixed phase region may be responsible for the observed increased percentage of positive CG over the study region.

How to cite: Gangane, A., Pawar, S., Priyadarshini, P., and Gopalakrishnan, V.: Enhancement of Catastrophic Positive Cloud to Ground Lightning in recent years over Maharashtra (India): Role of Dust Aerosols, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7982, https://doi.org/10.5194/egusphere-egu24-7982, 2024.

EGU24-7996 | ECS | Posters on site | NH1.5

Monte Carlo Error Analysis of Lightning Interferometry with LOFAR 

Paulina Turekova, Brian Hare, Olaf Scholten, Steven Cummer, Joseph Dwyer, Ningyu Liu, Chris Sterpka, and Sander ter Veen
The LOFAR radio telescope works on a principle of radio interferometric imaging. It coherently sums the signal of hundreds of antennas in northern Netherlands, covering the 30-80 MHz window of the very high frequency (VHF) band of 30-300 MHz. We are using the TRI-D algorithm to extract 3-D polarization data of a lightning flash observed by LOFAR. TRI-D functions by coherently summing recorded voltages, accounting for the antenna function, polarization, and geometric time delay for each voxel. The result is split into time slices. A coherent intensity is calculated for each time slice, and the maximum of this value is set as a source location. The outcome is a reconstructed source location and polarization as seen by the LOFAR antennas. We are now exploring the accuracy of TRI-D in response to realistic parameters. In this work, we perform a Monte Carlo error analysis which simulates the voltages on each antenna from an assumed dipole emitter, adds normally distributed noise, and then reconstructs the source properties with TRI-D. The difference between the simulated input and the reconstruction gives us an estimate of the resulting error bars. We will show a detailed account of the interferometry technique that produces our data, the Monte Carlo simulation that tests the accuracy of our model and finally, our polarization results.

How to cite: Turekova, P., Hare, B., Scholten, O., Cummer, S., Dwyer, J., Liu, N., Sterpka, C., and ter Veen, S.: Monte Carlo Error Analysis of Lightning Interferometry with LOFAR, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7996, https://doi.org/10.5194/egusphere-egu24-7996, 2024.

EGU24-8002 | ECS | Posters on site | NH1.5

Measuring evaporation-condensation charging of individual aerosol particles 

Andrea Stoellner, Isaac Christopher David Lenton, Caroline Muller, and Scott Russell Waitukaitis

Although cloud electrification has been studied for hundreds of years, it is still not fully understood [1]. The most promising charging mechanism – ice crystal-graupel collision charging – answers some of our questions, but leaves us with others. Why do ice crystals and graupel charge on collision in the first place? And why do they reverse their charging behavior below a certain temperature? To get some insights we take a step back and look at the charging behavior of individual aerosol particles in a humid environment. Shavlov et al. [2] suggest that the hydroxide and hydronium ions formed by the autodissociation of water are sufficient to cause charging during evaporation and condensation of water droplets or surface-adsorbed water on solid particles. This small amount of charge could be a precursor to bigger charge exchange during collision.

            We aim to test this hypothesis by levitating individual aerosol particles in an optical trap and measuring their charge while varying humidity. Our setup allows for trapping of different types of solid and liquid particles in the micrometer size range, like water droplets and silica microspheres. In the future we also hope to study ice crystals. Figure 1 shows an illustration of the measurement principle. The particle’s charge is measured by applying a sinusoidal electric field and observing the resulting particle motion. The Mie scattering pattern of the particle furthermore gives information about the particle’s size and refractive index, both at equilibrium and during evaporation/condensation. The experiment allows us to control the relative humidity, pressure and air ion concentration around as well as air flow across the particle.

Ultimately we hope to contribute to a better understanding of the microphysical processes involved in thundercloud electrification and adjacent electrical phenomena in the atmosphere. 

FIGURE 1. Optical tweezers (wavelength λ = 532 nm) holding a solid or liquid aerosol particle. A sinusoidal electric field is applied between the two electrodes and the resulting particle motion as well as the particle’s Mie scattering pattern are recorded.

Acknowledgments

This project has received funding from the European Research Council (ERC) under the European Union’s Starting Grant (A. Stoellner, I.C.D. Lenton & S.R. Waitukaitis received funding from ERC No. 949120, C. Muller received funding from ERC No. 805041).

 

References

  • Berdeklis, P. and List, R. (2001) J Aerosol Sci. 58(18) 2751–2770.
  • Shavlov A. et al. (2018) J Aerosol Sci. 123 17-26.

How to cite: Stoellner, A., Lenton, I. C. D., Muller, C., and Waitukaitis, S. R.: Measuring evaporation-condensation charging of individual aerosol particles, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8002, https://doi.org/10.5194/egusphere-egu24-8002, 2024.

EGU24-9355 | Posters on site | NH1.5

Dynamics of global lightning activity on different time scales as indicated by Schumann resonance frequency variations 

Gabriella Sátori, Tamás Bozóki, Earle Williams, Ernő Prácser, Raidiel Puig, and Rachel Albrecht

The electromagnetic waves in the Schumann resonance (SR) frequency range (<100 Hz) radiated by natural “lightning antennas” excite the Earth-ionosphere cavity confined between the Earth’s surface and the ionospheric D-region of ~100 km height. This contribution provides observational evidence for the relationships between the variations of peak frequencies of the first three modes and the global/regional lightning dynamics based on SR observations of the vertical electric field component, EZ, at Nagycenk (NCK), Hungary, Central Europe. Lightning source-observer distance-dependent frequency variations are considered on the annual, seasonal and diurnal time scale as well as during specific events when squall-line formation of lightning activity in South America moves toward NCK. The observations are interpreted with model calculations. The distance-dependent frequency variation has important applications to climate issues as well.

How to cite: Sátori, G., Bozóki, T., Williams, E., Prácser, E., Puig, R., and Albrecht, R.: Dynamics of global lightning activity on different time scales as indicated by Schumann resonance frequency variations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9355, https://doi.org/10.5194/egusphere-egu24-9355, 2024.

EGU24-9526 | Orals | NH1.5

On the radio wave polarization of Saturn lightning 

Georg Fischer, Ulrich Taubenschuss, David Pisa, and Masafumi Imai

The radio waves with Saturn lightning origin have been studied since the first detection by Voyager 1, but their wave polarization has rarely been explored. Fischer et al. (2007, JGR 112, A12308) examined lightning from a storm located at 35° south latitude and found its radio emissions below 2 MHz to be highly polarized (80%) in a right-handed circular sense with respect to the wave propagation direction. They explained this by absorption of the extraordinary mode in Saturn's ionosphere and the dominance of the ordinary mode emission, as the radio waves are propagating against a direction of the magnetic field when coming from a source in the southern hemisphere. A limited examination of Saturn lightning from the so-called Great White Spot at 35° north latitude by Fischer et al. (2011, Nature 475, 75-77) revealed radio wave polarization in the left-handed sense. In this presentation we will show the radio wave polarization of lightning from various other storms in Saturn's atmosphere, which have not been examined until today. In this way we want to corroborate the hypothesis that the sense of the circular radio wave polarization of Saturn lightning depends on the hemispherical location of the storm.

How to cite: Fischer, G., Taubenschuss, U., Pisa, D., and Imai, M.: On the radio wave polarization of Saturn lightning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9526, https://doi.org/10.5194/egusphere-egu24-9526, 2024.

EGU24-9986 | ECS | Orals | NH1.5 | Highlight

Potential gradient as a predictor of fog 

Caleb Miller, Keri Nicoll, Chris Westbrook, and R. Giles Harrison

Although fog is an important weather phenomenon, it remains difficult to predict using traditional methods. This could be improved by new observations-based nowcasting systems. It has long been understood that fog affects measurements of atmospheric electricity. However, there has been disagreement in the literature on whether these changes contain information which is valuable for fog prediction beyond other commonly used methods. Here, results are presented which show that the potential gradient (PG), a measure of atmospheric electricity, could be used as an additional diagnostic in predicting fog for timescales of several hours. A much larger dataset of fog and PG is examined than has been previously possible, which allows for a more robust understanding of the behaviour of the PG during radiation fog. It is found to increase by a median of 58 V/m by the start of the event. In addition, this increase is found to begin over two hours in advance of the fog, 30% of the time. This shows that PG may contain useful fog nowcasting information. A number of individual fog case studies are presented and the applicability of the general results to these specific cases is discussed. 

How to cite: Miller, C., Nicoll, K., Westbrook, C., and Harrison, R. G.: Potential gradient as a predictor of fog, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9986, https://doi.org/10.5194/egusphere-egu24-9986, 2024.

EGU24-10352 | Orals | NH1.5

Cloud Microphysical Characteristics Associated with Blue Corona Discharges at thundercloud tops 

Dongshuai Li, Alejandro Luque, Torsten Neubert, Olivier Chanrion, Yanan Zhu, Jeff Lapierre, Nikolai Østgaard, and Víctor Reglero

Blue corona discharges are bursts of streamer discharges often observed at the top of thunderclouds, but the conditions in the clouds that generate them are not well understood.

The cloud microphysical parameters related to them are important for future empirical studies and for theoretical models and simulations. Previous studies modeled the scattering and absorption emissions from blue corona discharges by assuming mean particle radius of 10–20 μm and densities of 1–2.5 × 10^8 m^−3, resulting in photon mean free paths of 1–20 m.

Here we present the first-ever estimate of important microphysical parameters related to blue corona discharges based on data measurements from the CALIPSO lidar. The results showed that most blue corona discharges were associated with ice particles with a radius of ∼50 μm and a number density of ∼ 2 × 10^7 m^−3, resulting in a photon mean free path of ∼3 m.

Around 20% of the blue corona discharges coincide with Narrow Bipolar Events (NBEs) indentified from the Earth Networks Total Lightning Network.The altitudes of blue corona discharges that were identified as NBEs are derived from both the optical and radio bands. It revealed that in six out of nine cases, the R^2 value was greater than 0.85, indicating a good agreement between the two methods and supporting our estimate of the photon mean free path as 3 m. However, in the shallowest and deepest cases, there was some discrepancy between the altitudes determined by the two methods, suggesting more complex cloud microphysical parameters. Possible reasons for the discrepancy, such as the homogeneous approximation for the cloud's microphysical parameters and the simplification of the source length, will be discussed.

How to cite: Li, D., Luque, A., Neubert, T., Chanrion, O., Zhu, Y., Lapierre, J., Østgaard, N., and Reglero, V.: Cloud Microphysical Characteristics Associated with Blue Corona Discharges at thundercloud tops, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10352, https://doi.org/10.5194/egusphere-egu24-10352, 2024.

EGU24-11101 | Orals | NH1.5

Mapping out lightning processes in both the VHF and VLF using LOFAR and the Met Office’s lightning detection system, LEELA 

Graeme Marlton, Brian Hare, Olaf Scholten, Mike Protts, Ed Stone, Sue Twelves, and Francesco Devoto

Lightning is one of the most destructive meteorological phenomena being a hazard to people and objects on the ground as well as aircraft. In addition to the strong currents and optical emission from a lightning stroke broadband radio emissions are also produced from the VLF to VHF. The LOw Frequency ARray (LOFAR) telescope centred in the Netherlands consists of a large array of VHF (30-300 MHz) receivers which can be configured to image a lightning strike in the 30-80 MHz bandwidth. The Met Office Lightning Electromagnetic Emission Location using Arrival time differencing LEELA system operates in the VLF (3-30 kHz). It also archives the raw incoming VLF data allowing the individual VLF waveforms to be analysed. From a lightning flash recorded in June 2021 over the Netherlands, 8 distinct events were detected by both systems. Here we present an analysis of these 8 events which include dart leaders, negative leaders, an intensely radiating negative leader and a cloud to ground strike. Initial results show that while both systems co-locate the events they are sensitive to different processes within the lightning strike process. VHF emission from a lightning strike is observed for periods of 30-40 ms and captures the development of the lightning channel. However, VLF emission is observed for much shorter periods of a few ms likely corresponding to the rapid vertical movement of charge during the strikes.

How to cite: Marlton, G., Hare, B., Scholten, O., Protts, M., Stone, E., Twelves, S., and Devoto, F.: Mapping out lightning processes in both the VHF and VLF using LOFAR and the Met Office’s lightning detection system, LEELA, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11101, https://doi.org/10.5194/egusphere-egu24-11101, 2024.

EGU24-11257 | Orals | NH1.5

Locating charged regions in extensive layer cloud 

R.Giles Harrison and Keri Nicoll

Extensive layer clouds are common in Earth’s atmosphere. They acquire charge at their upper and lower boundaries, from the vertical current flowing in the global atmospheric electric circuit. The quantity of charge collected is related to the current, the transition distance from clear air to cloudy air at the cloud boundary, and the background cosmic ray ionisation. The transition distance is the region in which a change in conductivity occurs, which determines the charge acquisition. This differs between cloud top and cloud base. At cloud top, the boundary transition distance is closely related to the temperature inversion, which can be less than the transition distance at cloud base. At cloud base, the transition distance depends on droplet growth rate and updraft speed. The combined effects of the local ionisation, current flow and conductivity gradient leads to droplet charging.

Using instrumentation carried on enhanced meteorological radiosondes, the extent of the charged region in extensive layer clouds has been observed with specially developed cloud sensors operating at multiple optical wavelengths, simultaneously with the in situ electrical measurements. (Further, in some situations, ceilometer measurements of backscatter are also available). These soundings are compared with modelled profiles of droplet properties and layer cloud charges, for situations characteristic of mid-latitude and polar clouds. Effects of the droplet size distribution on the layer cloud electrification are also investigated, and responses to variations in cosmic ray ion production.

Charging is known to affect some aspects of the microphysical behaviour of droplets, such as their evaporation and growth rates. This may in turn influence properties of layer clouds in the climate system.

How to cite: Harrison, R. G. and Nicoll, K.: Locating charged regions in extensive layer cloud, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11257, https://doi.org/10.5194/egusphere-egu24-11257, 2024.

EGU24-11482 | Orals | NH1.5 | Highlight

Thundercloud high-energy radiation production by long streamers 

Nikolai Lehtinen, David Sarria, Martino Marisaldi, Andrey Mezentsev, Nikolai Østgaard, Steven Cummer, and Yunjiao Pu

The novel Streamer Parameter Model (SPM) [Lehtinen, 2021, doi:10.1007/s11141-021-10108-5] allows to quickly calculate the shape, velocity, and electric field of an electric streamer in air, without resorting to lengthy hydrodynamic simulations. A streamer propagates faster as its length grows. When the streamer length exceeds several meters, the velocity may become comparable to the speed of light, which necessitates correcting the model for relativistic effects. Such long streamers may describe the experimentally observed fast positive and negative breakdown. We propose that they may produce large quantities of relativistic runaway electrons, and therefore x-rays. This is facilitated by several conditions: (1) electric fields at the streamer tip may be sufficiently close to the so-called thermal runaway threshold (~30 MV/m), at which free electrons may accelerate from thermal energies up to relativistic energies; (2) in negative streamers, the energetic electrons are synchronized in velocity with the streamer front; (3) the streamer tip radius may exceed tens of centimeters, providing a large volume of the high field where the thermal runaway acceleration may take place.

We apply SPM to long streamer propagation inside a thundercloud and calculate the relativistic runaway electron production, as well as radio, optical and x-ray radiation. The calculations are compared to the observations of Narrow Bipolar Events (NBE), Terrestrial Gamma Flashes (TGF), and luminous phenomena obtained during the recent ALOFT campaign.

How to cite: Lehtinen, N., Sarria, D., Marisaldi, M., Mezentsev, A., Østgaard, N., Cummer, S., and Pu, Y.: Thundercloud high-energy radiation production by long streamers, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11482, https://doi.org/10.5194/egusphere-egu24-11482, 2024.

EGU24-11937 | Orals | NH1.5

Feedback Effects in Positive Corona and Relativistic Runaway Discharges 

Victor Pasko, Sebastien Celestin, Anne Bourdon, Reza Janalizadeh, and Jaroslav Jansky

We discuss characteristic scales and direct physical analogy between the photoionization feedback in conventional positive corona discharges in air and the photoelectric feedback in discharges driven by relativistic runaway electrons in air. In a positive corona system the avalanche of electrons in bulk of discharge volume is initiated by specific distribution of photoionization far away from the electrode.  Under inception conditions in positive corona each electron arriving at the anode creates on average just enough seed electrons in discharge volume through photoionization to replicate itself. Under these self-sustained steady state conditions, photoionization feedback produces just enough secondary electrons upstream of the avalanche to maintain the system in steady state. Analogically, in case of relativistic electron avalanches a feedback process is realized when X-rays emitted by these electrons travel backwards with respect to the electron motion and generate new relativistic electron seeds due to the photoelectric absorption in air. It is demonstrated that terrestrial gamma-ray flashes are produced by growth of long bidirectional lightning leader system consisting of positive and stepping negative leaders. The spatial extent of streamer zones of a typical lightning leader with tip potential exceeding several tens of megavolts is on the order of 10–100 m. The photoelectric absorption of bremsstrahlung radiation generated by avalanching relativistic runaway electrons occurs efficiently on the same spatial scales. The intense multiplication of these electrons is triggered when the size of the negative leader streamer zone crosses a threshold of approximately 100 m (for sea-level air pressure conditions) allowing self-replication of these avalanches due to the upstream relativistic electron seeds generated by the photoelectric absorption.

References: 
Pasko et al., GRL, 50, e2022GL102710, 2023, https://doi.org/10.1029/2022GL102710
Pasko et al., PSST, 32, 075014, 2023, https://doi.org/10.1088/1361-6595/ace6d0

How to cite: Pasko, V., Celestin, S., Bourdon, A., Janalizadeh, R., and Jansky, J.: Feedback Effects in Positive Corona and Relativistic Runaway Discharges, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11937, https://doi.org/10.5194/egusphere-egu24-11937, 2024.

Terrestrial gamma-ray flashes (TGFs), powerful bursts of gamma-rays produced within our atmosphere, often occur in association with lightning. However, the mechanisms for generating the large number of runaway electrons required to account for the TGF luminosities remain uncertain. For example, TGFs might be produced by cold-runaway electron production from streamer heads and/or leader tips in the high-field regions near lightning, or TGFs might be produced by the self-sustained production of runaway electrons by relativistic feedback involving backward propagating runaway positrons and backscattered x-rays. Because both mechanisms could possibly occur in the presence of lightning leaders, it has been challenging to test which TGF production mechanisms are important. In this work, detailed simulations are used to test whether TGFs may be produced by thunderstorm electrification alone, without the presence if lightning. It is found that rapid thunderstorm charging may first produce strong gamma-ray glows, followed by large pulses of gamma-rays, followed by multi-pulsed TGFs similar to the TGFs first observed by CGRO/BATSE. Furthermore, the ionization produced by the high-energy particles partially discharges the electric field in some regions while amplifying the field in other regions, potentially allowing for the initiation of narrow bipolar events (NBEs) and/or lightning. If confirmed, such sequence of events would be strong evidence for the relativistic feedback mechanism.

How to cite: Dwyer, J. and Liu, N.: Gamma-ray glows and terrestrial gamma-ray flashes produced by thunderstorm electrification without lightning , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12103, https://doi.org/10.5194/egusphere-egu24-12103, 2024.

EGU24-12247 | ECS | Orals | NH1.5

Investigating Storm Charge Distribution Trends with Intracloud Lightning Polarity Data 

Elizabeth DiGangi, Jeff Lapierre, and Yanan Zhu

There are, at present, two accepted primary paradigms for thunderstorm charge distribution using a simple tripole model: “normal” polarity storms, which are characterized by a central negative charge region, an upper positive charge region, and sometimes a lower positive charge region; and “inverted” polarity storms, which are characterized by a central positive charge region, an upper negative charge region, and sometimes a lower negative charge region. The real distribution of thunderstorm charge is known to be more complex than the tripole model can represent, but the normal/inverted paradigm is still widely used in the field. Characterizing storms as having a normal or inverted polarity has been a subject of interest in lightning research since discovering that inverted storms produce a larger-than-average fraction of positive amplitude cloud-to-ground (CG) lightning compared with normal storms. +CG lightning is understood to have generally higher peak currents and a much greater probability of producing continuing current than -CGs, which is relevant for research into subjects like lightning-initiated wildfires and transient luminous events. Thunderstorm charge distribution is also directly related to storm microphysics and thermodynamics, which, in turn, links it to the meteorological characteristics of storms and storm environments.

Most published research on storm polarity has either investigated large-scale trends in +CG versus -CG frequency from long-range lightning detection systems (LDSs), or has used LDSs which map lightning in 3D to infer storm polarity directly from intracloud (IC) lightning leader propagation patterns. Data on IC lightning from long-range LDSs is a resource which, to our knowledge, has not yet been used to study bulk storm charge structures. It stands to reason that if inverted storms favor the production of more +CGs than normal storms, then they would also favor the production of more -ICs. The goal of this study is therefore to interrogate several years of lightning data from the Earth Networks Total Lightning Network (ENTLN) to determine whether or not IC peak current information can be used to study storm charge structure and the geographic distributions of inverted and normal polarity storms.

How to cite: DiGangi, E., Lapierre, J., and Zhu, Y.: Investigating Storm Charge Distribution Trends with Intracloud Lightning Polarity Data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12247, https://doi.org/10.5194/egusphere-egu24-12247, 2024.

EGU24-12606 | Posters on site | NH1.5

CubeSpark: Space-based 3-D Lightning Mapping using a Constellation of Radio Frequency Sensors 

Sonja Behnke, Kim Katko, Harald Edens, Patrick Gatlin, Timothy Lang, William Haynes, Paul Snow, Jeremiah Rushton, Joellen Renck, Charley Weaver, Larry Bronisz, Jacob Pratt, Steven Dobson, Nikhil Pailoor, Jackson Remington, and Sarah Stough

CubeSpark is a new concept for a constellation of CubeSats that combines bi-spectral optical lightning imaging with radio frequency (RF) sensing to provide a 3-D lightning detection capability with global coverage from low-Earth Orbit. The development of CubeSpark is a collaboration between Los Alamos National Laboratory and NASA Marshall Space Flight Center. CubeSpark innovates over current ground and space-based global lightning capabilities by determining the altitude of lightning radiation sources, enabling new science in thunderstorm processes and the impact of lightning on climate. The key to determining the altitude of lightning is using a constellation of RF sensors to make coordinated measurements of impulsive RF radiation sources, similar to the approach of a ground-based lightning mapping array. The RF measurements will be enhanced with bi-spectral optical sensors to improve overall lightning detection efficiency and provide additional, complementary information about lightning processes.

This presentation introduces the CubeSpark mission concept and science applications with a focus on the RF hardware under development. Two challenges of space-based RF lightning detection are ionospheric effects and RF noise from both the satellite bus and anthropogenic sources from Earth. While the process of removing ionospheric dispersion from broadband waveforms for time-of-arrival (TOA) estimation is well established, CubeSpark further reduces ionospheric impacts on TOA by using a circularly polarized antenna, which suppresses one of the birefringent wave modes. For noise reduction, the CubeSpark receiver leverages programmable high- and low-pass filters to allow for on-orbit modifications of its passband. A benchtop demonstration of the RF hardware has been completed.

How to cite: Behnke, S., Katko, K., Edens, H., Gatlin, P., Lang, T., Haynes, W., Snow, P., Rushton, J., Renck, J., Weaver, C., Bronisz, L., Pratt, J., Dobson, S., Pailoor, N., Remington, J., and Stough, S.: CubeSpark: Space-based 3-D Lightning Mapping using a Constellation of Radio Frequency Sensors, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12606, https://doi.org/10.5194/egusphere-egu24-12606, 2024.

EGU24-12658 | Posters on site | NH1.5

GLM lightning flashes observed during ASIM triggers over Tropical South America 

Carlos Morales, Joan Montanyà, Jesus Lopéz, Oscar Van Der Velde, Nicolai Østgaard, Torsten Neubert, and Víctor Reglero

The Atmosphere-Space Interactions Monitor (ASIM) on board the International Space Station (ISS) is collecting data of lightning and Terrestrial Gamma Flashes (TGF) over the globe since April 2018 by means of two suites: i) modular multispectral imaging array (MMIA); and ii) modular X and gamma-ray sensors (MXGS). MMIA responds to lightning flashes, while high energy detector (HED) and low energy detector (LED) of MXGS are employed to estimate TGF spectra and source. Based on these features, ASIM is providing a large dataset of MMIA, LED and HED triggers that are used identify potential TGF events that require an extra imaging analysis to depict the exact location and validation. Upon such measurements, this study employs coincident ASIM and GLM lightning flashes over Tropical South America (90-30W and 20S-10N) to inspect if the electrically active thunderstorms present unequivocal features associated with each ASIM trigger, i.e., MMIA, LED, HED and TGF. Electrically active thunderstorms were identified as contiguous GLM lightning flashes clustered at 0. 1 x 0.1 degrees on ± 30 minutes of ASIM trigger time following Barnes et al. (2015) and Morales et al. (2021) procedures. During the period of 2018 and 2021, we were able to find 30,417 active thunderstorms that have lightning flashes within ± 3 seconds of trigger time (19,546 during the night and 10,871 during the day). Of those thunderstorms, 343 (1,745) were identified with HED, 278 (1,752) with LED, 12,858 (27811) with MMIA and 49 (116) with TGF within 0-200 ms (200ms-3 sec) of the trigger time. The spatial distribution of those thunderstorms do not show any lightning hot spot. MMIA thunderstorms coincide with the location of HED and LED thunderstorms, except HED thunderstorms over the Peruvian Andes mountain range. Moreover, we did not find any TGF thunderstorms along the mountain regions, especially in Peru and Ecuador. The 60 minutes lightning activity (# flashes/per minute) reveal that TGF thunderstorms show higher lightning flash rates than the MMIA, HED and LED triggered thunderstorms, in addition of a sudden lightning flash rate increase prior to the TGF trigger and sustained high lightning activity for the following 10 minutes. HED and LED show similar lightning temporal evolution (flash rate increase before the trigger and decay afterwards), but LED triggered thunderstorms have higher flash rates over the entire 60 minutes time period. MMIA triggered thunderstorms show the lowest flash rates and almost steady lightning activity during the entire 60 minutes. Based on 90% confidence level of T-Student test, we found that TGF and MMIA thunderstorms are statistical different during the entire 60 minute time period, meaning that not all MMIA thunderstorms produce TGFs. In another hand, we can state that HED and LED triggers are good indicators of TGF emissions, since they are not statistically different, meaning that these parameters could be used as triggers to identify TGF occurrences.

How to cite: Morales, C., Montanyà, J., Lopéz, J., Van Der Velde, O., Østgaard, N., Neubert, T., and Reglero, V.: GLM lightning flashes observed during ASIM triggers over Tropical South America, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12658, https://doi.org/10.5194/egusphere-egu24-12658, 2024.

EGU24-13074 | ECS | Posters on site | NH1.5 | Highlight

A Deep Learning Approach to Lightning Nowcasting and Forecasting 

Randall Jones, Joel Thornton, Dale Durran, Lyatt Jaeglé, Christopher Wright, and Robert Holzworth

Lightning plays a fundamental role in Earth’s climate system and is a frequently occurring natural hazard. However, lightning remains a relatively unpredictable area of meteorology, especially in terms of lightning frequency per convective event, with limited ability for nowcasting and forecasting of lightning occurrence. The goal of this study is to develop a deep learning algorithm able to replicate lightning stroke density on a climatological average, as well as on a convective feature basis. We use a convolutional neural network (CNN) containing combinations of the following variables at 0.5-degree by 0.5-degree spatial resolution and a 3-hourly temporal-resolution over a domain that encompasses most of the Western Hemisphere: lightning from the World-Wide Lightning Location Network (WWLLN), precipitation rate from NASA’s Integrated Multi-satellite Retrievals for GPM (IMERG) and convective available potential energy (CAPE), cloud base height (CBH), two-meter temperature (T2M) and zero degree level (ZDL) from the European Centre for Medium-Range Weather Forecasting (ECMWF). We train the CNN on the years from 2010 to 2018, and tested on the years 2019 to 2022. Model performance was evaluated on a four-year average through changes to the initial seed used to train the model, the loss function used, transformations to the lightning dataset, and changing the spatial and temporal resolution of the input datasets. We further examined the value of 11 input variable combinations, from single variables to all five variables used in training. Preliminary results show that changing the initial seed, as well as changing the loss function from mean squared error to mean-squared logarithmic error, does not greatly impact model performance when running the model with more than one input variable. Results vary amongst the variable combinations, but amongst the different initial seeds and loss functions, the r-squared values remain above 0.75 for every model configuration over both land and ocean. Model performance is improved when using higher time resolution training set but not necessarily a higher spatial resolution. For example, a 1-degree by 1-degree spatial resolution and a 3-hourly time resolution resulted in an r-squared between predicted and observed lightning frequency 0.1 higher than that using 0.5-degree by 0.5-degree spatial resolution and a daily time resolution. The model is able to reproduce the approximate evolution of lightning stroke density of individual convective events, but tends to overestimate the stroke density on a 3-hourly basis. Future work will include a steeper penalty for overestimating lightning occurrence during training. These results show that larger-scale weather forecasting and earth system models could significantly improve lightning stroke density parameterizations by incorporating deep learning results.

How to cite: Jones, R., Thornton, J., Durran, D., Jaeglé, L., Wright, C., and Holzworth, R.: A Deep Learning Approach to Lightning Nowcasting and Forecasting, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13074, https://doi.org/10.5194/egusphere-egu24-13074, 2024.

EGU24-13383 | ECS | Posters on site | NH1.5

Peak currents of terminating flashes in thunderstorm ground enhancements around Mt Aragats, Armenia 

Gayane Karapetyan, Earle Williams, Hripsime Mkrtchyan, and Reik V. Donner

Thunderstorm ground enhancements (TGEs) are high-energy particle fluxes detected at the ground level during thunderstorms. It has been observed that some TGEs experience abrupt termination by lightning strikes (Chilingarian 2015, Tsuchiya 2013, Williams et al., 2022) often accompanied by simultaneous reductions in flux. Understanding the origin and parameters of terminating lightning can provide insights into the distribution of electric fields and potential within thunderclouds. 

Thundercloud potential is a key factor in determining the maximum peak current of lightning. One expects a linear relationship between peak current and cloud potential because the charge that is deposited on the leader channel is proportional to the leader potential (e.g. Chronis et. al. 2015). 

This study evaluates peak currents in terminating flashes documented in TGEs observed around Mt Aragats (Armenia) using a ground-based VLF lightning detection network, GLD360. A total of 71 terminating flashes have been identified over a period of 6 years (2017-2022). The events documented at Aragats were detected by particle detectors that showed the abrupt decrease in flux associated with lightning. These events were accurately timed using an EFM100 electric field mill (resolution of 2Hz). Thereafter, correlations between these events and the corresponding GLD360 lightning events were established, using millisecond precision times of GLD360 and electric field mill.

Our findings show that the mean peak current of this collection of terminating flashes (45 kA) is 3.4 times higher than that of the general population of lightning flashes measured in the same location (13.6 kA) over a similar period of time. However, it is difficult to define the relationship between the change in electric field during TGE or lightning and the peak currents. It appears that lightning with smaller peak currents tends to have larger values of the change of electric field, while lightning with larger peak currents is characterized by an average change in the electric field.

This research provides insights into peak currents of terminating lightning flashes with general parameters of the TGEs, such as duration and flash rate. Additionally, it shows that flashes with extremely high peak currents occur during thunderstorms with smaller flash rates and are located within 10 km distance from the particle detectors. Furthermore, flash rates of thunderstorms with terminating lightning are larger compared to general thunderstorms without TGEs.

How to cite: Karapetyan, G., Williams, E., Mkrtchyan, H., and Donner, R. V.: Peak currents of terminating flashes in thunderstorm ground enhancements around Mt Aragats, Armenia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13383, https://doi.org/10.5194/egusphere-egu24-13383, 2024.

EGU24-13989 | Posters on site | NH1.5

C3IEL, the Cluster for Cloud evolution ClImatE and Lightning mission to study convective clouds at high spatial and temporal resolutions 

Eric Defer, Celine Cornet, Daniel Rosenfeld, Cecile Cheymol, Adrien Deschamps, Alex Frid, Laurene Gillot, Vadim Holodovsky, Avner Kaidar, Raphael Peroni, Colin Price, Didier Ricard, Antoine Rimboud, Yoav Schechner, Aviad Shmaryahu, and Yoav Yair

The French-Israeli space-borne C3IEL (Cluster for Cloud evolution, ClImate and Lightning) mission aims at providing new insights on convective clouds, at high spatial and temporal resolutions, close to the scales of the individual convective eddies. The mission will simultaneously characterize the convective cloud dynamics, the interactions of clouds with the surrounding water vapor, and the lightning activity.

The C3IEL mission consists in a short-baseline (~150 km) train of 2 synchronized nano-satellites. Each nano-satellite carries a visible camera (670 nm) for cloud imagery at a spatial resolution of ~20 meters, near-infrared water vapor imagers (1.04, 1.13 et 1.37 µm) measuring in and near the water vapor absorption bands, and a lightning imager (777.4 nm) and at least one photometer (777.4 nm).

The scientific objectives of the C3IEL mission, i.e. documenting the 3D evolution of the clouds’ surface, entrainment of water vapor, and electrification, will be first reminded. Then, we will introduce the satellite train configuration, the different sensors of the mission and the innovative and different observational strategy that will be applied during daytime and nighttime. We will then detail the expected observations and products, including the ones related to lightning.

How to cite: Defer, E., Cornet, C., Rosenfeld, D., Cheymol, C., Deschamps, A., Frid, A., Gillot, L., Holodovsky, V., Kaidar, A., Peroni, R., Price, C., Ricard, D., Rimboud, A., Schechner, Y., Shmaryahu, A., and Yair, Y.: C3IEL, the Cluster for Cloud evolution ClImatE and Lightning mission to study convective clouds at high spatial and temporal resolutions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13989, https://doi.org/10.5194/egusphere-egu24-13989, 2024.

EGU24-14531 | ECS | Posters virtual | NH1.5

Identifying atmospheric conditions for intermittent, small-scale lightning discharges near the top of thunderstorms 

Reinaart van Loon, Jelle Assink, Olaf Scholten, Brian H Hare, and Hidde Leijnse

Despite its impact on society, many aspects of lightning, including the initiation and propagation, remain poorly understood. This also applies to a distinct type of intermittent small-scale lightning discharges recorded by the Low-Frequency Array (LOFAR) in the Netherlands (Scholten et al., 2023). The so-called “sparkles” seem uncorrelated and occur relatively high up in thunder clouds near the tropopause. This research investigates the meteorological conditions under which sparkles exist.  

Previous literature suggests a correlation between sparkles and strong updrafts. One hypothesis proposes that powerful updrafts overshooting the level of neutral buoyancy causes a charged screening layer aloft to be entrained into the cloud, resulting in charge pockets. Alternatively, some hypothesize that turbulence plays a vital role in discharge initiation and charge sedimentation. Therefore, intense turbulence near the top of strong updrafts could not only initiate numerous discharges, but could also influence the lightning structures through the spatial charge distribution. 

This project aims to improve the understanding of sparkles by comparing high- resolution LOFAR lighting data with meteorological data. Specifically, thunderstorm dynamics are studied using data from satellites, radar and the HARMONIE weather forecast model. Following the hypotheses, relations are explored between sparkling activity and factors such as updrafts strength, turbulence, mixing, and entrainment of the air aloft.

Scholten, O., Hare, B. M., Dwyer, J., Liu, N., Sterpka, C., Assink, J., ... & Veen, S. T. (2023). Small‐Scale Discharges Observed Near the Top of a Thunderstorm. Geophysical Research Letters, 50(8), e2022GL101304. 

How to cite: van Loon, R., Assink, J., Scholten, O., Hare, B. H., and Leijnse, H.: Identifying atmospheric conditions for intermittent, small-scale lightning discharges near the top of thunderstorms, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14531, https://doi.org/10.5194/egusphere-egu24-14531, 2024.

EGU24-14754 | Orals | NH1.5

Quantitative detection device for NOx of centimeters-discharge and its preliminary applications in laboratory long spark and rocket-triggered lightning 

Rubin Jiang, Yufan Ren, Ruiling Chen, Hongbo Zhang, Mingyuan Liu, Xinran Xia, Jianwen Wu, Dongfang Wang, Kun Liu, and Xiushu Qie

A quantitative detection device for nitrogen oxides (NOx) produced by the centimeters-scale discharge channel is designed, consisting of a container made of high-strength acrylic Plexiglas, two copper metal electrodes fixed to the top and bottom of the container, a pumping system and a back-end NOx detector. Inside the container, the gap between the two copper electrodes is 4 cm in length. When a discharge occurs between the electrodes, the NOx produced by the air ionization are confined within the container to provide a quantitative measurement. The device can be used in the laboratory long spark and rocket-triggered lightning scenarios, with container volumes of 12.2 L and 58.8 L, respectively, both of which ensure an accurate measurement of the discharge current. In the laboratory long spark scenario, the device is placed under the discharge electrode of the Marx generator. As the discharge is generated, the discharge strikes the upper copper metal electrode and leads to the gap breakdown within the container, then the current is released through the bottom copper metal electrode to the ground. In the rocket-triggered lightning scenario, the device is fixed between the current sensor and the grounding system. The triggered discharge leads to the gap breakdown within the container, and the current is also released through the bottom copper metal electrode to the ground. After the discharge, the gas in the canister is pumped to the NOx concentration meter. The instruments used are the Thermo, which uses a chemical method to measure NO and NOx concentrations with a time resolution of 1 minute, and the LGR-NO2, which uses an optical method to measure NO2 concentrations with a time resolution of 1 second. The preliminary experiment shows that the 4 cm long discharge due to the laboratory long spark with a peak current of about 2 kA produced 6.8×1017 NO2 molecules. In an unsuccessful triggering lightning case, the discharges due to the precursors also lead to significant NOx signals.

How to cite: Jiang, R., Ren, Y., Chen, R., Zhang, H., Liu, M., Xia, X., Wu, J., Wang, D., Liu, K., and Qie, X.: Quantitative detection device for NOx of centimeters-discharge and its preliminary applications in laboratory long spark and rocket-triggered lightning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14754, https://doi.org/10.5194/egusphere-egu24-14754, 2024.

EGU24-15389 | ECS | Posters on site | NH1.5

TGFs observed by the ALOFT 2023 flight campaign during an ISS overpass 

Ingrid Bjørge-Engeland, Nikolai Østgaard, Timothy Lang, Martino Marisaldi, J. Eric Grove, Mason Quick, Hugh Christian, Christopher Schultz, Richard Blakeslee, Ian Adams, Rachael Kroodsma, Gerald Heymsfield, Andrey Mezentsev, David Sarria, Anders Fuglestad, Nikolai Lehtinen, Kjetil Ullaland, Shiming Yang, Bilal Hasan Qureshi, and Jens Søndergaard and the ALOFT team

During the Airborne Lightning Observatory for FEGS and TGFs (ALOFT) campaign in July 2023, the International Space Station (ISS), at an altitude of approximately 410 km, passed over the same region as covered by ALOFT within a short time period on the 24th of July. The ALOFT campaign, which carried gamma-ray detectors, photometers, and instruments for characterizing the electrical activity and the cloud environment, flew at an altitude of approximately 20 km and covered thunderstorms over the Gulf of Mexico and Caribbean during its 60 flight hours. The Atmosphere-Space Interactions Monitor (ASIM) is mounted on the ISS, with its Modular X- and Gamma-ray Sensor (MXGS) designed for observing TGFs. During the ISS overpass, ALOFT observed six TGFs within less than two minutes that were all within the field of view of the ASIM instrument. However, none of the TGFs were detected by ASIM. Here we present the six TGFs observed by ALOFT during the ISS overpass and discuss their source properties. The ASIM non-detection provides a strong upper limit on the TGF fluence.

How to cite: Bjørge-Engeland, I., Østgaard, N., Lang, T., Marisaldi, M., Grove, J. E., Quick, M., Christian, H., Schultz, C., Blakeslee, R., Adams, I., Kroodsma, R., Heymsfield, G., Mezentsev, A., Sarria, D., Fuglestad, A., Lehtinen, N., Ullaland, K., Yang, S., Hasan Qureshi, B., and Søndergaard, J. and the ALOFT team: TGFs observed by the ALOFT 2023 flight campaign during an ISS overpass, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15389, https://doi.org/10.5194/egusphere-egu24-15389, 2024.

EGU24-15691 | ECS | Posters on site | NH1.5

Supervised Machine Learning for the Automatic Classification of Triggers from ASIM/MXGS on board the ISS 

Jose E. Adsuara, Javier Navarro-González, Paul Connell, Víctor Reglero, Nikolai Østgaard, and Torsten Neubert

During ASIM operations from June 2018 until the end of 2019, 486 TGFs were observed. For this task, the ASDC (ASIM Science Data Center) dealt with numerous triggers from the instrument (5000 per week). The relocation of the instrument from EPF SDX to EPF SDN (Starboard Deck Nadir) of the Columbus ISS Module on January 10, 2022, demonstrated that the MXGS location capabilities could be used not only for TGF location but also for imaging GRB events, as its Field of View in the SDN port encompasses both Earth and space.

It's worth noting that only a few of the ASIM triggers correspond to TGF events. There is a screening process employing a series of algorithms to detect and discard false positives (triggers that are not TGFs). Nevertheless, the ASIM archive retains all data from every trigger. Due to the extended operational time, there is currently a sufficiently large database that enables us to present the initial results here using novel machine learning methods, such as kernel methods or neural networks, for the automatic categorization of both present and future events.

Furthermore, our interest goes beyond mere classification, as we are currently investigating whether various explainability methods applied to these techniques can assist in identifying the relevant features of the signal for such classification. The aim of this work is to provide a tool to quantify new physical processes that could be the cause of instrument triggers and to examine whether there is a connection with the Earth-Space global circuit.

How to cite: Adsuara, J. E., Navarro-González, J., Connell, P., Reglero, V., Østgaard, N., and Neubert, T.: Supervised Machine Learning for the Automatic Classification of Triggers from ASIM/MXGS on board the ISS, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15691, https://doi.org/10.5194/egusphere-egu24-15691, 2024.

EGU24-16929 | Orals | NH1.5

Constraining electrification of volcanic plumes through numerical simulation 

Michael Herzog, Vishnu Nair, Alexa Van Eaton, and Ted Mansell

Technological improvements over the past decade have dramatically increased lightning detection from explosive eruptions worldwide. The underwater Hunga Tonga-Hunga Ha’apai volcano eruption in January 2022 in Tonga produced more lightning than any storm yet documented in the modern satellite era. These observations of volcanic lightning capture the imagination of the public and provide novel ways to monitor explosive hazards in near real time. In this presentation, we present the first results from the numerical simulation of the electrification of a volcanic plume using the volcanic plume model ATHAM. The electrification mechanisms of fracto-emission and triboelectrification along with the macroscopical transport of the charge carrying plume components have been modelled in ATHAM to make this the first numerical model to quantify volcanic electrification. We also present first results of discrete lightning discharges which are diagnosed as continuous branching regions defined by local net charge density and electric potential. 

The enhanced modelling capability of ATHAM opens new routes into the study of explosive eruptions and nowcasting of volcanic ash hazards for aviation and downwind communities. 

How to cite: Herzog, M., Nair, V., Van Eaton, A., and Mansell, T.: Constraining electrification of volcanic plumes through numerical simulation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16929, https://doi.org/10.5194/egusphere-egu24-16929, 2024.

EGU24-17483 | ECS | Orals | NH1.5

Characterisation and modelling of lightning strikes as point events in time and space 

Uldis Zandovskis, Davide Pigoli, and Bruce D. Malamud

Lightning, a spatio-temporal phenomenon, comprises of individual strikes with specific occurrence times and spatial coordinates. This study models and characterises lightning strikes from single thunderstorms, treating each strike as a point event. Utilising real-world datasets, we characterise and model lightning strikes' physical properties. Our analysis involves two severe UK thunderstorm systems, selected based on published synoptic analyses. These systems enable subdivision of the lightning dataset into subsets, each representing a distinct thunderstorm. Our two major storm systems feature three thunderstorms each: Storm system A with 7955, 11988, and 5655 strikes over the English Midlands on 28 June 2012; Storm system B with 4218, 455, and 1926 strikes characterised over the northern England on 1-2 July 2015. These six datasets exemplify individual thunderstorms and a total of three physical attributes are : movement speed, lightning inter-event time distribution, and spatial spread about the storm track. Applying least-squares plane and linear fits in the spatio-temporal and lag spaces, we estimate movement speeds of 47-59 km/h and 67-111 km/h for Storm systems A and B, respectively. The inter-event time distribution ranges from 0.01 to 100 seconds, with density peaks around 0.1 seconds and at 1-10 seconds. Autocorrelation analysis in natural time reveals significant autocorrelation in all storms, varying from short-range to long-range. For spatial spread, calculated as the distance from the storm track to the strikes, we employ a linear filter to establish the storm track. This analysis yields typical spatial spreads up to 80 km in either northing or easting dimensions, with an outlier of 226 km in the northing dimension for one storm. The paper concludes with a synthetic lightning strike model. This model allows selection of individual storms' starting points, directions, and movement speeds, generating point events based on our characterisation findings. This comprehensive study of lightning strikes in time and space accurately reflects severe thunderstorms' behaviour and informs statistical models for simulating lightning events.

How to cite: Zandovskis, U., Pigoli, D., and Malamud, B. D.: Characterisation and modelling of lightning strikes as point events in time and space, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17483, https://doi.org/10.5194/egusphere-egu24-17483, 2024.

EGU24-18529 | Orals | NH1.5

Analysis of an upward discharge above a thundercloud over Mediterranean Sea 

Serge Soula, Gabriel Hausknost, Axel Ventre, Sylvain Coquillat, Janusz Mlynarczyk, and Alex Hermant

On the night of November 1st, 2022, several weather photographers obtained remarkable photos showing a jet-like phenomenon with long blue filaments above a Mediterranean storm. An unprecedented set of optical and electrical data, including two pictures, one movie, VHF sources from a Lightning Mapping Array (LMA), detections from a LLS and Current Moment Waveforms from an ELF detection, makes it possible to characterize this event. It consists of a two-part upward luminous channel emerging from the cloud top at 11.8 km of altitude, developing up to 14.2 km and topped with blue streamers up to 17.2 km. It is embedded in a flash which starts with a positive 25 kA-discharge followed by a continuing current during 75 ms associated with VHF sources at 10 km. Contrary to blue jets and blue starters which have a positive polarity, the luminous event above the cloud is identified as a negative leader followed by three channel brightenings linked to the negative charge of a positive cloud dipole. The luminous event-producing flash is preceded by a convective surge and a production of positive flashes within the same region of the cloud. The triggering conditions and mechanisms of the event share similarities with gigantic jets, especially its polarity and a reduced upper positive charge.

How to cite: Soula, S., Hausknost, G., Ventre, A., Coquillat, S., Mlynarczyk, J., and Hermant, A.: Analysis of an upward discharge above a thundercloud over Mediterranean Sea, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18529, https://doi.org/10.5194/egusphere-egu24-18529, 2024.

EGU24-18594 | Orals | NH1.5 | Highlight

Observations of thunderstorms with a neuromorphic camera: First results of the THOR-DAVIS experiment on the International Space Station. 

Olivier Chanrion, Nicolas Pedersen, Yoav Yair, Martin Stendel, Andreas Mogensen, Dongshuai Li, Andreas Stokholm, and Torsten Neubert

THOR-DAVIS is an experiment on the International Space Station to observe thunderclouds and their electrical activity with a neuromorphic camera and a co-aligned video camera. A neuromorphic camera, or 'event camera,' only reads pixels when there is a change in pixel illumination, allowing for a temporal resolution that may reach 10 microseconds. Launched by the SpaceX Commercial Resupply Service mission on June 5, 2023, THOR-DAVIS was part of Danish ESA astronaut Andreas Mogensen’s Huginn mission. The scientific focus was to conduct video observations of electrical activity at the cloud tops and the stratosphere above and to extract their altitudes. The technical objective was to test the neuromorphic concept for observations of thunderstorms from space. Andreas Mogensen performed 15 days of observations, passing over 48 thunderstorms, most forecasted by us a day in advance following a procedure inherited from previous ISS experiments (THOR (2015), ILAN-ES (2022)) and some at his own initiative. In all, 36 thunderstorms were recorded in both cameras, totaling ~3 hours of observations. Most notably, Andreas Mogensen secured the first observations of sprites and of an elve with a neuromorphic camera. In addition, numerous lightning flashes, including spider lightning with leader branches extending above the clouds, were observed. The presentation will provide an overview of the THOR-DAVIS payload design, laboratory measurements, and some of the observations from the ISS.

How to cite: Chanrion, O., Pedersen, N., Yair, Y., Stendel, M., Mogensen, A., Li, D., Stokholm, A., and Neubert, T.: Observations of thunderstorms with a neuromorphic camera: First results of the THOR-DAVIS experiment on the International Space Station., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18594, https://doi.org/10.5194/egusphere-egu24-18594, 2024.

EGU24-18787 | Orals | NH1.5

Machine Learning to predict Downbursts in Japan Based on Total Lightning and Ground Precipitation Data 

Alexander Shvets, Yasuhide Hobara, Shiho Miyashita, Hiroshi Kikuchi, Jeff Lapierre, and Elizabeth DiGangi(

In this study, using total lightning data from the Japan Total Lightning Network (JTLN) and precipitation data from X-band MP radar, machine learning with a Random Forest model was used to successfully classify the occurrence of downbursts in Japan. TL data associated with the event is collected from JTLN which consists of 11 Earth Networks Total Lightning Sensorsover Japan (data set in 2017, currently 16 stations nationwide) deployed by the UECand jointly operated with Earth Networks. These sensors can detect lightning pulses with a spatial resolution of 500 m. TL parameters such as types of lightning (IC and CG), time of occurrence (UT), location (latitude-longitude), and lightning polarity were collected for each lightning discharge. Ground precipitation data (temporal resolution of 1min, spatial resolution of 250m) from the Ministry of Land, Infrastructure, Transport, and Tourism’s eXtended RAdar Information Network (XRAIN) composed of 26 C-band radars and 39 X-band multiparameter (X-MP) radars are used. Fourteen thunderstorms causing gusty winds and 33 of those without causing gusty winds that occurred in Japan from 2014 to 2017 were analyzed. AITCC (Algorithm for the Identification and Tracking of Convective Cells) was applied to track both precipitation cell and associated lightning discharges. By using Random Forest model, the importance of variables was derived, and it was shown that lightning jump is one of the most important variables for predicting downbursts. This implies that the updrafts inside the clouds are closely related to the occurrence of a significant increase in lightning, followed by a downburst. The prediction accuracy was highest for models that included both lightning and precipitation, followed by lightning-only and precipitation-only models, confirming the importance of data fusion for improving prediction accuracy.

How to cite: Shvets, A., Hobara, Y., Miyashita, S., Kikuchi, H., Lapierre, J., and DiGangi(, E.: Machine Learning to predict Downbursts in Japan Based on Total Lightning and Ground Precipitation Data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18787, https://doi.org/10.5194/egusphere-egu24-18787, 2024.

EGU24-20590 | Orals | NH1.5

Re-discharges on preexisting negative leader channels of a positive cloud-to-ground lightning flash  

Zhuling Sun, Xiushu Qie, Mingyuan Liu, and Fengquan Li

Using the lightning VHF interferometer, three types of discharges on the preexisting negative leader channels of a positive cloud-to-ground lightning flash were observed. The first type involved small-scale cluster discharges during the simultaneous development of the upper horizontally negative leader and downward positive leader before the return stroke. These discharges exhibited similar characteristics and radiation features as the needle-like discharges on the positive leader. Over time, their occurrence positions progressed toward the head of the negative leader, and some cluster discharges had the potential to develop into new negative branches. The other two types of re-discharges occurred after the return stroke. Immediately after the return stroke, rapid discharges initiated near the head of the negative leader, developed along the preexisting negative leader channel, and caused the decayed negative leaders to progress forward again. Subsequently, numerous lateral discharges breaking down the air occurred, distributed widely throughout the negative leader channel. These discharges developed rapidly, gradually slowing down over time until the long continuous current ended. In comparison to the positive leader discharges before the return stroke, which showed no obvious recoil leader discharges, the negative leader channel was more prone to extinguish. These re-discharges on the preexisting negative leader channel were influenced by both radial and longitudinal electric fields of flash channels, and they could also generate a backward surging current wave to sustain the discharge process on the positive leader or grounded channel.

How to cite: Sun, Z., Qie, X., Liu, M., and Li, F.: Re-discharges on preexisting negative leader channels of a positive cloud-to-ground lightning flash , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20590, https://doi.org/10.5194/egusphere-egu24-20590, 2024.

EGU24-20630 | Orals | NH1.5

eLMA: Supercells over the Upgraded Ebro 3D Lightning Mapping Array and High-speed Observations of Lightning in the Near-Ultraviolet 

Oscar van der Velde, David Romero, Jesús López, Joan Montanyà, and Nicolau Pineda

In 2011, the Ebro 3D Lightning Mapping Array was the first LMA installed outside the USA, consisting of 12 stations in 2012-2014 and was subsequently split in half in 2015 to facilitate a LMA in Colombia.

Thanks to research infrastructure service funding from the Spanish government and the European Union (MCIN/AEI/10.13039/501100011033/ and NextGenerationEU, project EQC2021-006957-P), the Ebro LMA has been upgraded to a wider area network operating 15 latest technology LMA stations operating on solar power in the Ebro Valley region (western Catalonia and eastern Aragón). New services are offered: (1) Real-time tracking of lightning flashes across northeastern Spain, available to the public via the website elma.upc.edu. (2) Archive of plotted data that can be browsed freely, including for the old Ebro LMA data. (3) Raw/processed data can be requested. (4) LMA rental is possible for field campaigns.

We developed a new processing & visualization tool for flash/thunderstorm analysis and future integration with the new EUMETSAT Lightning Imager (LI). It is written in the Julia programming language for its speed of processing with the Makie interactive graphics package. Additionally, we present a new tool for displaying regional maps of actual (not computed) LMA station contributions to assess the network performance. The capabilities of the new Ebro LMA are showcased with record-setting horizontal lightning flashes, several large-hail producing supercells with high flash rates, a lightning hole, and rising turrets of small flashes and sparks at the cloud top, which can now be isolated and analyzed with the Julia visualization tool. The electrical evolution features of these supercells are examined in relation to their timeline of severe weather production.

Additionally, a Vision Research Phantom TMX 6410 and UV-sensitive Lambert HiCATT 25 image intensifier with optics and filters were acquired, and is also available to third parties via eLMA rental services. During spring/summer 2023 it has been successfully used to image lightning leaders through a 337 nm optical narrowband filter (10 nm width) similar to imaging systems of the Atmosphere-Space Interactions Monitor (ASIM), at speeds of 65,000 to 320,000 frames per second. We found that observation distances <4 km are needed in order to be able to see the stepped leader in negative cloud-to-ground flashes. However, in only one case, of an intense burst of horizontal leader activity below the cloud base, negative streamers can be clearly distinguished and the stepping process analyzed over time. At greater distances only return strokes and dart leaders are tracked through the 337 nm filter. In fact, a >418 ms long continuing current negative return stroke (cut off by end of buffer) was observed. Also, the system captured elves, nocturnal optical emissions at the base of the ionosphere (85 km) over Mediterranean winter thunderstorms, clearly showing the typical double-wave structure originally reported by photometer arrays.

How to cite: van der Velde, O., Romero, D., López, J., Montanyà, J., and Pineda, N.: eLMA: Supercells over the Upgraded Ebro 3D Lightning Mapping Array and High-speed Observations of Lightning in the Near-Ultraviolet, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20630, https://doi.org/10.5194/egusphere-egu24-20630, 2024.

EGU24-20763 | ECS | Orals | NH1.5

Low-frequency sferics associated with consecutive Terrestrial Gamma-ray Flashes  

Hongbo Zhang, Xiushu Qie, and Gaopeng Lu

Terrestrial Gamma-ray Flashes (TGFs) are brief and intense emissions of hard X-rays and gamma-rays originating inside thunderstorms. It has been observed that TGF occurs much less frequently than lightning. However, the TGF generation conditions and mechanism of are not clear, such as why just the TGF-associated lightning produces TGF while others not. Consecutive TGFs detected by space-based platform are usually several seconds to 1-2 minutes apart, and they come from same meteorological environment and even from the same storm cells. This provides a possibility to understand the relationship between lightning and TGF. Based on Fermi high-energy photons observations and the ground low-frequency (LF) lightning sferics measurements, more than 10 pairs of consecutive TGFs with synchronous LF lightning waveform are analyzed. Preliminary results show that the sferics of each TGF pairs are almost same, while they vary with different pairs. More details will be shown. In addition, some TGFs detected by ASIM and the associated lightning will also be introduced.

How to cite: Zhang, H., Qie, X., and Lu, G.: Low-frequency sferics associated with consecutive Terrestrial Gamma-ray Flashes , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20763, https://doi.org/10.5194/egusphere-egu24-20763, 2024.

Based on the work of several PhD students in Amsterdam, we now have a verified model for positive streamers in air. For streamer propagation a fluid model is sufficient, while for branching the discreteness of the photo-ionization events has to be taken into account. The model results on propagation and branching have both been validated on experiments in Eindhoven, and hence a few streamers can now be modeled quantitatively in 3D. However,  bursts or coronas with hundreds and more streamers are computationally not feasible. Instead of this, models of dielectric breakdown type should be developed, but based on the now known microscopic basis. We present two results in this direction: 1. The identification of steady positive and negative streamers and a revision of the concept of the stability field. 2. The analysis of streamer heads as coherent structures which allows a macroscopic characterization of the streamer head dynamics by few parameters such as radius, velocity, maximal and minimal field, ionization degree etc. (up to now only for positive streamers). Together with the branching simulations, these are stepping stones towards a reduced model of dielectric breakdown type for multi-streamer structures.

The models were developed and evaluated by the PhD students Dennis Bouwman, Hani Francisco, Baohong Guo, Xiaoran Li and Zhen Wang under the supervision of Jannis Teunissen and Ute Ebert in Amsterdam, and the experiments used for model validation were performed by Ph.D. students Siebe Dijcks and Yihao Guo under the supervision of Sander Nijdam in Eindhoven. For the reduced model, we collaborate with Alejandro Luque in Granada, Spain.

How to cite: Ebert, U.: Towards quantitative modeling of multi-streamer processes in 3D, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20811, https://doi.org/10.5194/egusphere-egu24-20811, 2024.

EGU24-20835 | ECS | Orals | NH1.5

3D Geolocation of Simulated Lightning Sources from Low-Earth Orbit 

Jackson Remington, Sonja Behnke, Harald Edens, Patrick Gatlin, Timothy Lang, Nikhil Pailoor, Mason Quick, and Sarah Stough

The recent removal of the Lightning Imaging Sensor from the International Space Station has left an observational gap in lightning detection from low-Earth orbit (LEO). However, new studies have demonstrated the potential for 3D geolocation of lightning sources using orbiting sensors. The Cubespark mission concept aims to take advantage of these developments by deploying a constellation of satellites with radio frequency (RF) sensors and optical imagers to not only map lightning locations, but also to collect bi-spectral flash images. These new capabilities include mapping storm charge structure, flash channel structure, and distinguishing microphysical processes throughout flash development, helping link microphysics and convective processes with overall flash and storm structure around the globe from LEO.

In this study, we simulate lightning RF sources in the very high frequency (VHF) band, extrapolate their signals to space-based detection using an improved ionospheric model, and reconstruct their 3D locations using a time-of-arrival (TOA) minimization algorithm. Various constellation configurations, locations, and atmospheric conditions are considered in order to identify and quantify the three main sources of geolocation error: geometric, ionospheric, and instrumental effects. The promising results of this study emphasize the potential of space-based 3D lightning mapping under diverse conditions. 3D resolution is shown to be better than 1-2 km in many cases, enabling new global applications in meteorology and climate sciences. Here we present a selection of these geolocation results as seen from space alongside recent advancements, paving the way for a future generation of LEO lightning mappers.

How to cite: Remington, J., Behnke, S., Edens, H., Gatlin, P., Lang, T., Pailoor, N., Quick, M., and Stough, S.: 3D Geolocation of Simulated Lightning Sources from Low-Earth Orbit, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20835, https://doi.org/10.5194/egusphere-egu24-20835, 2024.

EGU24-20882 | ECS | Orals | NH1.5

Mixed Mode of Charge Transfer During an Upward Positive Flash at Säntis Tower 

Toma Oregel-Chaumont, Antonio Šunjerga, Marcos Rubinstein, and Farhad Rachidi

The term “mixed mode of charge transfer to ground for initial continuous current (ICC) pulses” in the context of upward lightning flashes was first proposed by Zhou et al. 2011 [1] to describe fast pulses, distinct from the classical M-component mode of charge transfer, superposed on the slowly varying initial-stage current of upward negative flashes they observed at the Gaisberg Tower in Austria. The pulses in question were associated with leader/return-stroke processes occurring in decayed or newly created branches of the plasma channel connecting to the grounded, current-carrying channel, with junction points below the cloud base (height < 1 km) [1,2].

Herein, we report, to the best of our knowledge, the first observation of a mixed-mode-type pulse during the initial stage of an upward positive flash that was initiated from the Säntis Tower in Switzerland. The Mt. Säntis Lightning Research Facility, which recorded the flash, consists of a current measurement system installed in the mountaintop tower (2.5 km ASL), slow and fast electric field sensors and X-ray detectors 20 m from the tower base, an additional fast E-field sensor 15 km away, as well as full HD cameras and a high-speed camera (HSC) at various distances, among other systems (see Šunjerga et al. 2021 for details [3]).

The observed flash, categorized as a Type 1 from its current waveform (see Romero et al. 2013 for definition [4]), occurred at 16:24:03 UTC on July 24th, 2021, during the Laser Lightning Rod project [5]. Its “return stroke”-like main pulse was confirmed from HSC footage to have been triggered by a downward-connecting leader with a junction height of approximately 369±5 m AGL, well below the defined cut-off of 1 km. Interestingly, though the 12 kA peak current is reasonable for a mixed-mode pulse, the current and E-field risetimes were both >10 μs, more characteristic of a M-component-type ICC pulse [2].

These observations are important to improving our understanding of the charge transfer mechanisms in upward lightning flashes, which regularly damage wind turbines, telecommunications towers, and airplanes during take-off and landing.

 

References:

[1] Zhou, H., Diendorfer, G., Thottappillil, R., Pichler, H., Mair, M. (2011). Mixed mode of charge transfer to ground for initial continuous current pulses in upward lightning. In 2011 7th Asia-Pacific International Conference on Lightning (pp. 677–681). Chengdu, China: IEEE. https://doi.org/10.1109/APL.2011.6110212

[2] Zhou, H., Rakov, V. A., Diendorfer, G., Thottappillil, R., Pichler, H., Mair, M. (2015). A study of different modes of charge transfer to ground in upward lightning. Journal of Atmospheric and Solar-Terrestrial Physics, 125–126, 38–49. https://doi.org/10.1016/j.jastp.2015.02.008

[3] Šunjerga, A., Mostajabi, A., Paolone, M., Rachidi, F., Romero, C., Hettiarachchi, P., … Smith, D. (2021). Säntis Lightning Research Facility Instrumentation. International Conference on Lightning Protection, 6.

[4] Romero, C., Rachidi, F., Rubinstein, M., Paolone, M., Rakov, V. A., Pavanello, D. (2013). Positive lightning flashes recorded on the Säntis tower from May 2010 to January 2012: POSITIVE LIGHTNING SÄNTIS TOWER. Journal of Geophysical Research: Atmospheres, 118(23), 12,879-12,892. https://doi.org/10.1002/2013JD020242

[5] Houard, A., Walch, P., Produit, T., Moreno, V., Mahieu, B., Šunjerga, A., … Wolf, J.-P. (2023). Laser-guided lightning. Nature Photonics, 17(3), 231–235. https://doi.org/10.1038/s41566-022-01139-z

How to cite: Oregel-Chaumont, T., Šunjerga, A., Rubinstein, M., and Rachidi, F.: Mixed Mode of Charge Transfer During an Upward Positive Flash at Säntis Tower, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20882, https://doi.org/10.5194/egusphere-egu24-20882, 2024.

EGU24-20981 | Orals | NH1.5 | Highlight

Energy from extraterrestrial sources is driving arctic lightning 

Eija Tanskanen and Marzieh Khansari

The possible effect of solar activity on lightning has been studied for a long period of time. Specifically, the relationship between sunspot number and lightning activity has been investigated, although the results still remain inconclusive across regions and time. In some regions, a positive correlation is found, in others a negative one. Thus, it is important to explore other solar-geomagnetic variables possibly influencing lightning activity.

In order to examine the possible relationship between solar activity and lightning activity we will study lightning and geomagnetic activity at the latitudes of 50° to 70° together with the solar and solar wind observations (SDO, ACE, OMNI database).  Data from the Nordic lightning location system (NORDLIS) was used for lightning strikes and geomagnetic measurements from Sodankylä Geophysical Observatory, INTERMAGNET and IMAGE for geomagnetic disturbances. Our analysis showed a strong correlation between high-speed streams and lightning activity as well as with geomagnetic activity during solar cycle 23. All parameters peaked in 2003 during the early declining phase of solar cycle 23 and showed similar trends over the solar cycle. The correlation was strong and significant between latitudes 62° and 66°.  The best coupling was found at 63° and 65°, where solar wind variability explained 86% and 88% of the variability of lightning activity, respectively. We hypothesize that this correlation is because of a much larger number of energetic particles due to an exceptionally high number of HSS during solar cycle 23. Penetration of these highly energetic particles to the atmosphere and production of high energetic secondary electrons can lead to runaway breakdown in thunderclouds and initiation of lightning.

How to cite: Tanskanen, E. and Khansari, M.: Energy from extraterrestrial sources is driving arctic lightning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20981, https://doi.org/10.5194/egusphere-egu24-20981, 2024.

EGU24-21048 | Orals | NH1.5

Employing VLF and field mill measurements to predict lightning activity 

Moacir Lacerda and Carlos Augusto Morales Rodrigues

The STORM-T Laboratory of University of São Paulo (USP) – Brazil operates a VLF long range lightning detection network known as STARNET (Morales et al., 2014) and a local field mill network. We have developed and implemented two operational schemes to predict the thunderstorm activity and propagation for the next 30 minutes (Now-STARNET) and the probability of occurrence of lightning strikes in a local area within 10 minutes (YANSA – Lacerda et al., 2022). Now-STARNET scheme is based on the cell-tracking algorithm proposed by Betz et al. (2008) to identify active thunderstorms over South America (90-30W and 60S-10N). STARNET lightning measurements are hourly accumulated over grids of 0.1 x 0.1 degrees and those cumulative grids are used to identify active thunderstorms that are defined as contiguous lightning grids. For each identified thunderstorm, we retrieve the lightning activity every 1 minute and the area, speed and direction of propagation every 5 minutes. Based on these temporal and dynamical features we adjust polynomial functions to forecast the position of active thunderstorms (must have lightning activity in the last 5 minutes) for the next 30 minutes every 5 minutes. Finally, the projected areas are used to identify the Brazilian cities that will have lightning activity to issue warnings. YANSA tool uses the temporal variation of the vertical electrical field observed by field mills to compute the time between the first lightning pulse and the first cloud-to-ground stroke as defined by Rodrigues and Lacerda (2022). Based on the elapsed time and the magnitude of the electrical field, YANSA issues different warning messages (no-risk, low, moderate, high and extreme risk) that help the users to know the probability of CG occurrence and time spam for lighting activity. YANSA was configured to use 4 field mills deployed in the USP campus transmitting every 1 minute and issue warning of lightning activity in area of the university. For the conference we will present the skills of both Now-STARNET and YANSA tools in predicting lightning activity and lightning strikes by means of contingency table tests, i.e., POD, FAR and CSI. For Now-STARNET we will use STARNET measurements of 2022 and 2023 and explore how the skills change with thunderstorm size and location. For YANSA, we will use LINET and STARNET lightning strokes observed in the vicinity of the University of São Paulo during the period of 2023 to validate each message.

Rodrigues F. and M. Lacerda, "Warning of lightning risk for the first lightning produced by a thunderstorm using electric field mill network records," 2022 36th International Conference on Lightning Protection (ICLP), Cape Town, South Africa, 2022, pp. 720-723, doi: 10.1109/ICLP56858.2022.9942596.

Lacerda, M. Rodrigues, F., Verly, R., Morales C.A.R. (2023). Monitoring lightning activity by using the YANSA platform to emit warnings of lightning risk in real time with an electric field mill network. risk for the first lightning produced by a thunderstorm using electric field mill network records," 2022 36th International Conference on Lightning Protection (ICLP), Cape Town, South Africa, 2022,

 

How to cite: Lacerda, M. and Morales Rodrigues, C. A.: Employing VLF and field mill measurements to predict lightning activity, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21048, https://doi.org/10.5194/egusphere-egu24-21048, 2024.

Lightning is an essential climate variable that could be influenced by climate change processes. In this study, wintertime lightning data over the Mediterranean Sea (MS) during the period 2009-2019 from the World-Wide Lightning Location Network were analyzed together with corresponding observational and modeled data of solar activity, atmospheric dynamics and seawater chemistry. The results of this analysis demonstrate that solar activity is the dominant parameter that influences lightning activity over the MS. Where, wintertime lightning intensity and frequency for lightning with energy >0.5 MJ over the MS is 237 and 517 times greater during the solar maximum compared to the minimum, respectively. In contrast, lightning activity parameters have a significantly smaller dependence on climate change parameters, including convective available potential energy, seawater salinity, pH and total alkalinity. Therefore, it is highly unlikely that trends in lightning activity over the MS due to climate change will be detectable in the near future.

How to cite: Asfur, M., Price, C., and Silverman, J.: Is winter cloud-to-sea-surface lightning activity over the Mediterranean Sea during 2009-2019 strongly influenced by solar activity?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22350, https://doi.org/10.5194/egusphere-egu24-22350, 2024.

EGU24-22447 | ECS | Orals | NH1.5

3D location estimation of lightning charges using electrostatic field changes 

Sho Yui, Yukihiro Takahashi, and Mitsutero Sato

Floods caused by the development of cumulonimbus clouds cause significant damage, especially in tropical areas, such as the Southeast Asian region. Lightning strikes in cumulonimbus clouds have been shown to correlate with a time lag of several tens of minutes preceding heavy rainfall. Therefore, it is expected that lightning observations will help us to forecast heavy rainfall. Especially, if we could know the 3-dimensional distribution of lightning charges, this information might be a good proxy way of knowing thunderstorm development.  Here, we improved 3D estimation of lightning charges using electrostatic field measurement. In this method the electrostatic field changes caused by lightning stroke are observed with a network consisting  of sensors installed at multiple locations at about 5 km interval. Based on those data, three-dimensional location and amount of charges removed by lightning stroke can be estimated. A previous study using same kind of data conducted a brute force calculation, which is not practical because it takes about 2 minutes longer than the typical interval of lightning stroke in the active thunderstorm. In this study, we propose a new method using interpolation analysis by kriging, which results in significant reduction of the estimation time to about 8 seconds. This improving will allow us to analyse more data we took so far and make the new model of thunderstorms.

This research is supported by Science and Technology Research, Partnership for Sustainable Development (SATREPS), Japan Science and Technology Agency (JST) / Japan International Cooperation Agency (JICA).

How to cite: Yui, S., Takahashi, Y., and Sato, M.: 3D location estimation of lightning charges using electrostatic field changes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22447, https://doi.org/10.5194/egusphere-egu24-22447, 2024.

EGU24-4236 | Posters on site | AS1.15

JAXA Level-2 Algorithms, Validations and Applications Preparation for the EarthCARE 

Takuji Kubota and Hajime Okamoto

Earth Clouds, Aerosols and Radiation Explorer (EarthCARE) mission (Illingworth et al. 2015) is designed to produce the maximum synergetic collaboration of European and Japanese science teams (Wehr et al. 2023, Eisinger et al. 2024). The EarthCARE products will be developed and distributed from both JAXA and ESA. Continuous exchanges of information have been conducted between Japan and Europe through the Joint Algorithm Development Endeavor (JADE). CPR, ATLID and MSI Level-2 provide cloud mask, cloud phase and cloud microphysics (such as cloud effective radius, liquid water content, optical depth, etc) for the respective sensor products, together with the synergy products by using the combination of the sensors. Further, the CPR provides the Doppler velocity measurement (which gives the vertical information of the in-cloud velocity), and precipitation products. ATLID Level-2 includes aerosol flagging, aerosol component type (such as dust, black carbon, sea salt and water soluble), as well as the aerosol optical properties including aerosol extinction. The cloud and aerosol products will be used to derive the radiative flux at shortwave and longwave, whose consistency with the BBR will be checked to produce the final radiation product by 4-sensors.

EarthCARE synthetic data using a global storm-resolving (NICAM) and Joint-Simulator (Joint Simulator for Satellite Sensors) have been developed in Japan and used in the JAXA L2 algorithm developments (Roh et al. 2023).

Validation activities are necessary to distribute the scientific products whose quality and reliability are assured. The JAXA is planning the validation activities by utilization of the existing observation network, campaign observation, and cross comparison with other satellite data.

Furthermore, a wide range of application research activities will be planned to achieve the mission objectives. EarthCARE observation data will contribute to understanding cloud, aerosol, and radiation processes, evaluations and improvements of climate models and numerical weather prediction (NWP) models, and atmospheric quality monitoring. JAXA is conducting joint-works with universities and research institutes. The Intergovernmental Panel on Climate Change (IPCC) report published in August 2021, “Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the IPCC”, summarizes that the cloud feedback remains the largest contribution to overall uncertainty, and contributions to mitigate the uncertainty can be expected by new insights by the EarthCARE observations.

This presentation will introduce JAXA Level 2, Validation and Applications Preparation for the EarthCARE mission.

How to cite: Kubota, T. and Okamoto, H.: JAXA Level-2 Algorithms, Validations and Applications Preparation for the EarthCARE, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4236, https://doi.org/10.5194/egusphere-egu24-4236, 2024.

EGU24-8203 | Posters on site | AS1.15

Validation of EarthCARE Cloud and Precipitation Products through the WegenerNet 3D Open-Air Laboratory facilities 

Jürgen Fuchsberger, Andreas Kvas, Gottfried Kirchengast, Ulrich Foelsche, Esmail Ghaemi, Robert Galovic, Daniel Scheidl, and Christoph Bichler

The WegenerNet 3D Open-Air Laboratory for Climate Change Research, located in southeastern Austria in an area of about 22 km x 16 km around the city of Feldbach (46.93°N, 15.90°E), provides a unique setup for atmospheric monitoring and validation of satellite data products. Its 3D instrumentation consists of a polarimetric X-band Doppler weather radar, a microwave radiometer for vertical profiling of temperature, humidity, and cloud liquid water, an infrared cloud structure radiometer, and a water-vapor-mapping GNSS station network. These 3D sensors complement the high-density WegenerNet hydrometeorological ground station network, which is comprised of 156 stations measuring precipitation, temperature, humidity, and (at selected locations) wind as well as soil parameters.

This highly synergistic measurement setup enables robust internal cross-evaluation, calibration and quality control for obtaining reliable observations and derived WegenerNet data products. The 3D instrumentation is operational since mid-2021 and will provide a consistent validation reference data record throughout the EarthCARE mission lifetime. With its ground-based observations of cloud base height, melting layer base and top heights, liquid water content, precipitation rates, and hydrometeor classification, the WegenerNet contributes specifically to the validation of EarthCARE L2a and L2b cloud and precipitation data products. This presentation summarizes the validation preparation activities carried out so far, with focus on the EarthCARE validation rehearsal, and gives an outlook on the planned post-launch validation work during the actual cal/val phase.

How to cite: Fuchsberger, J., Kvas, A., Kirchengast, G., Foelsche, U., Ghaemi, E., Galovic, R., Scheidl, D., and Bichler, C.: Validation of EarthCARE Cloud and Precipitation Products through the WegenerNet 3D Open-Air Laboratory facilities, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8203, https://doi.org/10.5194/egusphere-egu24-8203, 2024.

EGU24-8361 | Posters on site | AS1.15

Harnessing the power of forward models: past, present and future 

Robin Hogan, Shannon Mason, Fabian Jakub, and Mark Fielding

Variational retrievals, data assimilation, and model evaluation in observation space, all rely on accurate instrument simulators, or forward models. The scientific challenge is to find innovative approximations to the radiative transfer that make them fast enough to use iteratively, while retaining accuracy. In this presentation I will summarize how the development of various forward models, particularly in the context of synergistic retrievals from EarthCARE and the A-Train, has the capability to reveal important cloud and precipitation properties that would otherwise remain hidden, and potentially even to develop new satellite concepts. For example, our radar and lidar “Multiscatter” model enables the extinction profile to be retrieved in ice and liquid clouds even in the presence of lidar multiple scattering.  Our “FLOTSAM” solar radiance model can work with profiles containing arbitrary combinations of particles, and surprisingly can help improve rain-rate retrievals by better providing the additional information needed to partition the radar path-integrated attenuation into the contributions from liquid clouds and rain. The Two-Stream Source Function (TSSF) infrared and microwave radiance model enables us to interpret 94-GHz brightness temperature, which provides important additional information on precipitating ice and liquid clouds.  I will end by presenting a new radiance model for cloud/storm-resolving models that can efficiently represent horizontal radiation transport between columns; this could enable future retrievals and assimilation to take full account of 3D radiative effects.

How to cite: Hogan, R., Mason, S., Jakub, F., and Fielding, M.: Harnessing the power of forward models: past, present and future, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8361, https://doi.org/10.5194/egusphere-egu24-8361, 2024.

EGU24-8607 | Posters on site | AS1.15 | Highlight

EarthCARE, ESA’s Cloud and Aerosol Mission, Preparing for Launch 

Thorsten Fehr, Dirk Bernaerts, Jonas von Bismarck, Patrick Deghaye, Michael Eisinger, Björn Frommknecht, Timon Hummel, Robert Koopman, Stephanie Rusli, and Kotska Wallace

The influence of clouds on incoming solar and reflected thermal radiation remains the largest contribution to the overall uncertainty in climate feedbacks due to the diverse cloud formation processes. Furthermore, climate models still show deficiencies in correctly representing aerosol-cloud interactions and precipitation patterns limiting the overall confidence in climate predictions.

Global observations of vertical cloud ice and liquid water profiles with simultaneous and collocated solar and thermal flux observation will provide crucial data to address this uncertainty. Furthermore, collocated global observation of vertical aerosol profiles and types are required to address their direct effects and indirect aerosol-cloud-interaction effects.

In response to these needs, the European Space Agency (ESA), in cooperation with the Japan Aerospace Exploration Agency (JAXA), plans to launch the Earth Cloud, Aerosol and Radiation Explorer Mission, EarthCARE – ESA’s Cloud and Aerosol mission – in May 2024.

The two active instruments embarked on the satellite, a cloud-aerosol lidar (ATLID) and a cloud Doppler radar (CPR), together with the passive multispectral imager (MSI) and broad-band radiometer (BBR), will provide synergistically derived vertical profiles of cloud ice and liquid water, aerosol type, precipitation, as well as heating rates, solar and thermal top-of-atmosphere radiances with the objective to reconstruct top-of-the-atmosphere short- and longwave fluxes at an accuracy of 10 Wm-2 on a 10 km×10 km scene. The mission aims to significantly improve our understanding in the cloud and aerosol radiative feedback mechanisms, and their representation in climate and weather forecasting models.

The presentation will provide an up-to-date overview of the mission and science status weeks before the planned EarthCARE launch on a Falcon-9 rocket beginning of May 2024 from Vandenberg, USA. It will cover the mission’s science objectives, main performances of the three ESA instruments, expected science advances and foreseen validation activities. A detailed presentation on the data products, ground processing and data quality assurance will be provided by T. Hummel et al. at EGU24.

How to cite: Fehr, T., Bernaerts, D., von Bismarck, J., Deghaye, P., Eisinger, M., Frommknecht, B., Hummel, T., Koopman, R., Rusli, S., and Wallace, K.: EarthCARE, ESA’s Cloud and Aerosol Mission, Preparing for Launch, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8607, https://doi.org/10.5194/egusphere-egu24-8607, 2024.

EarthCARE will continue the record of spaceborne radar, lidar, and radiometric measurements that was begun in 2006 by CloudSat, CALIPSO, MODIS and CERES within the A-Train of satellites. EarthCARE’s multispectral imager (MSI), three-view broadband radiometer (BBR), Doppler-capable cloud profiling radar (CPR) and high-spectral resolution atmospheric lidar (ATLID) provide some advances over the instruments within the A-Train, and the single platform will improve the coregistration of synergistic measurements. Ultimately, the greatest novelty of the EarthCARE mission may arise from its highly coordinated L2 production models, which cover products ranging from single-instrument detection, target classification, and retrieval products, to synergistic retrievals, radiative transfer modelling, and finally top-of-atmosphere radiative closure assessment. Central to the ESA L2 production model is the synergistic (ATLID-CPR-MSI) “best estimate” retrieval of all clouds, aerosols and precipitation in the atmosphere, called ACM-CAP.

ACM-CAP is based on the CAPTIVATE optimal estimation retrieval algorithm, which includes sophisticated and efficient representations of hydrometeor fallspeeds to constrain ice particle density and raindrop size, ice particle scattering properties, radar and lidar multiple scattering, passive solar, thermal and microwave radiances, and the HETEAC model for aerosol properties. To test our retrieval and enhance scientific continuity between EarthCARE and the A-Train, we have developed an equivalent CloudSat-CALIPSO-MODIS retrieval product, called CCM-CAP, based on the same retrieval algorithm. 

In this talk we provide an overview of the ACM-CAP product, its capabilities and its place within the EarthCARE ESA production model. Using CCM-CAP, we present case studies and evaluation of the retrieved cloud and precipitation properties, and discuss how the challenges for unified retrievals in complex and layered scenes will inform the regimes of interest for validation and evaluation once EarthCARE data are available.

How to cite: Mason, S., Hogan, R., Bozzo, A., and Courtier, B.: Synergistic and unified retrieval of clouds, aerosols and precipitation from EarthCARE and the A-Train: the ACM-CAP and CCM-CAP products, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8811, https://doi.org/10.5194/egusphere-egu24-8811, 2024.

EGU24-9113 | Orals | AS1.15

Illuminating the Interplay between Clouds, Aerosols, and Radiation: Introducing the ESA EarthCARE L2 processing chain. 

Gerd-Jan van Zadelhoff and David Donovan and the CARDINAL team

The interactions between clouds, aerosols, and solar and terrestrial radiation play  key roles in the Earth’s Climate. Despite a long history of satellite observations, further high-quality novel observations are needed for atmospheric model evaluation and process studies. It has been recognized that true height-resolved global observations of cloud and aerosol properties are essential for making progress. EarthCARE is an upcoming ESA/JAXA mission scheduled to fly in 2024, focusing on providing these observations.

Operating in a sun-synchronous orbit at 393 km altitude with a descending node at 14:00, EarthCARE's payload comprises two innovative active (Atmospheric UV High spectral resolution Lidar - ATLID and Cloud Profiling Doppler Radar - CPR provided by Japan) and two passive (Multi-Spectral Imager - MSI and Broad-Band Radiometer - BBR) instruments. Using these instruments, EarthCARE will provide global profiles of clouds, aerosols, and precipitation properties, along with co-located radiative TOA flux measurements. These atmospheric microphysical properties and associated radiative fluxes will be used to evaluate the representation of aerosols, clouds, and precipitation in weather forecast and climate models, contributing to the improvement of parameterization schemes. 

The ESA scientific retrieval processors fully exploit the synergy of these observations. EarthCARE will provide twenty-five science (Level 2) products. These products include nadir profiles of cloud, aerosol and precipitation properties along with constructed three-dimensional cloud-aerosol-precipitation domains and associated derived radiative properties, such as heating rates. The final L2 processor compares the forward modeled top-of-atmosphere broad-band radiances and fluxes based on the constructed 3D atmospheric scenes with those measured by the BBR in order to assess and improve the quantitative understanding of the role of clouds and aerosols in the Earth's radiation budget.

This presentation will provide an overview of the EarthCARE mission, its data processors and scientific products.

How to cite: van Zadelhoff, G.-J. and Donovan, D. and the CARDINAL team: Illuminating the Interplay between Clouds, Aerosols, and Radiation: Introducing the ESA EarthCARE L2 processing chain., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9113, https://doi.org/10.5194/egusphere-egu24-9113, 2024.

EarthCARE, equipped with a suite of passive and active sensors, including Cloud Profiling Radar (CPR), Atmospheric LIDar (ATLID), Multi-Spectral Imager (MSI), and Broad Band Radiometer (BBR), is designed for comprehensive studies of clouds, aerosols, precipitation, and their radiation impact. The CPR's Doppler capability is crucial for assessing the terminal velocity of rain and ice particles and understanding convective motions.

Global storm-resolving models (GSRMs, Satoh et al. 2019; Stevens et al. 2019) have been used to generate detailed simulations of mesoscale convective systems using a kilometre-scale horizontal grid. New observations, such as the Doppler velocity from EarthCARE, will provide new insights into the evaluation and improvement of a GSRM.

Moreover, the utilization of satellite simulators — comprehensive radiative transfer models designed to simulate satellite signals using outputs from atmospheric models like GSRMs — plays a crucial role in this process. These simulators are integral for assessing, enhancing, and aligning numerical models with satellite observation data.

This study investigates EarthCARE's potential to enhance GSRM evaluations and improvements using a satellite simulator. We also introduce our collaboration with a satellite remote sensing group in developing retrieval algorithms.

How to cite: Roh, W. and Satoh, M.: EarthCARE's Potential to Evaluate a Global Storm-Resolving Model Using a Satellite Simulator, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9144, https://doi.org/10.5194/egusphere-egu24-9144, 2024.

EGU24-9240 | Orals | AS1.15

EarthCARE Processors and Products: Status Update and Outlook 

Timon Hummel, Dirk Bernaerts, Jonas von Bismarck, Patrick Deghaye, Michael Eisinger, Thorsten Fehr, Björn Frommknecht, Robert Koopman, Stephanie Rusli, Vasileios Tzallas, and Kotska Wallace

The Earth Cloud Aerosol and Radiation Explorer (EarthCARE) is a satellite mission carried out by the European Space Agency (ESA) in collaboration with the Japan Aerospace Exploration Agency (JAXA) to measure global profiles of aerosol, cloud and precipitation properties along with radiative fluxes and derived warming rates, with the goal of advancing our understanding of cloud-aerosol and radiation interactions and the Earth's radiative budget.

In order to fulfil its objectives, the EarthCARE mission will collect co-registered observations from a suite of four instruments located on a common platform. The optical payload encompasses the three ESA instruments, namely an ATmospheric LIDar (ATLID), a Multi-Spectral Imager (MSI) and a BroadBand Radiometer (BBR). The fourth instrument, provided by JAXA, is the Cloud Profiling Radar (CPR). The two active instruments (ATLID and CPR) will provide vertical profiles of the atmosphere along the satellite nadir path. The two passive instruments (BBR and MSI) will provide scene context information to support the active instruments data interpretation.

The presentation will provide an update on the status of EarthCARE processors and products prior to launch, focusing on ESA's ground science data processing chain, which includes the production of calibrated instrumental data (Level 1 data products) and retrieved geophysical data (Level 2 data products). Further, we will introduce the Data Innovation and Science Cluster (DISC) for the mission exploitation phase (E2). The DISC brings together several groups of instrument and product experts in one cluster to establish a comprehensive product quality assurance framework, including activities related to product algorithm evolution, data assimilation, calibration, validation support, and performance monitoring of ESA's EarthCARE products.

How to cite: Hummel, T., Bernaerts, D., von Bismarck, J., Deghaye, P., Eisinger, M., Fehr, T., Frommknecht, B., Koopman, R., Rusli, S., Tzallas, V., and Wallace, K.: EarthCARE Processors and Products: Status Update and Outlook, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9240, https://doi.org/10.5194/egusphere-egu24-9240, 2024.

EGU24-10363 | ECS | Posters on site | AS1.15

Using AEOLUS Aerosol Assimilation to pave the way for EarthCARE 

Thanasis Georgiou, Athanasios Tsikerdekis, Konstantinos Rizos, Emmanouil Proestakis, Antonis Gkikas, Eleni Drakaki, Anna Kampouri, Holger Baars, Athena Augousta Floutsi, Eleni Marinou, Angela Benedetti, Will McLean, Christian Retscher, Dimitris Melas, and Vassilis Amiridis

EarthCARE, ESA’s and JAXA’s joint mission, is expected to launch in 2024 carrying ATLID, a high-spectral resolution lidar with depolarization capability. The instrument will provide valuable data for characterizing atmospheric aerosols and for improving atmospheric composition modelling. The aim of this study is to show how working with ESA’s Aeolus wind mission prepares us for taking advantage of ATLID.

Aeolus, which launched in 2018 and deorbited in 2023, was not specifically designed to observe aerosols but still provided aerosol products. Due to the lack of a cross-polar channel, it underestimated the aerosol-related backscatter by as much as 50% in scenes with non-spherical particles. During the ESA L2A+ project, an enhanced aerosol product was developed through data fusion with other data sources (such as NASA’s CALIPSO mission) to account for Aeolus deficiencies. The impact of this new product was assessed through assimilation experiments in regional NWP models, showing both the direct improvements of the new product, as well as the betterment of aerosol fields in regional models through assimilation of a profiling instrument. Our results were validated using data from the ESA-ASKOS tropical campaign, which took place in Cabo Verde during Summer and Autumn of 2021 and 2022.

The open-source tools created for Aeolus are further developed to support EarthCARE. Working with simulated data, we show the impact of ATLID profile assimilation on both the representation of aerosols in the model, as well as the impact on numerical weather prediction through radiative feedback. The experiments are done using the Weather Research and Forecasting (WRF) model, alongside the Data Assimilation Research Testbed (DART), with AEOLUS and EarthCARE support added.

The L2A+ team acknowledges support by ESA in the framework of the "Enhancing Aeolus L2A for depolarizing targets and impact on aerosol research and NWP project (4000139424/22/I-NS). This work was supported by computational time granted from the National Infrastructures for Research and Technology S.A. (GRNET S.A.) in the National HPC facility - ARIS - under project ID pr014048_thin.

How to cite: Georgiou, T., Tsikerdekis, A., Rizos, K., Proestakis, E., Gkikas, A., Drakaki, E., Kampouri, A., Baars, H., Floutsi, A. A., Marinou, E., Benedetti, A., McLean, W., Retscher, C., Melas, D., and Amiridis, V.: Using AEOLUS Aerosol Assimilation to pave the way for EarthCARE, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10363, https://doi.org/10.5194/egusphere-egu24-10363, 2024.

EGU24-11671 | Posters on site | AS1.15

Performance Analysis of the ATLID Lidar: A Multi-Parameter Statistical Approach Using L1 Data 

Artem Feofilov, Hélène Chepfer, and Vincent Noël

Recognizing the need for a comprehensive lidar system performance analysis, we present a method for day-to-day assessment of the ATLID/EarthCARE lidar system. Unlike traditional calibration/validation methods involving in situ measurements or comparisons with ground-based, air- and space-borne instruments, our approach dispenses with the need for a second instrument. Instead, we focus on stability control checks using the atmosphere and surface as a 'reference,' assuming their properties remain constant during the lidar mission's lifetime.

Leveraging L1 data flow, our method evaluates critical performance aspects, including the stability of ATLID channels, accuracy of cross-talk coefficients, and the consistency of day- and nighttime noise. Employing a clustering algorithm on scattering ratio histograms, we monitor radiation detection stability globally across diverse atmospheric scenarios.

Defining 11 parameters related to surface reflection, stratospheric noise, and scattering ratio histograms, we showcase the feasibility of our approach using CALIOP L1 data. We also present results from our analysis of simulated ATLID data, demonstrating the sensitivity of the proposed quality control indicators to various experimental issues.

How to cite: Feofilov, A., Chepfer, H., and Noël, V.: Performance Analysis of the ATLID Lidar: A Multi-Parameter Statistical Approach Using L1 Data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11671, https://doi.org/10.5194/egusphere-egu24-11671, 2024.

EGU24-12553 | Posters on site | AS1.15

Presenting lidar surface returns as Aeolus product with the outlook on future spaceborne lidar missions including EarthCARE and Aeolus-2  

Lev D. Labzovskii, Gerd-Jan van Zadelhoff, David P. Donovan, Jos de Kloe, L. Gijsbert Tilstra, Ad Stoffelen, Piet Stammes, and Damien Josset

We previously discovered the sensitivity of Aeolus lidar surface returns (LSR) to surface characteristics and reported very good agreement of LSR with Lambertian Equivalent Reflectances from passive remote sensing instruments for the first year of Aeolus on orbit. In this way, we provided the first evidence that active remote sensing can be used for retrieving unidirectional UV surface reflectivity. Here, as a continuation of this effort, we report the detailed methodological solutions for retrieving and evaluating LSR to be implemented as official L2A product during Phase-F of Aeolus project for its entire lifetime. Unlike our previous report that relied on detecting surface bin using our own methodology and assumptions, we now align the approach of detecting surface bins with the official Aeolus processing methodology for retrieving LSR and elaborate on the resultant differences. Besides that, we report how this successful application of atmospheric spaceborne lidar data for inferring land surface reflectivity properties can be translated for future lidar missions such as EarthCARE and Aeolus-2. On one hand, our results will briefly introduce all the details of the LSR retrieval for Aeolus with its unique and complex optical setup (highly-non nadir incidence and UV wavelength) for broad audience for the first time. On the other hand, we will shed light on the opportunities and challenges of LSR-alike retrievals for future lidar spaceborne missions, thereby trying to minimize the key methodological uncertainties associated with implementation of LSR algorithms.

How to cite: Labzovskii, L. D., van Zadelhoff, G.-J., Donovan, D. P., de Kloe, J., Tilstra, L. G., Stoffelen, A., Stammes, P., and Josset, D.: Presenting lidar surface returns as Aeolus product with the outlook on future spaceborne lidar missions including EarthCARE and Aeolus-2 , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12553, https://doi.org/10.5194/egusphere-egu24-12553, 2024.

EGU24-12971 | Orals | AS1.15

How lessons learned during previous validation campaigns are guiding our airborne validation of EarthCARE  

Florian Ewald, Silke Groß, Martin Wirth, and Julien Delanoe͏̈

Radar and lidar are valuable active remote sensing techniques to assess global ice cloud properties from space. Recent global climate model studies are increasingly relying on ice cloud products obtained from the synergy of the radar and lidar satellites in the A-Train constellation. For the first time, the upcoming ESA/JAXA satellite mission EarthCARE will acquire radar-lidar measurements from a single platform, ensuring the continuity of vertical resolved ice cloud products on a global scale. Due to additional and higher resolved measurements and a more comprehensive retrieval framework, a seamless transition from A-Train products cannot be taken for granted.  In this light and with the imminent launch of EarthCARE, it is now crucial to establish a validation strategy for the EarthCARE products using airborne measurements.

During the A-Train era, we learned numerous lessons and gained experience with coordinated aircraft and satellite underpasses which we performed during several airborne campaigns. During these exercises, the German research aircraft HALO was equipped with a EarthCARE-like payload consisting of a high spectral resolution lidar (HSRL) system at 532 nm, a high-power cloud radar at 35 GHz, a microwave radiometer package, and passive radiation measurements. Coordinated flights were performed with other airborne platforms carrying instruments at different wavelengths (DLR Falcon, Safire Falcon and ATR) or for validation with in-situ measurements (FAAM BAe-146) as well as below the A-Train satellite tracks.

In this presentation, we will give an overview of our lessons learned and how they are guiding our airborne validation strategy. Going along with the commissioning of EarthCARE, we will employ HALO with its remote sensing payload in the PERCUSION campaign later this year. Based from multiple locations in the tropical Atlantic (Cape Verde and Barbados) and Europe (Oberpfaffenhofen), underflights of EarthCARE will be performed. The comparison with the dataset acquired during A-Train underpasses will allow us to determine if derived cloud products can be directly compared or if conversions are necessary. By sharing our knowledge and plans with the wider community, we hope to foster helpful discussions to consolidate our airborne validation strategy for EarthCARE.

How to cite: Ewald, F., Groß, S., Wirth, M., and Delanoe͏̈, J.: How lessons learned during previous validation campaigns are guiding our airborne validation of EarthCARE , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12971, https://doi.org/10.5194/egusphere-egu24-12971, 2024.

EGU24-13604 | Orals | AS1.15

Radiative closure assessment using A-Train satellite data for the EarthCARE mission 

Zhipeng Qu, Jason Cole, Howard Barker, Meriem Kacimi, Shannon Mason, Robin Hogan, and Ben Courtier

The EarthCARE mission will perform continuous radiative closure assessment utilizing both 1D and 3D broadband (BB) radiative transfer (RT) models. The radiance and flux calculations from these models will be compared to observations obtained through EarthCARE's Broadband Radiometer (BBR). The inputs for the RT models will be derived from synergistic retrievals of cloud and aerosol properties, facilitated by the Clouds, Aerosol and Precipitation from Multiple Instruments using a Variational Technique (CAPTIVATE) algorithm. In preparation for the EarthCARE launch, this study involves the application of CAPTIVATE to A-Train data, with the resultant cloud, aerosol, and precipitation properties serving as inputs for the RT models. The outcomes of these models will be utilized in a radiative closure assessment, incorporating measurements from the Clouds and the Earth's Radiant Energy System (CERES). The analyses center on discerning differences between 1D and 3D RT calculations, as well as differences between RT calculations and measurements obtained from the CERES.

How to cite: Qu, Z., Cole, J., Barker, H., Kacimi, M., Mason, S., Hogan, R., and Courtier, B.: Radiative closure assessment using A-Train satellite data for the EarthCARE mission, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13604, https://doi.org/10.5194/egusphere-egu24-13604, 2024.

EGU24-14900 | Posters on site | AS1.15

EC-KLIM – Coordination of German EarthCARE Validation 

Sabrina Zechlau, Silke Groß, Ulla Wandinger, and Holger Baars

The joint ESA and JAXA Earth Explorer mission EarthCARE is designed to close gabs in the knowledge of aerosol, clouds and their interactions, and effects on radiation. For this the platform comprises four remote sensing instruments observing the vertical structure of the atmosphere with a highly spectral resolving lidar and a doppler cloud radar. Together with a hyperspectral imager radiation fluxes can be inferred and compared to the measurements of the on-board broad band radiometer. Based on these four instruments EarthCARE will provide over 40 data products, which are partly synergistic products of observations of all instruments. For the success of the mission it is therefore crucial to exactly validate the individual data products and to quantify their errors. A variety of observational sources are used, reaching from ground-based stations and networks to airborne measurements, and from satellite observations to modelled data. Already in the past a number of dedicated validation campaigns to prepare for validation from German research institutes were carried out with eg. the ground-based cloud observation system LACROS or with an EarthCARE-like payload on board the German research aircraft HALO. After launch a continuous validation of EarthCARE products will be necessary. For this the German Initiative for the Validation of EarthCARE (GIVE) bundles the expertise of the German atmospheric research community and aims at the validation of the entire chain of EarthCARE Level 1 and 2 products and the evaluation of related algorithms and instrument calibrations. The GIVE project will include dedicated campaigns as well as long‐term support over the lifetime of the mission. Here we want to introduce in general the German project EC-KLIM (former project office) to prepare for the use of EarthCARE, and especially of the GIVE project. We will present an overview of past preparation campaigns and of planned German validation activities.

How to cite: Zechlau, S., Groß, S., Wandinger, U., and Baars, H.: EC-KLIM – Coordination of German EarthCARE Validation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14900, https://doi.org/10.5194/egusphere-egu24-14900, 2024.

EGU24-15182 | ECS | Orals | AS1.15

Using synthetic EarthCARE Cloud Profiling Radar data to develop validation methodologies for ground-based cloud radar sites 

Lukas Pfitzenmaier, Nils Risse, Pavlos Kollias, Bernat Puigdomenech Treserras, and Imke Schirmacher

The value of permanent, multi-sensor surface-based observatories that collect continuous long-term observations for satellite L2 data products has grown significantly over the last 10-15 years. Examples of such established surface-based networks include the Aerosol, Clouds, and Trace Gases Research Infrastructure (ACTRIS) network, the US Department of Energy Atmospheric Radiation Measurements (ARM) observatories, and the recently established 94-GHz Miniature Network for EarthCARE Reference Measurements (FRM4Radar).

The core of the work presented is the use of the developed transformation of suborbital to orbital radar data by Orbital-Radar. This simple L1 transformational operator converts L1 suborbital (ground-based or airborne) measurements to the EarthCARE Cloud Profiling Radar (CPR) L1 observations. The transformational operator ensures that the orbital to suborbital comparison accounts for differences in the sampling geometry, measurement uncertainty, and instrument sensitivity and simulates the impact of the surface echo. Furthermore, the operator simulates the EarthCARE characteristic reflectivity and Doppler velocity errors.

Applying such a tool to long-time data sets allows to generate the optimal foundation for a statistical analysis of the EarthCARE CPR performance. Hence, the optimal sampling for CPR and ground-based data can be estimated, and the CPR detection of clouds and precipitation processes near the ground can be analyzed and evaluated. In addition, it shows how critical ground-based networks are and that they play an essential role in evaluating satellite measurements and products. Tools like Orbital-Radar may help evaluate future CPR satellite missions, expanding the L1 transformational operator to other spaceborne radar systems.

How to cite: Pfitzenmaier, L., Risse, N., Kollias, P., Puigdomenech Treserras, B., and Schirmacher, I.: Using synthetic EarthCARE Cloud Profiling Radar data to develop validation methodologies for ground-based cloud radar sites, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15182, https://doi.org/10.5194/egusphere-egu24-15182, 2024.

EGU24-16225 | Posters on site | AS1.15

The EarthCARE ATLID profile processors 

David Donovan, Gerd-Jan van Zadelhoff, and Ping Wang

ATLID (Atmospheric Lidar) is the lidar to be embarked on the Earth Clouds and Radiation Explorer (EarthCARE) mission. EarthCARE is a joint ESA-JAXA mission and will embark a cloud/aerosol lidar (ATLID), a cloud-profiling Radar (CPR) a multispectral cloud/aerosol imager (MSI) and a three—view broad-band radiometer (BBR). ATLID is a 355nm high-spectral-resolution, polarization sensitive lidar.

The accurate retrieval of aerosol and cloud properties from space-based lidar is a challenging endeavor, even when the extra information provided by an HSRL system is exploited. The generally low signal-to-noise (SNR) ratios involved coupled with the need to respect the structure of the aerosol and cloud fields being sensed are particular challenges.

Over the past several years, cloud/aerosol algorithms have been developed for ATLID that have focused on the challenge of making accurate retrievals of cloud and aerosol extinction and backscatter specifically addressing the low SNR nature of the lidar signals and the need for intelligent binning/averaging of the data. Two of these ATLID processors are A-FM (ATLID featuremask) and A-PRO (ATLID profile processor). A-FM uses techniques adapated from the field of image processing to detect the presence of targets at high resolution while A-PRO (using A-FM as input) preforms a multi-scale optimal-estimation technique in order to retrieve both aerosol and cloud extinction and backscatter profiles.

Adaptations of the A-FM and A-PRO processors have been developed for Aeolus (called AEL-FM and AEL-PRO, respectively) and have been introduced into the Aeolus L2a operational processor. In this presentation A-FM and A-PRO will be described. Results based on simulated data for A-FM and A-PRO and results using AEL-FM and AEL-PRO using Aeolus observations will be presented and discussed.

 

How to cite: Donovan, D., van Zadelhoff, G.-J., and Wang, P.: The EarthCARE ATLID profile processors, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16225, https://doi.org/10.5194/egusphere-egu24-16225, 2024.

EGU24-17361 | Posters on site | AS1.15

EarthCARE Cal/Val Campaigns - Overview 

Jonas von Bismarck, Robert Koopman, Stephanie Rusli, Malcolm Davidson, Dirk Bernaerts, Kotska Wallace, Thorsten Fehr, Timon Hummel, Vasileios Tzallas, Bjoern Frommknecht, and Michael Eisinger

 

The Earth Cloud Aerosol and Radiation Explorer (EarthCARE) is a satellite mission developed by the European Space Agency (ESA) in collaboration with the Japan Aerospace Exploration Agency (JAXA) to measure global profiles of aerosol, cloud and precipitation properties along with radiative fluxes and derived warming rates, with the goal of advancing our understanding of cloud-aerosol and radiation interactions and the Earth's radiative budget.  
 

Assuring the data quality of EarthCARE science products early after launch is an essential effort. This will be realised based on contributions from the independent EarthCARE validation team (ECVT) under coordination by ESA as well as monitoring-, calibration- and campaign activities performed under ESA (co-)management.  

 

An early focus to stabilize the data quality will be on airborne activities underflying the satellite with remote sensing and in-situ payloads. This will be done either in the context of larger science field campaigns or in individual activities, flanked by ground based measurement activities. 

 

The presentation will give an overview of ESA’s planned EarthCARE campaign activities, both directly implemented by ESA and in collaboration with science teams, and selected airborne and ground based instrument developments critical for the EarthCARE validation. 

How to cite: von Bismarck, J., Koopman, R., Rusli, S., Davidson, M., Bernaerts, D., Wallace, K., Fehr, T., Hummel, T., Tzallas, V., Frommknecht, B., and Eisinger, M.: EarthCARE Cal/Val Campaigns - Overview, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17361, https://doi.org/10.5194/egusphere-egu24-17361, 2024.

EGU24-17575 | Posters on site | AS1.15

Cloud and Precipitation Microphysical Retrievals from the EarthCARE Cloud Profiling Radar: The C-CLD Product 

Kamil Mroz, Bernat Puidgomènech Treserras, Alessandro Battaglia, and Pavlos Kollias

This presentation delves into the C-CLD processor and its output product, both named the same, developed for the EarthCARE mission. The C-CLD processor has been designed to extract detailed microphysical properties of clouds and precipitation from the EarthCARE Cloud Profiling Radar data. The algorithm introduces a significant advancement by incorporating Doppler velocity information for the first time in space-borne radar retrievals. Our approach integrates an optimal estimation method to deduce vertical profiles of hydrometeor water content and particle characteristic size, employing reflectivity, mean Doppler velocity measurements, and path-integrated attenuation. The algorithm's robustness is further amplified by an ensemble-based method in the ice regions, ensuring both accuracy and consistency in the forward model relations.

Emphasizing the algorithm's advancements, we present a comprehensive overview of its theoretical basis and development. This includes the validation process, performance sensitivity analysis and quantification of the information content. The presentation will demonstrate the retrieval efficacy in diverse atmospheric conditions, ranging from warm to cold rain and snow.

In addition to algorithmic developments, our research also emphasizes the importance of iterative testing and refinement. Our approach combines model simulations with actual campaign datasets, which include both in-situ and remote sensing measurements, to validate and refine our methods. The rigorous analysis of data from campaigns like CADDIWA or IMPACTS, provided insights that allowed us to improve the C-CLD algorithm, ensuring its robustness and improving the reliability of its retrievals.

How to cite: Mroz, K., Puidgomènech Treserras, B., Battaglia, A., and Kollias, P.: Cloud and Precipitation Microphysical Retrievals from the EarthCARE Cloud Profiling Radar: The C-CLD Product, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17575, https://doi.org/10.5194/egusphere-egu24-17575, 2024.

EGU24-17933 | ECS | Posters on site | AS1.15

Aerosol dust absorption - measurements with a reference instrument (PTAAM-2λ) and impact on the climate as measured in airborne  JATAC/CAVA-AW 2021/2022 campaigns 

Jesús Yus-Díez, Luka Drinovec, Marija Bervida, Uroš Jagodič, Blaž Žibert, Matevž Lenarčič, Eleni Marinou, Peristera Paschou, Nikolaos Siomos, Holger Baars, Ronny Engelmann, Arnett Skupin, Cordula Zenk, Thorsten Fehr, Andres Alastuey, Adolfo Gonzalez-Romero, Marco Pandolfi, Carlos Perez García-Pando, and Griša Močnik

Aerosol absorption coefficient measurements classically feature a very large uncertainty, especially given the absence of a reference method. The most used approach using filter-photometers is by measuring the attenuation of light through a filter where aerosols are being deposited. This presents several artifacts, with cross-sensitivity to scattering being most important at high single scattering albedo with the error exceeding 100%. 

We present lab campaign results where we have resuspended dust samples from different mid-latitude desert regions and measured the dust absorption and scattering coefficients, their mass concentration and the particle size distribution. The absorption coefficients were measured with two types of filter photometers: a Continuous Light Absorption Photometers (CLAP) and a multi-wavelength Aethalometer (AE33). The  dual-wavelength photo-thermal interferometer (PTAAM-2λ) was employed as the reference. Scattering coefficients were measured with an Ecotech Aurora 4000 nephelometer. The mass concentration was obtained after the weighting of filters before and after the sampling, and the particle size distribution (PSD) was measured by means of optical particle counters (Grimm 11-D).

Measurements of the scattering with the nephelometer and absorption with the PTAAM-2λ we obtained the filter photometer multiple scattering parameter and cross-sensitivity to scattering as a function of the different sample properties. Moreover, by determining the mass concentration and the absorption coefficients of the samples, we derived the mass absorption cross-sections of the different dust samples, which can be linked to their size distribution as well as to their mineralogical composition.

The focus of the JATAC campaign in September 2021 and September 2022 on and above Cape Verde Islands was on the calibration/validation of the ESA Aeolus satellite ALADIN lidar, however, the campaign also featured secondary scientific climate-change objectives. As part of this campaign, a light aircraft was set-up for in-situ aerosol measurements. Several flights were conducted over the Atlantic Ocean up to and above 3000 m above sea level during intense dust transport events. The aircraft was instrumented to determine the absorption coefficients using a pair of Continuous Light Absorption Photometers (CLAPs) measuring in the fine and coarse fractions separately, with parallel measurements of size distributions in these size fractions using two Grimm 11-D Optical Particle Size Spectrometers (OPSS). In addition, we performed measurements of the total and diffuse solar irradiance with a DeltaT SPN1 pyranometer.

The combination of the absorption and PSD with source identification techniques enabled the separation of the contributions to  absorption by dust and black carbon. The atmospheric heating rate of these two contributions was determined by adding the irradiance measurements. Therefore, the integration of the results from the Using laboratory resuspension experiments  to interpret the airborne measurements is of great relevance for the determination  of the radiative effect of the Saharan Aerosol Layer as measured over the tropical Atlantic ocean.

How to cite: Yus-Díez, J., Drinovec, L., Bervida, M., Jagodič, U., Žibert, B., Lenarčič, M., Marinou, E., Paschou, P., Siomos, N., Baars, H., Engelmann, R., Skupin, A., Zenk, C., Fehr, T., Alastuey, A., Gonzalez-Romero, A., Pandolfi, M., Perez García-Pando, C., and Močnik, G.: Aerosol dust absorption - measurements with a reference instrument (PTAAM-2λ) and impact on the climate as measured in airborne  JATAC/CAVA-AW 2021/2022 campaigns, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17933, https://doi.org/10.5194/egusphere-egu24-17933, 2024.

EGU24-18417 | ECS | Orals | AS1.15

Advancements in COSP Lidar Simulator Development for Aeolus Satellite Instrument and Future Applications for Earth Care 

Marie-Laure Roussel, Hélène Chepfer, Olivier Chomette, and Marine Bonazzola

The Aeolus mission,  conducted by the European Space Agency (ESA), relies on lidar technology to measure global wind profiles and observe Earth's atmosphere, and in particular clouds that will be the subject of special attention with the incoming Earth Care mission. In future climate predictions generated through climate models, clouds represent the greatest source of uncertainty. Therefore, it is crucial to study them, especially within the atmospheric column, as their vertical distribution has a radiative impact which is poorly known. Active lidar remote sensing technology onboard satellites is a valuable way of conducting measurements accross the atmosphere. However, cloud comparison between observational data and models is challenging due to differences in their definitions. To address this issue, a simulator is employed to model cloud-specific features as they would appear to a given instrument if it were flying over the modeled Earth.

This research initiative enhances the functionalities of the existing CFMIP Observation Simulator Package (COSP) (Bodas Salcedo, 2011). Our developments rest on the advancements achieved in adapting COSP for various satellite instruments in the past (Chepfer, 2006 & 2008) and its improvements over the years (Swales, 2018 - Bonazzola, 2023). The ongoing work focuses on refining the specifities of the current simulator to meet the unique requirements of the lidar of the Aeolus satellite and preparing thoses of ATLID onboard Earth Care satellite.

The success of this development is optimistic for the future creation of the simulator of the lidar of the Earth Care satellite that may be launched this year, showing the adaptability and versatility of this tool. Ultimately, these advancements contribute to the broader scientific community by providing a sophisticated tool for the analysis of satellite data and the validation of model predictions across various satellite missions (Cesana, 2013).

How to cite: Roussel, M.-L., Chepfer, H., Chomette, O., and Bonazzola, M.: Advancements in COSP Lidar Simulator Development for Aeolus Satellite Instrument and Future Applications for Earth Care, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18417, https://doi.org/10.5194/egusphere-egu24-18417, 2024.

EGU24-18425 | ECS | Posters on site | AS1.15

Aerosol Light Extinction Coefficient Closure - Comparison of Airborne In-situ Measurements with LIDAR measurements during JATAC/CAVA-AW 2021/2022 campaigns 

Marija Bervida Mačak, Jesús Yus-Díez, Luka Drinovec, Uroš Jagodič, Blaž Žibert, Matevž Lenarčič, Eleni Marinou, Peristera Paschou, Nikolaos Siomos, Holger Baars, Ronny Engelmann, Annett Skupin, Athina Augusta Floutsi, Cordula Zenk, Thorsten Fehr, and Griša Močnik

The JATAC campaign in September 2021 and September 2022 on and above Cape Verde Islands resulted in a large in-situ and remote measurement dataset. Its main objective was the calibration and validation of the ESA satellite Aeolus ALADIN Lidar. The campaign also featured secondary scientific objectives related to climate change. Constraining remote sensing measurements with those provided by in-situ instrumentation is crucial for proper characterization and accurate description of the 3-D structure of the atmosphere.

We present the results performed with an instrumented light aircraft (Advantic WT-10) set-up for in-situ aerosol measurements. Twenty-seven flights were conducted over the Atlantic Ocean at altitudes around and above 3000 m above sea level during intense dust transport events. Simultaneous measurements with PollyXT, and eVe ground-based lidars took place, determining the vertical profiles of aerosol optical properties, which were also used to plan the flights.

The aerosol light extinction coefficient was obtained at three different wavelengths as a combination of the absorption coefficients determined using Continuous Light Absorption Photometers (CLAP) and the scattering coefficients measured with an Ecotech Aurora 4000 nephelometer, which also measured the backscatter fraction. The particle size distributions above 0.3 µm diameter were measured with two Grimm 11-D Optical Particle Size Spectrometers (OPSS). Moreover, CO2 concentration, temperature, aircraft GPS position and altitude, air and ground speed were also measured.

We compare the in-situ aircraft measurements of the aerosol extinction coefficients with the AEOLUS lidar derived extinction coefficients, as well as with the ground-based eVe and PollyXT lidar extinction coefficients when measurements overlapped in space and time. The comparison was performed at the closest available wavelengths, with in-situ measurements inter/extrapolated to those of the lidar systems.

In general we find an underestimation of the extinction coefficient obtained by lidars compared to the in-situ extinction coefficient. The slopes of regression lines of ground-based lidars, PollyXT and eVe, against the in-situ measurements are characterised by values ranging from 0.61 to 0.7 and R2 between 0.71 and 0.89. Comparison further suggests better agreement between Aeolus ALADIN lidar and the in-situ measurements. Relationship described by fitting the Aeolus to in-situ data is characterised by the slope value 0.76 and R2 of 0.8.

The causes of better agreement of the in-situ measurements with the ALADIN lidar than with the surface based ones are being studied, with several reasons being considered: a) lower spatial and temporal resolution which homogenize the area of study in comparison with the very fine vertical variations of the aerosols, which can be detected with the surface-based measurements, impairing the comparison with highly vertically resolved ground-lidar measurements while not affecting averaged space-borne lidar; b) the effect of lower clouds/ Saharan air layers on the attenuation of the lidar signal.

The presented results show the importance of the comparison of the remote with in-situ measurements for the support of the research on evolution, dynamics, and predictability of tropical weather systems and provide input into and verification of the climate models.

How to cite: Bervida Mačak, M., Yus-Díez, J., Drinovec, L., Jagodič, U., Žibert, B., Lenarčič, M., Marinou, E., Paschou, P., Siomos, N., Baars, H., Engelmann, R., Skupin, A., Floutsi, A. A., Zenk, C., Fehr, T., and Močnik, G.: Aerosol Light Extinction Coefficient Closure - Comparison of Airborne In-situ Measurements with LIDAR measurements during JATAC/CAVA-AW 2021/2022 campaigns, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18425, https://doi.org/10.5194/egusphere-egu24-18425, 2024.

The National Research Institute for Earth Science and Disaster Resilience owns five scanning Ka-band cloud radars. Using these radars, we are planning to validate the cloud profiling radar (CPR) of the EarthCARE satellite. The EarthCARE CPR only observes along the line directly under the satellite path and has a return period of about 25 days. Therefore, we will facilitate the comparison by collecting data from what we can consider to be the similar region as the EarthCARE path. Statistical validation will be performed by creating a Contoured Frequency by Altitude/Temperature Diagram (CFAD/CFTD) and comparing the distributions. Both case and relatively long-term comparisons are possible. On the other hand, although the opportunity is rare, it is possible to compare the vertical profiles at the intersection with the vertical (RHI) observations of the ground-based radar if the EarthCARE path comes within the range of the ground-based cloud radar with the observation range of 30 km. Three-dimensional observations using Plan Position Indicator (PPI) scans can be used to generate data on the Cartesian grid (CAPPI data). From this CAPPI data, it is possible to create a vertical cross section along the path of the EarthCARE satellite. Because of the limited number of elevation angles, the comparison is relatively coarse in the vertical direction. Since this observation is not done in the vertical direction, only radar reflectivity is used for comparison, not Doppler velocity. Other methods of verifying liquid water contents using cloud radar and microwave radiometer are also under consideration.

How to cite: Ohigashi, T. and Misumi, R.: Ground-based scanning Ka-band cloud radar observations for validation of EarthCARE Cloud Profiling Radar (CPR), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18461, https://doi.org/10.5194/egusphere-egu24-18461, 2024.

EGU24-18670 | ECS | Posters on site | AS1.15

A sensitivity study using the ATLID lidar simulator and upcoming plans for the validation of EarthCARE mission 

Peristera Paschou, Eleni Marinou, Jos de Kloe, Dave Donovan, Gerd-Jan van Zadelhoff, Kalliopi-Artemis Voudouri, and Vassilis Amiridis

The Earth Clouds, Aerosol and Radiation Explorer (EarthCARE) is a joint mission of the European Space Agency (ESA) and the Japan Aerospace Exploration Agency (JAXA) for monitoring the aerosols, clouds, and precipitation, and for radiation closure studies. The Atmospheric Lidar (ATLID) is a High Spectral Resolution Lidar system and one of the four instruments that will be deployed onboard the platform. ATLID will use linearly polarized emission at 355 nm while pointing at 3o off-nadir and will detect the molecular (Rayleigh) and particulate (Mie) backscattered signals as well as the cross-polar component of the backscatter signals, aiming to provide profiles of the optical properties of aerosols and optically thin clouds such as the particle backscatter and extinction coefficients, and the depolarization ratio. In preparation for the calibration and validation (cal/val) activities that will be performed for ATLID upon the EarthCARE launch in May 2024, a lidar simulator tool (CARDINAL Campaign Tool; CCT) has been developed for providing realistic simulations of the ATLID lidar signals and the Level 1 (L1) products of the attenuated particulate (Mie) backscatter, the attenuated molecular (Rayleigh) backscatter, and the attenuated cross-polar backscatter. In brief, the CCT workflow includes the parametrization of an atmospheric scene with the use of model fields and/or measurements from airborne or ground-based lidars, a lidar radiative transfer model, and an instrument model based on the ATLID design. The CCT simulates the lidar signals that would be recorded from ATLID for the provided atmospheric scene and obtains the corresponding ATLID L1 products.

In this study, measurements of eVe lidar from the ASKOS campaign (Cabo Verde, 2021/2022), are used as an input in the simulator for obtaining realistic ATLID L1 profiles. eVe lidar is a combined linear/circular polarization Raman lidar operating at 355 nm for aerosol profiling and consists ESA’s ground reference system for the cal/val of the ESA Aeolus and EarthCARE missions. Several cases of different aerosol layers and cirrus clouds are investigated.

Furthermore, the simulated ATLID L1 profiles will be used in the Level 2A processing chain (A-PRO) to derive realistic profiles of the particle backscatter and extinction coefficients, and the linear depolarization ratio. The realistic ATLID L2A profiles will be compared with the corresponding L2 profiles from eVe lidar, aiming to investigate the detection sensitivity of ATLID products on real aerosol layers.

Upcoming plans for the validation of EarthCARE mission include the exploitation of eVe lidar in an overpass cross point. The key aspects of this validation will be presented. In brief, the system will undergo an upgrade to enhance its capabilities for the cal/val activities of EarthCARE mission, retaining its combined linear/circular configuration while incorporating state-of-the-art equipment tailored for measurements on multiple scattering effects and automations to enhance the measurement procedures.

How to cite: Paschou, P., Marinou, E., de Kloe, J., Donovan, D., van Zadelhoff, G.-J., Voudouri, K.-A., and Amiridis, V.: A sensitivity study using the ATLID lidar simulator and upcoming plans for the validation of EarthCARE mission, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18670, https://doi.org/10.5194/egusphere-egu24-18670, 2024.

EGU24-18904 | Posters on site | AS1.15

Use of EarthCARE products within the EUMETSAT validation facility for Level 2 Cloud products   

Loredana Spezzi, Alessio Bozzo, Phil Watts, John Jackson, and Andre Belo do Couto

The EUMETSAT central facility generates and disseminates several cloud products from both geostationary and low-Earth orbit passive sensors, which serve a variety of applications, spanning from nowcasting, to numerical weather prediction to climate monitoring. The retrieved cloud parameters include cloud/dust/ash detection, cloud top height and microphysics (particle effective radius and optical thickness). All EUMETSAT products are validated and continuously quality monitored against independent reference data to ensure state-of-the-art algorithm performance, product quality/accuracy compliant with user and operational service requirements, and stability and continuity/consistency over time (i.e., coping with instrument degradation, algorithm evolutions, updated calibration, etc.).

This contribution provides an overview of the tools developed at EUMETSAT to perform the monitoring and validation of cloud products against lidar/radar measurements, which have established themselves as a trustworthy source for the detection of cloud layers and superior to any other validation data source when it comes to estimate the cloud height, particle microphysical and optical properties. We focus on the status of these tools and the plans for their further development and release to users. The tools are fully automated and handle the validation of products from both geostationary and polar-orbiting satellites, including data download and organisation, instrument co-location and the development of comparison metrics. The toolkit includes:

  • A tool performing the validation of EUMETSAT against space-based radar and lidar measurements. For almost two decades (since 2006), the CloudSat and CALIPSO observations have been the prime reference source for this validation. EarthCARE will provide the natural continuation to the observations provided by these two instruments, which reached their end of life in autumn 2023. We discuss the use of EarthCARE products as envisaged in the validation activities with a particular focus on the retrieval of cloud properties based on the synergistic use of lidar, radar and multi-spectral imager data. Furthermore, the higher sensitivity measurements expected from HSRL and CPR on board EarthCARE with respect to CALIPSO and CloudSat will require careful investigations in order to transfer the current experience in the use of A-Train products as a validation reference to the new EarthCARE products.
  • A tool performing the validation of EUMETSAT cloud products against ground-based radar and lidar measurements from ACTRIS (the European Research Infrastructure for the observation of Aerosol, Cloud and Trace Gases), specifically using the cloud products generated by the ACTRIS-Cloudnet processing facility maintained by the Finnish Meteorological Institute (FMI). This validation activity fills in the gap between CloudSat/CALIPSO end of life and EarthCARE launch.
  • METIS-Clouds (Monitoring and Evaluation of Thematic Information from Space), a web application tool providing access to the collection of monitoring and validation results of EUMETSAT cloud products, on a global and regional level. This collection is exploited by both the in-house algorithm developers (to identify and fix issues, bugs, etc.) and the users (to assess the product accuracy).

How to cite: Spezzi, L., Bozzo, A., Watts, P., Jackson, J., and Belo do Couto, A.: Use of EarthCARE products within the EUMETSAT validation facility for Level 2 Cloud products  , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18904, https://doi.org/10.5194/egusphere-egu24-18904, 2024.

EGU24-20094 | Posters on site | AS1.15

Radiative fluxes estimation for the Broadband Radiometer (BBR) on EarthCARE: The BMA-FLX product 

Almudena Velazquez Blazquez, Carlos Domenech, Edward Baudrez, Nicolas Clerbaux, and Carla Salas Molar

The Broad-Band Radiometer (BBR) instrument on the EarthCARE satellite will provide accurate outgoing solar and thermal radiances at the Top of the Atmosphere (TOA) obtained in an along track configuration at three fixed viewing directions (nadir, fore and aft).

The operational BMA-FLX product on top-of-atmosphere radiative fluxes, is based on a radiance-to-flux conversion algorithm mainly fed by the unfiltered broad-band radiances, obtained in the BM-RAD product, auxiliary data from EarthCARE L2 cloud products and modelled geophysical databases. The conversion algorithm models the angular distribution of the reflected solar radiation and thermal radiation emitted by the Earth-Atmosphere system, and returns geometry independent flux estimates to be used for the radiative closure assessment of the Mission.

Different methodologies are employed for the solar and thermal BBR ADMs. Models for SW radiances are created for different scene types and constructed from Clouds and the Earth’s Radiant Energy System (CERES) data using a feed-forward back-propagation artificial neural network (ANN) technique. The LW angular models are derived through multiple regressions on brightness temperatures and brightness temperature differences of the multispectral imager (MSI) 10.8 µm and 12 µm channels, and corresponding LW fluxes obtained by using a large database of LibRadtran and SBDART radiative transfer simulations.

Both retrieval algorithms exploit the multi-viewing capability of the BBR by applying the radiance to flux conversion algorithms to each of the BBR views, which have been previously collocated at a reference level in order to minimize parallax effects. The reference height where the three BBR measurements are co-registered corresponds to the height where most reflection or emission takes place and depends on the spectral regime. LW observations are co-registered at the cloud top height while SW reference height is instead selected by minimizing the flux differences between nadir, fore and aft fluxes. The derived fluxes from the collocated views are then combined into a single flux value at the selected reference level.

Verification of the algorithms has been carried out using the 3 test scenes developed by the EarthCARE team using the Environment Canada and Climate Change’s Global Environmental Multiscale model (GEM). The BBR solar and thermal flux retrieval algorithms have been successfully employed to retrieve radiative fluxes over the 3 test scenes. Comparisons with the true fluxes from the GEM model provide RMSE < 5 W/m² for the LW fluxes and < 15 W/m² for the SW fluxes.

How to cite: Velazquez Blazquez, A., Domenech, C., Baudrez, E., Clerbaux, N., and Salas Molar, C.: Radiative fluxes estimation for the Broadband Radiometer (BBR) on EarthCARE: The BMA-FLX product, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20094, https://doi.org/10.5194/egusphere-egu24-20094, 2024.

EGU24-20108 | Posters on site | AS1.15

EarthCARE mission for global height-resolved cloud particle categories and vertical motion 

Kaori Sato and Hajime Okamoto

Improved representation of ice-phase processes in numerical models necessitates an enhanced understanding of ice-particle microphysics/radiative properties and their respective formation conditions. The EarthCARE JAXA L2 standard/research algorithms for clouds, precipitation and vertical motions aims at providing more detailed information of cloud particle categories and associated cloud microphysics/radiative properties from ATLID-CPR-MSI synergy. In particular, measurements from CPR Doppler and ATLID are expected to enable more comprehensive exploration of the relation between different cloud particle categories and the dynamical conditions. Based on complimentary information from long-term A-train data, the cloud particle category classification methodology planned for EarthCARE is tested and a new dataset has been developed. With this dataset, the geographical dependence of the occurrence of different ice cloud particle habit category and their properties that will be further investigated in detail from EarthCARE observations are discussed. Activities related to JAXA L2 validations from EU-Japan collaboration are developing new ways of combining ground-based active sensors and detailed surface observation of snow and rain to improve the quantification of precipitation and particle type retrievals. These studies would be valuable for further assessing the physical processes associated with cloud-precipitation formation from the EarthCARE mission.

How to cite: Sato, K. and Okamoto, H.: EarthCARE mission for global height-resolved cloud particle categories and vertical motion, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20108, https://doi.org/10.5194/egusphere-egu24-20108, 2024.

EGU24-20116 | Orals | AS1.15

PACE-PAX validation campaign – validating PACE and supporting Earthcare 

Ivona Cetinić, Kirk Knobelspiesse, Brian Cairns, and Monserrat Piñol Solé

NASA’s Plankton, Aerosol, Clouds and ocean Ecosystems (PACE) Mission, scheduled to be launched in early 2024, will produce a variety of ocean color, aerosol, cloud and land surface data products from its three sensors. Some of these products will be created with established ‘heritage’ algorithms, and others are new, representing recent algorithm development and the unique measurement capability of the PACE sensors. A crucial part of the validation activities is the PACE Postlaunch Airborne eXperiment (PACE-PAX), that is planned to occur in September of 2024. This dedicated field campaign, due to its platform and instrumental setup, offers an opportunity to support not only PACE, but the EarthCARE mission as well, opening opportunities for validation, new collaborations, and development of new algorithms for both Earth science missions.

How to cite: Cetinić, I., Knobelspiesse, K., Cairns, B., and Piñol Solé, M.: PACE-PAX validation campaign – validating PACE and supporting Earthcare, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20116, https://doi.org/10.5194/egusphere-egu24-20116, 2024.

EGU24-20244 | Posters on site | AS1.15

Development of JAXA L2 algorithms to retrieve cloud properties and vertical velocity for the EarthCARE mission 

Hajime Okamoto, Kaori Sato, Tomoaki Nishizawa, and Hiroaki Horie

JAXA L2-algorithms for cloud properties and vertical velocity were developed for the EarthCARE mission. CPR will be the first 94GHz Doppler cloud radar in space and ATLID is the 355nm-high spectral resolution lidar that can provide backscattering, extinction and depolarization ratio. The JAXA L2 standard cloud products will be derived by using (1) CPR-only algorithms without Doppler velocity, (2) CPR and ATLID algorithms and (3) CPR. ATLID and MSI algorithms. The JAXA L2 research product will be produced by using Doppler velocity (Vd) from CPR in addition to above. The products include cloud mask, cloud particle type, cloud particle categories, terminal velocity and vertical air motion. The L2 algorithms correspond to the extended version to those for CloudSat, CALIPSO (Hagihara et al., 2010 for cloud mask, Yoshida et al., 2010 and Kikuchi et al., 2017 for cloud particle type and Okamoto et al., 2010, Sato and Okamoto 2011 for cloud microphysics) and the latter has been distributed as JAXA EarthCARE A-train products. Vd may be affected by aliasing and the correction algorithm was developed. After the correction, Vd is effective to discriminate clouds and precipitation in cloud particle type products. It is also effective to specify the upward motion in convections. Cloud particle type algorithms for CPR use Vd and Ze for the better discrimination of clouds and precipitation. Two-dimensional diagram of lidar ratio and depolarization ratio from ATLID enables to retrieve ice particle categories (Okamoto et al., 2019, 2020, Sato and Okamoto 2023). The knowledge of particle categories reduce the uncertainties in the retrieved microphysics. Recently developed physical model (Sato et al., 2018) and vectorized physical model (Sato et al., 2019) were implemented into the algorithms to account multiple scattering contribution to the signals.

Synergetic ground-based observation system has been constructed in NICT Koganei, Tokyo. The ground-based system consists of 94GHz high-sensitivity-cloud radar (HG-SPIDER) and electric scanning cloud radar (ES-SPIDER), Multi-Field-of-view Multiple Scattering Polarization Lidar (Okamoto et al., 2016, Nishizawa et al., 2021), high spectral resolution lidar (Jin et al., 2020), direct-detection Doppler lidar (Ishii et al., 2022), coherent Doppler lidar (Iwai et al., 2013) and wind profiler. Cloud mask, particle type, cloud particle category, cloud microphysics, terminal velocity and vertical motion are retrieved by the system and can be used to evaluate L2 products.

How to cite: Okamoto, H., Sato, K., Nishizawa, T., and Horie, H.: Development of JAXA L2 algorithms to retrieve cloud properties and vertical velocity for the EarthCARE mission, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20244, https://doi.org/10.5194/egusphere-egu24-20244, 2024.

EGU24-22410 | Orals | AS1.15

Development of JAXA L2 algorithm to retrieve aerosol and cloud properties using ATLID and MSI 

Tomoaki Nishizawa, Rei Kudo, Eiji Oikawa, Akiko Higurashi, Yoshitaka Jin, Kaori Sato, and Hajime Okamoto

We have developed JAXA L2 algorithm to retrieve aerosol and cloud optical properties using data of 355nm high spectral resolution lidar (HSRL) with depolarization measurement function “ATLID” onboard EarthCARE satellite, to determine the global distribution of aerosols and clouds and to better understand cloud-aerosol interactions and their climate impacts. Using the three channel data of the ATLID, the developed algorithm estimates (1) extinction coefficient, backscatter coefficient and depolarization ratio of particles (aerosols and clouds) without assuming a particle lidar ratio, (2) identifies molecule-rich, aerosol-rich, or cloud-rich slab layers, (3) classifies particle type (e.g., dust and maritime), (4) retrieves planetary boundary layer height, and (5) estimates extinction coefficients for several main aerosol components such as dust, sea-salt, carbonaceous, and water-soluble aerosols using difference in depolarization and light absorption properties of the aerosol components. Furthermore, we have developed aerosol retrieval algorithm using both the ATLID and multi-spectral imager “MSI”. This algorithm retrieves vertically mean mode-radii for dust and fine-mode aerosols as well as the extinction coefficients for the four aerosol components using the three channels of the ATLID and radiances at 670nm and 865nm of MSI. The algorithms described above were developed based on our developed algorithm for the CALIOP and MODIS measurements. In the presentation, the overview of the algorithms and their performance will be described. In addition, related studies will be presented.

How to cite: Nishizawa, T., Kudo, R., Oikawa, E., Higurashi, A., Jin, Y., Sato, K., and Okamoto, H.: Development of JAXA L2 algorithm to retrieve aerosol and cloud properties using ATLID and MSI, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22410, https://doi.org/10.5194/egusphere-egu24-22410, 2024.

EGU24-22426 | Posters on site | AS1.15

Enhancing Satellite Validation in Antarctica: A Novel K2W Methodology for Comparing Ground-Based Measurements at K-band with Spaceborne Radar Observations Collected at W band 

Alessandro Bracci, Kaori Sato, Luca Baldini, Federico Porcú, Roberta Paranunzio, and Hajime Okamoto

Validating satellite measurements and geophysical retrievals is crucial for Earth observation missions, particularly in remote regions like Antarctica. This task faces challenges due to the harsh environment, logistical complexities, equipment maintenance, and operational costs. In Antarctica, where satellite observations play a pivotal role in estimating precipitation, validating satellite products through ground-based measurements is imperative but limited.

Cloud Profiling Radar (CPR) on NASA's CloudSat satellite provides reflectivity profiles at W-band (94 GHz), while the upcoming ESA/JAXA EarthCARE satellite will offer Doppler profiles in addition to reflectivity profiles. Despite efforts to enhance instrumentation for ice particle profiling at some Antarctic research stations, widely-used instruments include the Micro Rain Radar (MRR) and laser disdrometers.

This work introduces a novel validation methodology, K2W, which combines ground-based reflectivity profiles at K-band (24 GHz) from MRR and laser disdrometer observations. K2W enables the simulation of reflectivity and Doppler profiles at W-band, facilitating the validation of satellite-borne radar measurements at 94 GHz.

A comparison between CloudSat reflectivity profiles and K2W profiles during a satellite overpass at the Italian Antarctic station “Mario Zucchelli” revealed a mean difference of 0.2 dB at the lowest satellite radar range bin, with a time lag within ±12.5 min and distance within 25 km around the CloudSat overpass. Additionally, K2W simulated the 94 GHz Doppler velocity below 1 km altitude expected by EarthCARE, yielding a standard deviation of the simulated Doppler velocity less than 0.2 m s-1.

The use of simulated K2W profiles significantly enhances precipitation quantification over Antarctica and validates satellite measurements with reduced attenuation compared to ground-based W-band radar. K2W, utilizing MRR and disdrometer available at most Antarctic stations, broadens the scope for validation sites. The proposed methodology extends its applicability to assessing EarthCARE CPR Doppler velocity products and Level 2 standard precipitation products at various ground observation sites.

How to cite: Bracci, A., Sato, K., Baldini, L., Porcú, F., Paranunzio, R., and Okamoto, H.: Enhancing Satellite Validation in Antarctica: A Novel K2W Methodology for Comparing Ground-Based Measurements at K-band with Spaceborne Radar Observations Collected at W band, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22426, https://doi.org/10.5194/egusphere-egu24-22426, 2024.

EGU24-1493 | ECS | Posters on site | AS1.16

Overview of Secondary Ice Production In the Deep Convective Microphysics Experiment (DCMEX) 

Kezhen Hu, Gary Lloyd, HuiHui Wu, Keith Bower, Mike Flynn, Nike Marsden, Tom Choularton, Martin Daily, Ben Murray, Hugh Coe, Paul Connolly, Graeme Nott, Chris Reed, Waldemar Schledewitz, Martin Gallagher, and Alan Blyth

Secondary ice formation has long been a problem in cloud physics. This affects the radiation properties, precipitation development and the lifetime of mixed-phase clouds.  We conducted multiple flights over the Magdalena Mountain region in New Mexico to provide high-resolution information on the spatio-temporal distribution of ice phase evolution and the linkage between convective cloud thermodynamic and secondary ice processes. A combination of high-resolution cloud spectrometers (including 3VCPI, 2DS, HVPS, and CDP) were used to provide measurements of the evolution of cloud particle and precipitation concentrations, sizes, and morphology. Those data were used to identify and assess primary and secondary ice production (SIP) contributions compared with measured INP concentrations to characterise the frequency of SIP events, where precipitation particles first form and how they interact with cloud dynamics. The initial results suggest that most ice enhancement events in these clouds occurred in the temperature range of -5 °C to -10 °C, while occasionally even larger concentrations were observed between -22.5 °C and -25 °C. The results also show that observed secondary ice in the temperature range from -25 °C to -30 °C was more related to the updraft regions. The next step is to produce more detailed explanations and results by examining these data in conjunction with the cloud thermodynamic background.

 

How to cite: Hu, K., Lloyd, G., Wu, H., Bower, K., Flynn, M., Marsden, N., Choularton, T., Daily, M., Murray, B., Coe, H., Connolly, P., Nott, G., Reed, C., Schledewitz, W., Gallagher, M., and Blyth, A.: Overview of Secondary Ice Production In the Deep Convective Microphysics Experiment (DCMEX), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1493, https://doi.org/10.5194/egusphere-egu24-1493, 2024.

EGU24-1580 | ECS | Posters on site | AS1.16

Understanding the tropical high-cloud feedback through the ice-water-path lens 

Jakob Deutloff, Ann Kristin Naumann, Manfred Brath, and Stefan Buehler

The response of tropical high clouds to global warming has the potential to produce an important climate feedback but remains poorly constrained. To improve our understanding of the tropical high-cloud feedback, we develop a conceptual model of the high-cloud radiative effect as a function of the ice water path (IWP) and surface temperature. This model provides a framework for analysing how changes in IWP distribution and cloud top height with surface warming can generate a tropical high-cloud feedback. By including the entire IWP range, it improves on previous conceptual models that rely on cloud fractions. To parameterize our conceptual model, we use atmospheric profiles from global simulations with the ICOsahedral Nonhydrostatic weather and climate model (ICON) with 5 km horizontal resolution, which are used to calculate the radiative fluxes offline with the line-by-line Atmospheric Radiative Transfer Simulator (ARTS). This setup allows us to “switch off” the high clouds in the radiative transfer calculations to better study the radiative effect of high clouds over low clouds. Our conceptual model represents the main physical processes underlying the high-cloud radiative effect and is able to reproduce the results from the ARTS simulations. It therefore provides a valuable framework for analysing the tropical high-cloud feedback produced by climate models and helps to understand the origin of the associated uncertainties.

How to cite: Deutloff, J., Naumann, A. K., Brath, M., and Buehler, S.: Understanding the tropical high-cloud feedback through the ice-water-path lens, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1580, https://doi.org/10.5194/egusphere-egu24-1580, 2024.

EGU24-1986 | ECS | Posters on site | AS1.16

A systematic evaluation of high-cloud controlling factors 

Sarah Wilson Kemsley, Peer Nowack, and Paulo Ceppi

Clouds strongly modulate the top-of-the-atmosphere (TOA) energy budget. While most evidence indicates that changes in cloud-induced radiative anomalies at the TOA likely amplifies warming, the magnitude of this global cloud feedback remains highly uncertain. “Cloud Controlling Factor” (CCF) analysis is an approach that can be used to tackle this uncertainty, deriving relationships between large-scale meteorological drivers and cloud-radiative anomalies which can subsequently be used to constrain cloud feedback. However, the choice of meteorological controlling factors is crucial for a meaningful constraint, and while there is rich literature investigating ideal CCF setups for low-level clouds, there is a distinct lack of analogous research that explicitly targets high clouds.

Here, we use ridge regression to systematically evaluate CCFs that specifically target high cloud formation and cessation using historical data. We evaluate the addition of five candidate CCFs to previously established core CCFs within large spatial domains to predict longwave high-cloud radiative anomalies: upper-tropospheric static stability (SUT), sub-cloud moist static energy, convective available potential energy, convective inhibition, and upper-tropospheric wind shear. We identify an optimal configuration including SUT, and show that the spatial distribution of the  SUT  ridge regression coefficients are congruent with the physical drivers of known high-cloud feedbacks. We further deduce that inclusion of SUT into observational constraint frameworks may reduce uncertainty associated with changes in anvil cloud amount as a function of climate change. These results highlight upper-tropospheric static stability as an important CCF for high clouds and longwave cloud feedback, which we begin to explore using modelled data under an abrupt quadrupling of CO(abrupt-4xCO2).

How to cite: Wilson Kemsley, S., Nowack, P., and Ceppi, P.: A systematic evaluation of high-cloud controlling factors, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1986, https://doi.org/10.5194/egusphere-egu24-1986, 2024.

EGU24-3815 | ECS | Posters on site | AS1.16

Influence of contrasting sea surface temperature warming patterns on atmospheric circulation and cloud feedbacks 

Anna Mackie, Michael P. Byrne, and Andrew I.L. Williams

Climate sensitivity, defined as the global-mean surface temperature change due to a doubling of atmospheric CO2, is a key metric for quantifying the Earth system response to increasing greenhouse gases. Estimates of climate sensitivity vary widely, making it difficult for societies to prepare for the impacts of climate change. Uncertainty in climate sensitivity is driven primarily by uncertainty in how clouds will respond to warming. But how clouds respond to climate change depends strongly on the geographic pattern of warming: the so-called ‘pattern effect’. This recently-discovered phenomenon is crucial to narrowing uncertainty in climate projections, yet fundamental understanding of the processes underpinning the pattern effect is underdeveloped. In particular, the potential role of changes in atmospheric circulation as a crucial link between warming patterns and cloud feedbacks remains unclear. 

Here we use a series of idealised GCM simulations and a moist static energy (MSE) framework to investigate the coupling between tropical sea surface temperature (SST) warming, circulation changes and cloud feedbacks. In the simulations the SST of different ‘patches’ of the tropical ocean are perturbed, resulting in strongly non-linear cloud responses. We demonstrate that the circulation response is also non-linear and closely coupled to the cloud response. Specifically, SST warming in the west Pacific leads to a reduction in ascent fraction – the proportion of the atmosphere that is ascending at 500 hPa – over the tropical ocean, associated with an increased top-of-atmosphere shortwave cloud radiative effect.  In contrast, SST warming in the east Pacific has little effect on ascent fraction. We develop a framework for estimating ascent fraction as a function of near-surface MSE, inclusive of an entraining-plume model to account for dry-air mixing into moist ascending air. We demonstrate how this framework can provide insight into both the circulation changes associated with patterned SST warming and the resulting cloud feedbacks. 

How to cite: Mackie, A., Byrne, M. P., and Williams, A. I. L.: Influence of contrasting sea surface temperature warming patterns on atmospheric circulation and cloud feedbacks, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3815, https://doi.org/10.5194/egusphere-egu24-3815, 2024.

EGU24-4149 | Orals | AS1.16

Anvil cloud thinning in high-resolution models implies greater climate sensitivity 

Adam Sokol, Casey Wall, and Dennis Hartmann

Anvil clouds produced by tropical convection are expected to shrink in area as the climate warms, and the associated radiative feedback has long been the subject of controversy. In the World Climate Research Programme’s (WCRP) recent assessment of equilibrium climate sensitivity (ECS), the anvil area feedback was the least certain of any individual feedback process but was nevertheless estimated to be significantly negative. Here we show that such a negative feedback is not supported by an ensemble of high-resolution atmospheric models. On the contrary, the models suggest that changes in high cloud area and opacity act as a modest positive feedback. The positive opacity component arises from the disproportionate reduction in the area of thick, climate-cooling anvils relative to thin, climate-warming clouds. This suggests that thick cloud area is tightly coupled to the rate of convective overturning—which is expected to slow with warming—whereas thin cloud area is influenced by other processes. The cloud response is examined from a novel perspective that treats high ice clouds as part of an optical continuum as opposed to entities with fixed opacity. The positive feedback differs significantly from previous estimates and leads to a 0.3 °C increase in the WCRP estimate of ECS and a 10% widening of the likely range. We find that constraining the response of thin, high clouds in the Tropics to warming is critical for improved estimates of cloud feedback and global change.

How to cite: Sokol, A., Wall, C., and Hartmann, D.: Anvil cloud thinning in high-resolution models implies greater climate sensitivity, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4149, https://doi.org/10.5194/egusphere-egu24-4149, 2024.

EGU24-5449 | ECS | Posters on site | AS1.16

Secondary ice production within mixed-phase clouds in cold air outbreaks over the North Atlantic.  

Michael Biggart, Tom Choularton, Martin Gallagher, Keith Bower, Gary Lloyd, and Benjamin Murray

In-cloud measurements of ice crystal concentrations often greatly exceed ice nucleating particle (INP) concentrations. This discrepancy can be accounted for by secondary ice production (SIP), describing mechanisms producing new ice crystals from the presence of existing primary ice particles. As ice particle formation in clouds strongly influences precipitation and earth’s radiative balance, accurate representation of SIP processes is critical for global climate and weather prediction model simulations. However, the dominant SIP mechanisms operating in different cloud systems remain poorly understood. This study aims to improve understanding of SIP within mixed-phase clouds associated with cold air outbreaks (CAOs). We examine in-situ ice particle and ice-nucleating particle measurements made in October-November 2022, using the UK FAAM BAE 146 research aircraft, during a set of CAOs in the North-western Atlantic over the Labrador Sea. This flight campaign comprised part of the M-PHASE project, part of the NERC-funded CloudSense programme, which aims to reduce uncertainties in climate sensitivity due to clouds. Detailed measurements of cloud microphysical properties were made to study the evolution of stratocumulus clouds as they advect southwards before breaking up under increasingly convective conditions.

In-cloud ice crystal concentrations measured with 2D-S (size range 10 - 1280 μm) and HVPS (size range 150 μm - 19.2 mm) optical array probes frequently exceeded INP concentrations measured at the same temperature. Peak ice particle concentrations greater than 200 L-1 were recorded on numerous flights, several orders of magnitude above INP concentrations. These ice concentration enhancements were observed between -5 and -10 oC, within the active temperature range for the Hallett-Mossop SIP process. Analysis of corresponding ice particle imagery from the 2D-S and Cloud Particle Imager instruments shows that small hollow columns, often mixed with larger heavily rimed particles, were the dominant ice crystal habits, providing further evidence of rime splintering. A second ice concentration peak at around -17oC was also observed. Large irregularly shaped ice crystals were present during this period, suggesting that fragmentation due to ice-ice collisions may be another active SIP mechanism.

We identify a series of SIP events across the flight campaign, with their short-lived nature suggesting ice multiplication is active across limited spatial extents. These segments of elevated ice concentrations are heavily populated by ice crystals of diameter < 100μm. Overall, SIP is observed to increase across convective regions of the CAO, with stratocumulus regions upwind often consisting mainly of supercooled water.

This work provides critical information for numerical modelling studies requiring detailed representation of SIP processes within mixed-phase clouds across the transition region from stratocumulus to convective regimes in CAOs. 

How to cite: Biggart, M., Choularton, T., Gallagher, M., Bower, K., Lloyd, G., and Murray, B.: Secondary ice production within mixed-phase clouds in cold air outbreaks over the North Atlantic. , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5449, https://doi.org/10.5194/egusphere-egu24-5449, 2024.

EGU24-6008 | Orals | AS1.16

Spontaneous formation of OH radical and H2O2 at the liquid-ice interface 

Junwei Song and Christian George

Recently, intensive new particle formation (NPF) events have been observed in the upper troposphere/lower stratosphere (UTLS), where ice formation is predominant. Atmospheric oxidants including hydroxyl radical (OH∙) and hydrogen peroxide (H2O2) play important roles in these NPF events. However, the underlying formation mechanisms of OH∙ and H2O2 remain poorly understood. Here we propose that spontaneous formation of OH∙ and H2O2 is occurring at the liquid-ice interface during ice freezing, acting as so far unconsidered source of oxidants in the UTLS. This production is induced by the Workman-Reynold effect which predicts that a freezing potential appears in a freezing salt solution and thus an electric field is formed at the liquid-ice interface.

In this work, solutions containing disodium terephthalate (TA, ~5 x 10-5 M) were frozen either by immersion into an ethanol bath (-20 ºC) or into liquid nitrogen, and then melted. These steps were repeated creating freezing-melting cycles (n = 0-25). The solutions were then analyzed by a fluorescent spectroscopy to monitor the formation of 2-hydroxyterephthalic acid (TAOH), a product of the reaction of TA with OH∙. The production of TAOH was observed to be positively correlated with the number of freezing-melting cycles, demonstrating the formation of OH∙ during the freezing process. A series of salt solutions containing either NaCl, NH4Cl, NaBr, NaI, NaIO3 at different concentrations i.e.,10-6-100 M were also frozen and melted, and analyzed for their content in H2O2. Also here, our results confirmed the H2O2 production at the liquid-ice interface for the freezing salt solutions. In the case of NaCl, the maximum H2O2 production was observed at the concentration of ~10-4 M. Furthermore, the production rate of H2O2 at the NaCl concentration range of 10-4-10-2 M, was in agreement with the known Workman-Reynold freezing potential values. In order to investigate the role of OH∙ recombination in the H2O2 formation, mixed solutions of NaCl (~10-4 M) and TA (~5 x 10-5 M) subjected to different freezing-melting cycles were analyzed. The production rate of H2O2 was higher than that of TAOH by a factor of ~65, suggesting less significant effect of TA as a OH∙ scavenger on H2O2 formation. Overall, our experimental results provide direct evidence that OH∙ and H2O2 are formed spontaneously at the liquid-ice interface due to the Workman-Reynold effect. This study could improve our ability to describe the multiphase oxidation processes of the UTLS regions.

How to cite: Song, J. and George, C.: Spontaneous formation of OH radical and H2O2 at the liquid-ice interface, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6008, https://doi.org/10.5194/egusphere-egu24-6008, 2024.

EGU24-8440 | ECS | Orals | AS1.16

Plant pollen and spores as sources of ice nucleating particles 

Nina L. H. Kinney, Matthew I. Gibson, Daniel Ballesteros, and Thomas F. Whale

Soluble molecules released from plant pollen can nucleate ice from supercooled water and are an enigmatic source of atmospherically relevant biological ice nucleators. Recently, it has been highlighted that ice nucleating particles from pollen may possess greater potential to impact cloud glaciation than previously considered, as fragments generated by pollen bursting under atmospheric conditions could act as carriers of ice nucleating molecules, with significantly longer residence times than whole pollen grains1,2. Previous studies have indicated a range in ice nucleation activity across pollen samples, but still relatively little is known about the structure of the molecules responsible or the basis for this variability3,4.

Our collaboration with the Royal Botanic Gardens, Kew, UK has enabled the collection of over fifty pollen samples from across taxa, from representatives with different pollination methods, pollination times and growth climates. Immersion mode ice nucleation experiments reveal that the ice nucleation ability of pollen is highly diverse; amongst our collections we identify particularly active samples (mean freezing temperature of microlitre droplets, T50 = -7.6 °C for Pinus mugo pollen solution) and others with far lower activity (T50 = -23.8 °C for Musa rubra pollen solution). Examining the relationship between this activity and selected characteristics, no dependency on various plant and pollen features could be determined, which may indicate that the ice nucleating molecules from pollen fulfil a distinct biological function and nucleate ice incidentally.

Looking to earlier diverging plant lineages, we tested the activity of fern spores and find that they also release molecules in water which can nucleate ice. These ice nucleating molecules demonstrate absorbances consistent with polysaccharides from pollen. Ferns colonise diverse habitats and their spores, primarily transported by wind, are present in quantities comparable to pollen grains in the air over vegetated regions5. Better understanding these potential sources of atmospheric ice nuclei is essential for improving climate model prediction of their impacts. Our results suggest that these ice nucleating molecules evolved prior to the divergence of seed plants and are conserved in the spores and pollen of extant plants across the phylogeny.

References

1. Burkart, J., Gratzl, J., Seifried, T., Bieber, P. & Grothe, H. Subpollen particles (SPP) of birch as carriers of ice nucleating macromolecules. Biogeosciences Discuss. 1–15 (2021).

2. Werchner, S. et al. When Do Subpollen Particles Become Relevant for Ice Nucleation Processes in Clouds? J. Geophys. Res. Atmos. 127, e2021JD036340 (2022).

3. Pummer, B. G., Bauer, H., Bernardi, J., Bleicher, S. & Grothe, H. Suspendable macromolecules are responsible for ice nucleation activity of birch and conifer pollen. Atmos. Chem. Phys. 12, 2541–2550 (2012).

4. Dreischmeier, K., Budke, C., Wiehemeier, L., Kottke, T. & Koop, T. Boreal pollen contain ice-nucleating as well as ice-binding ‘antifreeze’ polysaccharides. Sci. Rep. 7, 1–13 (2017).

5. Després, V. R. et al. Primary biological aerosol particles in the atmosphere: A review. Tellus, Ser. B Chem. Phys. Meteorol. 64, (2012).

How to cite: Kinney, N. L. H., Gibson, M. I., Ballesteros, D., and Whale, T. F.: Plant pollen and spores as sources of ice nucleating particles, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8440, https://doi.org/10.5194/egusphere-egu24-8440, 2024.

EGU24-8556 | ECS | Posters on site | AS1.16

Climate sensitivity, the pattern effect, and cloud parametrisation 

Kai-Uwe Eiselt and Rune Grand Graversen

Climate sensitivity changes over time in numerical global climate models (GCMs) due to a so-called “pattern effect”. That is, surface-warming patterns evolve over time to favour different geographical regions giving rise to different climate feedbacks, thus changing climate sensitivity over time.

One of the most important climate feedbacks is the cloud feedback and it has been shown that the pattern effect may strongly impact the strength of this feedback in GCMs. Here we perform slab-ocean model simulations with different versions of the Community Earth System Model (CESM). Different patterns of ocean heat transport convergence (Q-flux) are prescribed, inducing different patterns of surface warming. Notably, the prescribed Q-flux changes average to zero in the global mean, thus introducing no net forcing. We show that (1) net-zero forcing Q-flux changes can have surprisingly large effects on the climate, (2) that the impact strongly depends on the geographic pattern of the Q-flux change and, (3) that different cloud parametrisations may imply different impacts of the same patterns.

While these results may have important implications for the quantification of the pattern effect and climate sensitivity in climate models, we caution against overinterpretation, as preliminary experiments with fully coupled models indicate a weaker sensitivity to similar pattern changes.

How to cite: Eiselt, K.-U. and Graversen, R. G.: Climate sensitivity, the pattern effect, and cloud parametrisation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8556, https://doi.org/10.5194/egusphere-egu24-8556, 2024.

EGU24-8877 | Posters on site | AS1.16

Searching for the atomic scale mechanism of ice nucleating particles: hydration layer structures on K-Feldspar microcline surfaces from a combination of atomistic simulation and atomic force microscopy 

Bernhard Reischl, Rasmus Nilsson, Adam Foster, Franziska Sabath, Tobias Dickbreder, Ralf Bechstein, and Angelika Kühnle

Ice and mixed-phase clouds can form at moderate supercooling on seed particles through heterogeneous ice nucleation, but despite numerous experimental and computational investigations, understanding heterogeneous ice nucleation remains one of the great challenges in atmospheric science. While feldspar mineral dust particles have been identified as particularly good ice nucleating particles, they can exhibit different chemical composition and crystal structure, making it difficult to determine the atomistic details of the ice nucleation mechanism, both experimentally, and computationally. Here, we present systematic atomistic molecular dynamics studies of hydration layer structures at the interfaces of K-feldspar maximum microcline (001), (010), and (100) surfaces and water, at room temperature and moderate supercooling. Simulations on the fully hydroxylated α-terminated (001) cleavage plane reveal a complex lateral structure in the first water layer and a less ordered second layer. At room temperature, water exchange within the first hydration layer and between the first and second hydration layers occurs on a sub-nanosecond timescale. We also observe that surface potassium ions can go into solution and return to vacant surface sites on a timescale of tens of nanoseconds, but this causes surprisingly minor perturbations within the first hydration layer if the sampling time is sufficient. Hydration layer structures from simulation are in very good agreement with 3D atomic force microscopy data recently obtained for the first time on a freshly cleaved microcline surface in pure water (Dickbreder et al., 2024) – validating the accuracy of the atomistic model and providing an interpretation of the experimental data. However, the simulated hydration layer structures on the low energy (001) or (010) surfaces do not exhibit a lattice match with faces of cubic or hexagonal ice. Only the higher energy (100) surface with slightly strained lattice parameters can stabilize an ice interface at moderate supercooling in the simulations. Our results confirm previous findings (Kiselev et al., 2017; Soni and Patey, 2019) and indicate that the good ice nucleating properties of feldspars likely result from more complex active sites, possibly involving changes in surface chemistry, or topographic features such as defects, strained lattices, or step edges, which we are currently investigating.

Dickbreder, T., Sabath, F., Reischl, B., Nilsson, R. V. E., Foster, A., Bechstein, R. and Kühnle, A.: Atomic structure and water arrangement on K-feldspar microcline (001), accepted in Nanoscale, DOI:10.1039/d3nr05585j, 2024.

Kiselev, A., Bachmann, F., Pedevilla, P., Cox, S. J., Michaelides, A., Gerthsen, D., and Leisner, T.: Active sites in heterogeneous ice nucleation—the example of K-rich feldspars, Science, 355, 367–371, 2017.

Soni, A. and Patey, G. N.: Simulations of water structure and the possibility of ice nucleation on selected crystal planes of K-feldspar, J. Chem. Phys., 150, 214501, 2019.

How to cite: Reischl, B., Nilsson, R., Foster, A., Sabath, F., Dickbreder, T., Bechstein, R., and Kühnle, A.: Searching for the atomic scale mechanism of ice nucleating particles: hydration layer structures on K-Feldspar microcline surfaces from a combination of atomistic simulation and atomic force microscopy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8877, https://doi.org/10.5194/egusphere-egu24-8877, 2024.

EGU24-10690 | Posters on site | AS1.16

Radiative effect of thin cirrus clouds in the extratropical lowermost stratosphere and tropopause region 

Reinhold Spang, Rolf Müller, and Alexandru Rap

Cirrus clouds play an important role in the radiation budget of the Earth; nonetheless, the radiative effect of ultra thin cirrus clouds in the tropopause region and in the lowermost stratosphere remains poorly constrained. These clouds have a small vertical extent and optical depth, and are frequently neither observed even by sensitive sensors nor considered in climate model simulations. In addition, their shortwave (cooling) and longwave (warming) radiative effects are often in approximate balance, and their net effect strongly depends on the shape and size of the cirrus particles. However, the CRyogenic Infrared Spectrometers and Telescopes for the Atmosphere instrument (CRISTA-2) allows ultra thin cirrus clouds to be detected. Here we use CRISTA-2 observations in summer 1997 in the northern hemisphere midlatitudes together with the Suite Of Community RAdiative Transfer codes based on Edwards and Slingo (SOCRATES) radiative transfer model to calculate the radiative effect of observed ultra thin cirrus.
Using sensitivity simulations with different ice effective particle size and shape, we provide an estimate for the uncertainty of the radiative effect of ultra thin cirrus in the extratropical lowermost stratosphere and tropopause region during summer and - by extrapolation of the summer results - for winter.
Cloud top height and ice water content are based on CRISTA-2 measurements, while the cloud vertical thickness was predefined to be 0.5 or 2 km. Our results indicate that if the ice crystals of these thin cirrus clouds are assumed to be spherical, their net cloud radiative effect is generally positive (warming). In contrast, assuming aggregates or a hexagonal shape, their net radiative effect is generally negative (cooling) during summer months and very likely positive (warming) during winter. The radiative effect is in the order of +/-(0.1-0.01) W/m2 for a realistic global cloud coverage of 10%, similar to the magnitude of the contrail cirrus radiative forcing (of ~0.1 W/m2). The radiative effect is also dependent on the cloud vertical extent and consequently the optically thickness and effective radius of the particle size distribution (e.g. effective radius increase from 5 to 30~microns results in a factor ~6 smaller long and shortwave effect respectively). The properties of ultrathin cirrus clouds in the lowermost stratosphere and tropopause region need to be better observed and ultra thin cirrus clouds need to be evaluated in climate model simulations.

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How to cite: Spang, R., Müller, R., and Rap, A.: Radiative effect of thin cirrus clouds in the extratropical lowermost stratosphere and tropopause region, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10690, https://doi.org/10.5194/egusphere-egu24-10690, 2024.

EGU24-11003 | Posters on site | AS1.16

Two years of aerosol and cloud observations from the Antarctic Peninsula. 

Tom Lachlan-Cope, floortje van den Heuvel, Michael Flynn, Joanna Dyson, and Daniel Smith

Clouds over the Southern Ocean and Antarctica are poorly represented within climate models. It is thought that our poor understanding of aerosol-cloud interaction at these latitudes could play a major role in biasing models towards consistently underpredicting cloud formation in these regions. Unfortunately, there are few studies of aerosols and their impact on clouds at high southern latitudes and those that do exist concentrate on the summer period. Here we present two years of observations from Rothera Station on the Antarctic Peninsula.

 

The East Beach Hut clean air facility at Rothera Station has a comprehensive set of online aerosol instruments measuring size, composition, and the capacity to act as a Cloud Condensation Nuclei (CCN) in addition to offline filter samplers from which the concentration of Ice Nucleating Particle (INP) can be derived. Measurements of the aerosol precursor gas, dimethly sulphide are also available in addition to a micropulse LiDAR to give information on cloud properties. The object of these measurements is to identify the composition and source of the cloud nuclei active at high latitudes so they can be correctly incorporated within climate models through new or revised parameterisations.

 

Here we report on the first two years of measurements and identify the correlation between chemical composition, biological activity and cloud nuclei activation.. We will present a comparison of aerosol and cloud nuclei during the Antarctic Summer and Winter of 2021-2023, offering an initial assessment of the different sources of CCN and INP observed during these periods.

How to cite: Lachlan-Cope, T., van den Heuvel, F., Flynn, M., Dyson, J., and Smith, D.: Two years of aerosol and cloud observations from the Antarctic Peninsula., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11003, https://doi.org/10.5194/egusphere-egu24-11003, 2024.

EGU24-11320 | ECS | Posters on site | AS1.16

The impact of mixed-phase cloud processes on radiative fluxes over the Southern Ocean in a convection-permitting model 

Daniel Smith, Ian Renfrew, Floor van den Heuvel, Tom Lachlan-Cope, Ian Crawford, Keith Bower, and Mike Flynn

Atmospheric and climate models have large biases in their short and long wave radiative fluxes over the Southern Ocean, leading to significant errors in their sea surface temperature, sea ice and large scale circulation. The primary cause for these biases is the representation of low-level clouds, both at the macro- and micro-scale. We assess the performance of a convection-permitting configuration of the Met Office Unified Model (MetUM) over the Southern Ocean using satellite and aircraft observations from the 2023 special observing period of the Southern Ocean Clouds (SOC) field experiment. We focus on the model’s sensitivity to the microphysics schemes. Firstly, the impact of ice nucleating particles (INP) parametrizations via sensitivity experiments using different temperature dependent INP distributions: (i) from Cooper (1986); (ii) as derived for the east Antarctic coast; and (iii) a new distribution derived from observations from the west Antarctic Peninsula during the SOC experiment. Secondly, we examine the impact of the parameterized overlap between ice and water within a grid box (the mixed-phase overlap factor), which modifies mixed-phase process rates, for example the Wegener–Bergeron–Findeisen process and riming.

 

Reducing the INP concentration to values observed over the Southern Ocean results in top of the atmosphere (TOA) radiative fluxes closer to observations. The lower INP concentrations result in lower ice water content and higher liquid water content, leading to brighter and more widespread cloud; this increases the albedo, resulting in a more accurate simulation of the TOA radiation. Equally, a large sensitivity in the top of the atmosphere fluxes is seen when changing the mixed-phase overlap factor. Decreasing (increasing) the mixed-phase overlap factor results in less (more) ice and more (less) liquid reducing the TOA fluxes. Decreasing the mixed-phase overlap factor results in TOA fluxes closer to the satellite observations. In summary, simulations using INP concentrations suitable for the Southern Ocean result in simulations closer to observed but other parametrizations in the microphysics scheme are equally important for accurate simulations of radiative fluxes.

How to cite: Smith, D., Renfrew, I., van den Heuvel, F., Lachlan-Cope, T., Crawford, I., Bower, K., and Flynn, M.: The impact of mixed-phase cloud processes on radiative fluxes over the Southern Ocean in a convection-permitting model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11320, https://doi.org/10.5194/egusphere-egu24-11320, 2024.

EGU24-11511 | ECS | Posters on site | AS1.16

Atomic structure of pristine and water-covered microcline (001) – A prerequisite for understanding the ice nucleation mechanism on feldspar mineral dust particles 

Tobias Dickbreder, Franziska Sabath, Bernhard Reischl, Rasmus V. E. Nilsson, Adam S. Foster, Ralf Bechstein, and Angelika Kühnle

The aggregate state of water in clouds has a fundamental impact on the clouds’ properties such as reflectivity and lifetime. Consequently, it is crucial for the development and improvement of climate models to understand the mechanism of ice nucleation under atmospheric conditions. Most atmospheric ice nucleation is heterogeneous caused by the interaction between water droplets and ice nucleating particles. Under mixed-phase cloud conditions, one of the most important ice nucleating particles are feldspar minerals. Recent scanning electron microscopy studies have shown that ice nucleation on cleavage planes of K-rich feldspars predominantly takes place at step edges and pores (Kiselev, 2017). This has also been confirmed by video and atomic force microscopy on the micrometer scale (Holden, 2019). However, experimental insights into the atomic-scale structure of the most ice-nucleation active K-feldspar microcline are still missing, and, thus, the mechanism behind ice nucleation on feldspar minerals remains elusive. Here, we present high-resolution atomic force microscopy (AFM) data revealing the atomic structure of the microcline (001) surface in its pristine state and in contact with water (Dickbreder, 2024). AFM images of the pristine microcline (001) surface kept under ultrahigh-vacuum conditions, reveal features consistent with a hydroxyl-terminated surface. This finding suggests that water in the residual gas readily reacts with the surface highlighting the high reactivity of the as-cleaved surface. Indeed, corresponding density functional theory calculations confirm a dissociative water adsorption. Three-dimensional AFM measurements performed at the mineral-water interface unravel a layered hydration structure with two features per surface unit cell. Comparison with MD calculations suggest that the structure observed in AFM corresponds to the second hydration layer rather than the first water layer. We are convinced that the combination of structural information of the pristine and water-covered microcline (001) surface will contribute to uncovering the atomic-scale mechanism behind the exceptional ice-nucleation activity of feldspar minerals.

 

References:

Atkinson, J. D., Murray, B. J., Woodhouse, M. T., Whale, T. F., Baustian, K. J., Carslaw, K. S., Dobbie, S., O’Sullivan, D., Malkin, T. L., Nature, 498, 355-358, 2013.

Dickbreder, T., Sabath, F., Reischl, B., Nilsson, R. V. E., Foster, A., Bechstein, R. and Kühnle, A., Nanoscale, DOI:10.1039/d3nr05585j, 2024.

Holden, M. A., Whale, T. F., Tarn, M. D., O’Sullivan, D., Walshaw, R. D., Murray, B. J., Meldrum, F. C., Christenson, H. K., Science Advances, 5, 4316, 2019.

Kiselev, A., Bachmann, F., Pedevilla, P., Cox, S. J., Michaelides, A., Gerthsen, D., and Leisner, T., Science, 355, 367–371, 2017.

How to cite: Dickbreder, T., Sabath, F., Reischl, B., Nilsson, R. V. E., Foster, A. S., Bechstein, R., and Kühnle, A.: Atomic structure of pristine and water-covered microcline (001) – A prerequisite for understanding the ice nucleation mechanism on feldspar mineral dust particles, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11511, https://doi.org/10.5194/egusphere-egu24-11511, 2024.

EGU24-11598 | Posters on site | AS1.16

Ice production in northern hemisphere cold air-outbreak clouds: two contrasting aircraft campaigns 

Benjamin Murray and the M-Phase Team

Cold-air outbreaks (CAOs) are common high-impact weather events that produce extensive boundary layer clouds that have a substantial influence on our planet’s climate. These clouds are often supercooled and therefore their properties are susceptible to the formation of ice.  The amount of ice in these clouds has been identified as being particularly important for defining the magnitude of the cloud-climate feedback and climate sensitivity.

To address ice production in northern hemisphere CAOs we conducted two contrasting aircraft campaigns in 2022.  One campaign (ACAO, 11 flights) was in March in the Norwegian and Barents Sea where cold air flowed from the ice-covered Arctic Ocean. The other (M-Phase, 12 flights) was in October-November and focused on the Labrador Sea with air coming from the Arctic Archipelago. In both campaigns, we used similar instruments on the FAAM BAe-146 research aircraft designed to probe the aerosol properties, cloud microphysics and atmospheric thermodynamics of the CAO events.  Flight sorties were designed to study aerosol-cloud interactions as the CAO developed through the stratus and into the cumulus regime.

We found that INP concentrations in these Northern Hemisphere CAOs were orders of magnitude greater than CAO events over the Southern Ocean.  The springtime ACAO cases had systematically greater INP (and aerosol) concentrations than the autumnal Labrador Sea M-Phase cases. The presence of substantial amounts of mineral dust in the springtime Arctic, despite all local sources being covered in ice and snow, implies a reservoir of old INPs and aerosol in the springtime Arctic that originated from the low latitudes. This is supported by our global aerosol model. Primary ice production by INPs is shown to define the ice concentrations in the stratus regime in many cases, but in the cumulus regime there are pockets of very high ice concentrations that are indicative of secondary ice production.

Our modelling work has demonstrated that INPs are key to defining the stratus to cumulus transition and the cases are providing an excellent test for the high-resolution regional modelling with the Met Office Unified Model.  We are also using ACAO cases to study how INPs interact with clouds in CAOs, where warm temperature INPs are preferentially lost through nucleation scavenging. Furthermore, we envisage that the data from these campaigns will provide a valuable resource for model development, hypothesis testing and contrasting with other CAO campaigns in other places and times. 

Given the stark contrast of primary ice production in CAO clouds in different locations and times around the globe, we conclude that the primary production of ice in model CAO clouds should be linked to the aerosol properties and knowledge of the local INP population to reduce uncertainty in cloud feedback and climate sensitivity.

How to cite: Murray, B. and the M-Phase Team: Ice production in northern hemisphere cold air-outbreak clouds: two contrasting aircraft campaigns, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11598, https://doi.org/10.5194/egusphere-egu24-11598, 2024.

EGU24-12083 | ECS | Posters on site | AS1.16

Extending measurements of ice nucleation activity to large-size mineral dust particles 

Sebastian Vergara Palacio, Franziska Vogel, Romy Fösig, Adolfo González-Romero, Konrad Kandler, Xavier Querol, Nsikanabasi Silas Umo, Corinna Hoose, Ottmar Möhler, Carlos Pérez García-Pando, and Martina Klose

Mineral dust is considered one of the most important seeds for heterogeneous ice nucleation in clouds. In the past decades, several studies have worked on establishing a relationship between mineral dust, number concentration, nucleation temperature, supersaturation, and the number of ice crystals. The explored dust particle-size range was usually limited to a few micrometers for two main reasons: (1) larger and heavier particles are difficult to keep suspended in an experimental setting; and (2) the fraction of coarser aerosol was considered negligible. However, recent studies have shown that dust particles as large as 100 μm or even more can be transported over long distances, leaving a knowledge gap concerning their role as ice-nucleating particles.

In this work, we aim to contribute to closing this gap by investigating the ice nucleation activity for large-size mineral dust particles, extending the studied size range to particles of up to several tens of microns. For this purpose, we used natural dust samples with different mineralogical composition, collected consistently during field campaigns in Morocco and in Iceland, and segregated into five different size classes. In the framework of the MICOS (Dust-induced ice nucleation: effects of Mineralogical COmposition and Size) campaign, we conducted experiments with the Aerosol Interaction and Dynamics in the Atmosphere (AIDA) chamber and with the Ice Nucleation Spectrometer of the Karlsruhe Institute of Technology (INSEKT), in which the size-segregated samples were tested at different temperatures in the range between -16 and -27 °C. The ice nucleation efficiency was quantified in terms of the ice nucleation active surface site (INAS) density approach for the immersion freezing mode. Preliminary results from the AIDA and INSEKT experiments are presented, in which we extended the size range at which cloud chamber experiments are typically conducted.

How to cite: Vergara Palacio, S., Vogel, F., Fösig, R., González-Romero, A., Kandler, K., Querol, X., Umo, N. S., Hoose, C., Möhler, O., Pérez García-Pando, C., and Klose, M.: Extending measurements of ice nucleation activity to large-size mineral dust particles, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12083, https://doi.org/10.5194/egusphere-egu24-12083, 2024.

EGU24-12198 | Orals | AS1.16 | Highlight

Sources and abundance of ice nucleating particles derived from long-term measurements at high time resolution 

Ottmar Möhler, Pia Bogert, Alexander Böhmländer, Nicole Büttner, Kristina Höhler, Larissa Lacher, Romy Ullrich, and Franziska Vogel

Ice Nucleating Particles (INPs), a minor and strongly temperature dependent fraction of atmospheric aerosol particles, are key players in the weather and climate systems be inducing the formation of ice in mixed-phase and cirrus clouds. There is increasing evidence that INPs not only induce the formation of precipitation in particular over continental areas, but also have an important impact on a number of radiatively important clouds types throughout the troposphere.

New insight into the abundance, types, and sources of INPs, and by that also into their various roles in the atmosphere, can be obtained by longer-term measurements at high time resolution. Such measurements can be conducted with the PINE (Port-able Ice Nucleation Experiment) instrument, which was developed for both, flexible operation during dedicated laboratory experiments on ice nucleation processes and for automated operation during longer-term INP monitoring activities in the field.

This contribution will give a short introduction into the topics of primary ice formation and ice-nucleating particles, and will present and discuss examples of recent longer-term records of INP measurements with the PINE instrument at different European field sites like the Sonnblick Observatory in Austria, the Helmos observatory in Greece, the Zeppelin observatory in Spitzbergen, or the National Atmospheric Observatory Kosetice in the Czech Republic. These locations will also become observatories as part of the pan-European infrastructure ACTRIS for longer-term monitoring of aerosols and INPs.

How to cite: Möhler, O., Bogert, P., Böhmländer, A., Büttner, N., Höhler, K., Lacher, L., Ullrich, R., and Vogel, F.: Sources and abundance of ice nucleating particles derived from long-term measurements at high time resolution, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12198, https://doi.org/10.5194/egusphere-egu24-12198, 2024.

EGU24-12214 | ECS | Orals | AS1.16

Shaping the Tropical Anvil Cloudiness: the relative roles of net convective detrainment and vapor deposition in controlling the tropical high cloud fraction in an extratropically-warmed climate 

S. R. Monisha Natchiar, Mark Webb, Hugo Lambert, Geoffrey Vallis, Cyril Morcrette, Christopher Holloway, and Denis Sergeev

Improving the estimates of global climate sensitivity relies on understanding the mechanisms that control the fractional coverage of tropical anvil clouds. Even small changes in the tropical anvil cloud coverage have been shown to significantly impact the radiative budget of the Earth. Most general circulation models and cloud resolving models depict a decrease in the tropical anvil cloud cover with surface warming. According to the "stability-iris" hypothesis, this reduction is thermodynamically controlled by the changes in the upper-tropospheric static stability, which in turn is governed by the peak of the radiatively-driven clear-sky convergence. However, the influence of the changes in the atmospheric dynamics independent of the local SST changes remains relatively less explored due to the difficulty in segregating the dynamical influence from the local thermodynamic influence on the tropical anvil cloud cover.

Using idealized general circulation model simulations from the Met Office Unified Model, our study aims to understand the dynamical impact on the fractional cloudiness of tropical high clouds with global warming. To achieve this, we propose a novel method to separate the dynamical effects from the local thermodynamical effects by warming the extratropics and keeping the tropical sea surface temperatures unchanged. We thereby focus on the mechanisms underpinning the changes in the tropical high clouds resulting from changes in the atmospheric dynamics induced by extratropical warming. We find that the depositional growth of ice cloud condensates has relatively greater significance than the net convective detrainment of condensates in controlling the reduction of the fractional cloudiness over a considerable altitude range of the upper troposphere in the deep tropics.

How to cite: Natchiar, S. R. M., Webb, M., Lambert, H., Vallis, G., Morcrette, C., Holloway, C., and Sergeev, D.: Shaping the Tropical Anvil Cloudiness: the relative roles of net convective detrainment and vapor deposition in controlling the tropical high cloud fraction in an extratropically-warmed climate, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12214, https://doi.org/10.5194/egusphere-egu24-12214, 2024.

Aerosol-cloud interactions and ice production processes are important uncertainties in models of mixed-phase cold-air outbreak (CAO) clouds, which are vital for the estimation of cloud-phase feedback. Our model simulation results show that the sensitivities of the mixed-phase cloud properties during the two selected CAO cases are different, with Ice Nucleating Particle (INP) concentrations having a strong influence for both case studies, but the cloud droplet number concentration and the HM (Hallett-Mossop) efficiency only affect the warmer case. We also find that the simulations showing the best performance compared to observations are not consistent across multiple satellite-observed cloud properties, which suggests a possible structural deficiency in the model. The two cases are CAO events over the Labrador Sea, 15 March 2022 and 24 October 2022, with the latter one coinciding with the M-Phase aircraft campaign. The regional Met Office Unified Model coupled with a two-moment microphysics scheme was used to quantify the sensitivity of cloud cover, stratocumulus-to-cumulus transition, and cloud radiative properties to cloud droplet number concentration, INP concentration and efficiency of the HM process. Recent studies have aimed to understand how these two aspects influence CAO clouds, but have not compared the sensitivities under different environmental conditions or with a realistic temperature-dependent parameterisation for INPs. This study provides an instructive perspective on how cloud microphysics affects mixed-phase CAO clouds under different environmental conditions, and serves as a good basis for exploring the whole uncertain cloud microphysics parameter space across a range of environmental conditions.

How to cite: Huang, X., Field, P., Murray, B., Grosvenor, D., Van Den Heuvel, F., and Carslaw, K.: Sensitivity of mixed-phase cold-air outbreak clouds to aerosol-cloud interactions and ice production processes depends on environmental conditions: a comparison between spring and autumn CAO case studies over the Labrador Sea, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12568, https://doi.org/10.5194/egusphere-egu24-12568, 2024.

EGU24-12784 | ECS | Posters on site | AS1.16

Source apportionment and parameterization of ice nucleating particles observed at a high-altitude station in the north-eastern Mediterranean in autumn 2021 during the CALISHTO campaign 

Kunfeng Gao, Romanos Foskinis, Georgakaki Paraskevi, Stergios Vratolis, Konstantinos Granakis, Anne-Claire Billault-Roux, Franziska Vogel, Ottmar Möhler, Alexis Berne, Konstantinos Eleftheriadis, Alexandros Papagiannis, and Athanasios Nenes

Aerosol source apportionment improves the understanding of aerosol-cloud interaction processes and benefits the parameterization of ice nucleating particles (INPs), which also contributes to the predictability of climate models for quantifying the impacts of aerosols on the changing climate. This study, which took place in the frame of the Cloud-Aerosol InteractionS in the Helmos background TropOsphere (CALISHTO) campaign, investigates the interactions between mixed-phased clouds and aerosol particles at Helmos Mt. in Peloponnese, Greece (north-eastern Mediterranean). The source apportionment of INPs originating from different aerosol sources is achieved by identifying exclusive characteristics of relevant air masses. A synergy of measurement techniques was employed, including in-situ measurements for INP number concentration and aerosol property characterization, remote sensing techniques for atmospheric condition observations, as well as modelling simulations for calculating aerosol particle footprints.

The number concentration of INPs was observed in the mixed-phase cloud regime (>−27°C) in both the planetary boundary layer (PBL) and the free troposphere (FT). The results show that one in a million of aerosol particles can serve as INPs under the background condition in FT. The presence of precipitation/clouds may enrich INPs by suspending biological particles from near ground sources or releasing cloud-processed particles when the observation site is above PBL. The intrusion of remotely transported air masses leads to increased INPs for conditions above PBL, suggesting the observed INPs are of both local and remote origins. In addition, the INP abundance of different sources spans a range of three orders of magnitude and increases following the order of marine aerosols, continental aerosols, and then dust plumes. Biological particles are approximate to INPs observed in continental and marine aerosols, whereas mineral dust particles dominate the observed INPs when dust plumes are present. Furthermore, a case study on a calendar day was performed to investigate the effects of precipitation/clouds on INP abundance in the PBL. In contrast observations above the PBL, the presence of precipitation/clouds may lead to wet removal of aerosol particles and thus, decreased INPs.

Statistical analysis suggests that INP concentration in the mixed-phase cloud regime is significantly correlated with fluorescent particles, including biological and non-biological particles such as dust particles associated with fluorescent materials. The ratio of fluorescent to nonfluorescent particles and the ratio of coarse (>1.0 μm) to fine (<1.0 μm) particles are also found to be significantly correlated with observed INPs from different aerosol sources. Such properties further constrain the ice formation ability of aerosol particles showing fluorescence and are then used to improve the parameterization of INPs as a function of temperature, particle number concentration and the fluorescent or coarse particle ratio. The adapted INP parameterizations are demonstrated to be able to predict >90% INP observations within an uncertainty range of a factor of 10. The improved predictabilities of the adapted INP parameterizations are demonstrated by comparisons to parameterizations reported in the literature, and the improvement will reduce the uncertainties in cloud physics simulations.

How to cite: Gao, K., Foskinis, R., Paraskevi, G., Vratolis, S., Granakis, K., Billault-Roux, A.-C., Vogel, F., Möhler, O., Berne, A., Eleftheriadis, K., Papagiannis, A., and Nenes, A.: Source apportionment and parameterization of ice nucleating particles observed at a high-altitude station in the north-eastern Mediterranean in autumn 2021 during the CALISHTO campaign, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12784, https://doi.org/10.5194/egusphere-egu24-12784, 2024.

EGU24-13392 | Posters on site | AS1.16

Investigating lignin’s ice nucleation mechanisms by applying nano-particle synthesis and high-speed cryo-microscopy 

Paul Bieber, Anna Zeleny, and Nadine Borduas-Dedekind

Due to the changing climate, wildfires globally have been increasing in size and intensity. With the increase of these biomass burning events there is a surge of organic aerosols present in the atmosphere. Recent evidence from our group and the community suggests that organic aerosols can catalyze heterogeneous ice nucleation.1–3 Currently, heterogeneous ice nucleation is the largest source of uncertainty in climate models as it governs the formation of mixed-phase clouds, important climate regulators linked to annual precipitation and global cloud coverage. We are interested in what impact an increase in atmospheric biomass burning aerosols will have on mixed-phase cloud formation.

An important component of organic biomass aerosols is lignin, a macromolecule which provides strength and structure to vascular plants. Lignin has been measured as a notably recalcitrant component of organic aerosols following biomass burning events.4,5 To elucidate the role of morphology and size of biomass burning organic aerosols in ice nucleation, we synthesized nanoparticles from commercially available Kraft lignin via a facile nanoprecipitation process.6,7 The nanoparticles were centrifugally separated by size, characterized by dynamic light scattering (DLS) and by transmission electron microscopy (TEM), then tested for their freezing ability in our home-built Freezing Ice Nuclei Counter (FINC).8 Next, the freezing mechanism and location of onset freezing for lignin was investigated using a high-speed camera on a cryo-microscope.9 Cylindrical droplets, between two glass slides, were frozen to localize the onset location of freezing at the air-water interface (AWI) or in the bulk of the droplets. Videos of single freezing events were recorded with a time resolution of over 2000 frames per second.

Our preliminary results suggest that lignin nanoparticles ranging in size from 50 – 500 nm in diameter are ice active at -15 ºC, well above the background freezing of the instrument (-25 °C). Normalizing the freezing data to mass and surface area suggests that aggregation facilitates ice nucleation. Moreover, the high-speed videos suggest that lignin’s ice nucleation activity is higher closer to the AWI of a droplet, indicating that hydrophobic interactions could be responsible for the aggregation of lignin and adsorption at the AWI, similar to the behavior of surfactants. These findings help understand how lignin within biomass burning organic aerosols are able to nucleate ice and hence impact the ice crystal concentration in mixed-phase clouds.

References:

(1)        Bogler, S.; Borduas-Dedekind, N. Atmospheric Chem. Phys. 2020, 20 (23), 14509–14522.

(2)        Knopf, D. A. et al., Atmos Chem Phys 2014, 14 (16), 8521–8531.

(4)        Shakya, K. M. et al., Environ. Sci. Technol. 2011, 45 (19), 8268–8275.

(5)        Myers-Pigg, A. N. et al., Environ. Sci. Technol. 2016, 50 (17), 9308–9314.

(6)        Lievonen, M. et al., Green Chem. 2016, 18 (5), 1416–1422.

(7)        Zou, T. et al., J. Phys. Chem. B 2021, 125 (44), 12315–12328.

(8)        Miller, A. J. et al., Atmospheric Meas. Tech. 2021, 14 (4), 3131–3151.

(9)        Bieber, P.; Borduas-Dedekind, N. ChemRxiv 2023. (preprint)

How to cite: Bieber, P., Zeleny, A., and Borduas-Dedekind, N.: Investigating lignin’s ice nucleation mechanisms by applying nano-particle synthesis and high-speed cryo-microscopy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13392, https://doi.org/10.5194/egusphere-egu24-13392, 2024.

EGU24-13746 | Orals | AS1.16

Estimating Climate Sensitivity from UV satellite observations and CMIP6 models since 1980 

Clark Weaver, Dong Wu, Gordon Labow, David Haffner, Lauren Borgia, Laura McBride, and Ross Salawitch

We construct a long-term record of Top of Atmosphere shortwave (SW) albedo of clouds and aerosols from 340 nm radiances observed by NASA and NOAA satellite instruments from 1980 to 2013. We compare our SW cloud+aerosol albedo with simulated cloud albedo from both AMIP and historical CMIP6 simulations from 47 climate models. While most historical runs did not simulate our observed spatial pattern of the trends in albedo over the Pacific Ocean, four models qualitatively simulate our observed patterns. Those historical models and the AMIP models collectively estimate an Equilibrium Climate Sensitivity (ECS) of ~3.5oC, with an uncertainty from 2.7 to 5.1oC. Our ECS estimates are sensitive to the instrument calibration which drives the wide range in ECS uncertainty. We force the calibrations to have a near neutral change in reflectivity over the Antarctic ice sheet. Our observations show no sign of dissipating marine stratocumulus clouds. Instead, they show increasing cloudiness over the eastern equatorial Pacific and off the coast of Peru as well as neutral cloud trends off the coast of Namibia and California.

 To produce our SW cloud+aerosol albedo we first retrieve a Black-sky Cloud Albedo and empirically correct the sampling bias from diurnal variations. Then we estimate the broadband proxy albedo using multiple non-linear regression along with several years of CERES cloud albedo to obtain the regression coefficients. We validate our product against CERES data from the years not used in the regression. Zonal mean trends of our SW cloud+aerosol albedo show reasonable agreement with CERES as well as the Extended Pathfinder Atmospheres (Patmos-x) observational dataset.

How to cite: Weaver, C., Wu, D., Labow, G., Haffner, D., Borgia, L., McBride, L., and Salawitch, R.: Estimating Climate Sensitivity from UV satellite observations and CMIP6 models since 1980, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13746, https://doi.org/10.5194/egusphere-egu24-13746, 2024.

EGU24-13967 | ECS | Orals | AS1.16

Heterogeneous Ice Nucleation of Microplastics before and after Oxidation 

Teresa M. Seifried, Sepehr Nikkho, Aurelio Morales Murillo, Lucas J. Andrew, Edward R. Grant, and Allan K. Bertram

Many recent studies point to the environmental threat posed by microplastic pollution, both in waterways and as transmitted globally in the atmosphere.1,2 Airborne microplastics impact the climate by the direct absorption and scattering of radiation3 and may act indirectly to influence cloud formation and precipitation by means of heterogeneous ice nucleation.4 But, the true efficiency of microplastics as ice-nucleating particles and its implications for cloud formation remain largely unknown.

Here, we present evidence for ice nucleation in immersion freezing mode induced by various microplastics suspended in water. This study focuses on seven distinct microplastic morphologies in substances composed of polypropylene (PP), polyethylene (PE) and polyethylene terephthalate (PET). For each polymer type, we analyzed at least one commercially-available microplastic sample and one generated from the breakdown of a commonly used commercial product. PP needles, PP fibers and PET fibers nucleated ice at temperatures relevant for mixed-phase cloud formation, with T50 values of -20.88 °C ± 0.52, -23.24°C ± 0.21 and -21.93°C ± 0.51, respectively. The number of ice nucleation sites per surface area (ns(T)) ranged from 10-1 to 104 cm-2 in a temperature interval of -15 to -25°C. In addition, we conducted oxidation experiments, exposing the samples to ozone and UV light, resulting in a decrease of nucleation temperatures among the ice-active microplastics. The presented data holds significant potential for integration into climate models, facilitating estimations of their impact on cloud formation.

 

(1) Dris, R.; Gasperi, J.; Rocher, V.; Saad, M.; Renault, N.; Tassin, B. Microplastic Contamination in an Urban Area: A Case Study in Greater Paris. Environ. Chem. 2015, 12 (5), 592–599. https://doi.org/10.1071/EN14167.

(2) Allen, S.; Allen, D.; Baladima, F.; Phoenix, V. R.; Thomas, J. L.; Le Roux, G.; Sonke, J. E. Evidence of Free Tropospheric and Long-Range Transport of Microplastic at Pic Du Midi Observatory. Nat Commun 2021, 12 (1), 7242. https://doi.org/10.1038/s41467-021-27454-7.

(3) Revell, L. E.; Kuma, P.; Le Ru, E. C.; Somerville, W. R. C.; Gaw, S. Direct Radiative Effects of Airborne Microplastics. Nature 2021, 598 (7881), 462–467. https://doi.org/10.1038/s41586-021-03864-x.

(4) Ganguly, M.; Ariya, P. A. Ice Nucleation of Model Nanoplastics and Microplastics: A Novel Synthetic Protocol and the Influence of Particle Capping at Diverse Atmospheric Environments. ACS Earth Space Chem. 2019, 3 (9), 1729–1739. https://doi.org/10.1021/acsearthspacechem.9b00132.

How to cite: Seifried, T. M., Nikkho, S., Morales Murillo, A., Andrew, L. J., Grant, E. R., and Bertram, A. K.: Heterogeneous Ice Nucleation of Microplastics before and after Oxidation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13967, https://doi.org/10.5194/egusphere-egu24-13967, 2024.

EGU24-15019 | Posters on site | AS1.16

Overview of DCMEX project, progress made towards goals, and measurements of primary ice particles 

Alan Blyth and Declan Finney and the DCMEX team

The Deep Convective Microphysics EXperiment (DCMEX) was held in and around the convective clouds that formed and grew steadily over the Magdalena Mountains near Socorro, New Mexico during July and August, 2022. The overall goal of DCMEX is to reduce the uncertainty in cloud feedbacks associated with deep convection by improving the representation of microphysical processes in the UM/CASIM model. It is part of the NERC CloudSense programme that aims to reduce the uncertainty in climate sensitivity due to clouds. The aim of the field campaign was to make observations of the aerosols, ice nucleating particles (INPs), and the microphysics and dynamics of the clouds in order to both make new discoveries and to provide novel measurements to improve models. Measurements were made with the FAAM aircraft, ground-based aerosol instruments, radars and routinely with the NEXRAD radars and GOES-17 satellite instruments. In this talk, we will present an overview of the project and of the progress that has been made so far towards the overall goals, such as a new representation of INP in CASIM based on the observations and good measurements of the ice concentrations at several temperatures and stages of development. We will also present results on the observations of primary ice in the context of the measured INPs

How to cite: Blyth, A. and Finney, D. and the DCMEX team: Overview of DCMEX project, progress made towards goals, and measurements of primary ice particles, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15019, https://doi.org/10.5194/egusphere-egu24-15019, 2024.

Organic aerosols make up a considerable mass fraction of atmospheric particulate matter, and impact air quality and climate. In the atmosphere, organic aerosols are exposed to different relative humidities (RH), often ranging between 20% to 100% RH. Gas-particle partitioning of water equilibrates the aerosol particles with the ambient RH, forming aqueous organic aerosols. When exposed to solar radiation, photochemical reactions can occur within the aqueous organic aerosol particles. Such photochemical interactions are often enhanced at the interface formed between the aqueous organic phase and the surrounding air. Depending on the changes in composition these photochemical reactions can induce phase transitions of the particles, including liquid-liquid phase separation, resulting in aqueous organic aerosols with multiple condensed phases. Understanding of the interfacial photochemical reaction and impacts on the number of phases in aqueous organic aerosols remains poor but is critical to assess the impacts of aqueous organic aerosols on air pollution and climate. For example, the number of phases in aqueous organic aerosol particles impacts their reactivity and cloud formation potential, with important implications for air quality and climate.

Here, we propose how the combination of spectroscopy and microscopy tools can be exploited to address this issue: Sum-frequency generation, a surface-sensitive, nonlinear optical spectroscopy method, is used to investigate bulk laboratory proxies of atmospheric aqueous organic aerosols and study changes in their chemical surface composition, as a function of solar irradiation. In addition, we use optical microscopy, to directly study the number of condensed phases in individual particles of the same aerosol system. The combined methods provide microscopic- and molecular-level insights how photochemical reactions impact the phase behavior of aqueous organic aerosols.

How to cite: Abdelmonem, A. and Mahrt, F.: Combining surface spectroscopy and optical microscopy can provide evidence of phase separation induced by photochemical aging of organic aerosol, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15080, https://doi.org/10.5194/egusphere-egu24-15080, 2024.

EGU24-15843 | ECS | Posters on site | AS1.16

Investigating the Molecular-Scale Mechanism of Deposition Ice Nucleation on Silver Iodide Surfaces 

Golnaz Roudsari, Mária Lbadaoui-Darvas, André Welti, Athanasios Nenes, and Ari Laaksonen

Heterogeneous ice nucleation is a ubiquitous process in the natural and built environment. Deposition ice nucleation, according to the traditional view,, occurs in a subsaturated water vapor environment without the presence of supercooled water on the solid, ice-forming surface. This process is notably significant among the various ice formation mechanisms in high-altitude cirrus and mixed-phase clouds. Despite its significance, our understanding of the microscopic mechanism of deposition ice nucleation remains quite limited. This study introduces an adsorption-based mechanism for deposition ice nucleation through results from a combination of atomistic simulations, experiments and theoretical modeling.

Silver iodide (AgI) particles prove highly efficient as ice-nucleating particles (INPs), commonly employed in rain seeding, and stand as one of the most potent laboratory surrogates for ice nucleation. In this study, AgI is used as a substrate for the simulations. The study involves a combination of grand canonical Monte Carlo and molecular dynamics (GCMC/MD) techniques to investigate deposition ice nucleation on AgI. We find that water initially adsorbs in clusters which merge and grow over time to form layers of supercooled water. Ice nucleation on silver iodide requires at minimum the adsorption of 4 molecular layers of water. Guided by the simulations we propose the following fundamental freezing steps: 1) Water molecules adsorb on the surface, forming nanodroplets. 2) The supercooled water nanodroplets merge into a continuous multilayer when they grow to about 3 molecular layers thick. 3) The layer continues to grow until the critical thickness for freezing is reached. 4) The critical ice cluster continues to grow.

How to cite: Roudsari, G., Lbadaoui-Darvas, M., Welti, A., Nenes, A., and Laaksonen, A.: Investigating the Molecular-Scale Mechanism of Deposition Ice Nucleation on Silver Iodide Surfaces, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15843, https://doi.org/10.5194/egusphere-egu24-15843, 2024.

EGU24-15951 | ECS | Posters on site | AS1.16

Exploring the role of aggregation in ice-nucleating macromolecules of Betula pendula pollen 

Florian Reyzek, Nadine Bothen, Ralph Schwidetzky, Teresa Seifried, Paul Bieber, Ulrich Pöschl, Konrad Meister, Mischa Bonn, Janine Fröhlich-Nowoisky, and Hinrich Grothe

A wide range of aerosols, including dust, soot, and biological particles, can serve as ice nuclei, initiating the freezing of supercooled cloud droplets. This process significantly impacts cloud characteristics, and consequently, weather and climate. Among biological ice nuclei, some exhibit exceptionally high nucleation temperatures. While Ice Nucleating Macromolecules (INMs) found on pollen are typically not among the most active ice nuclei, they are abundant, as evidenced by their presence throughout the tissues of trees. Notably, recent studies have shown that certain tree-based INMs, such as those from Betula pendula, demonstrate ice nucleation activity above -10°C. These findings suggest that INMs emitted from the biosphere could play a more significant role in atmospheric processes than previously understood.

Our research delves into the properties of Betula pendula INMs through comprehensive ice-nucleation assays. We explore the stability of these INMs and the factors influencing their ice nucleation activity. Our approach integrates experimental data with size measurements and chemical analyses to better comprehend the underlying mechanisms.

Our findings reveal that Betula pendula INMs comprise three distinct classes active at -6°C, -15°C, and -18°C, each present in varying concentrations. We observed that freeze-drying and freeze-thaw cycles markedly alter their ice nucleation capacity. Additionally, heat treatments and chemical analysis suggest that these INM classes may be size-varying aggregates, with larger aggregates being more efficient at nucleating ice. This hypothesis aligns with previous studies on fungal and bacterial ice nucleators. Our research highlights the significance of birch INMs in atmospheric ice nucleation, not only because of their prevalence but also due to their occasional but notable high nucleation temperatures.

How to cite: Reyzek, F., Bothen, N., Schwidetzky, R., Seifried, T., Bieber, P., Pöschl, U., Meister, K., Bonn, M., Fröhlich-Nowoisky, J., and Grothe, H.: Exploring the role of aggregation in ice-nucleating macromolecules of Betula pendula pollen, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15951, https://doi.org/10.5194/egusphere-egu24-15951, 2024.

EGU24-16314 | ECS | Posters on site | AS1.16

Insights from a Year-long Study on Ice-Nucleating Particles in Helsinki 

Germán Perez Fogwill, André Welti, Patipun Nontasin, Linnea Mustonen, Ana Álvarez Piedehierro, and Katrianne Lehtipalo

This study explores the seasonal dynamics of ice-nucleating particles (INPs) in Helsinki over a year. Using an automatic sampler, we collected atmospheric particle samples daily onto filters, which were subsequently analyzed offline through drop freezing experiments in our laboratory. The year-long measurements are used to study the temporal variations and seasonal patterns of INP concentrations in Helsinki. Measurements of different meteorological variables are also considered for the study. Additionally, we present case studies with higher temporal resolution. The offline laboratory analysis of the collected filters enables the characterization of INP concentrations in an urban environment with changing aerosols sources in different seasons. The presented results contribute to the understanding of the variation of INPs in an environment where anthropogenic activity is a main contributor to the present aerosol load.

How to cite: Perez Fogwill, G., Welti, A., Nontasin, P., Mustonen, L., Álvarez Piedehierro, A., and Lehtipalo, K.: Insights from a Year-long Study on Ice-Nucleating Particles in Helsinki, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16314, https://doi.org/10.5194/egusphere-egu24-16314, 2024.

EGU24-16526 | Posters on site | AS1.16

Phase state of water adsorbed on ice nucleating particles 

André Welti, Yrjö Viisanen, Ana A. Piedehierro, and Ari Laaksonen

Barnes and Sänger (1961) suggested that a substance only becomes active at nucleating ice when the adsorbed water on the surface is in an ice-like state, and that there should be a correspondence between the temperature of ice nucleation and the “freezing” of adsorbed water.
We present spectroscopic measurements that allow to simultaneously determine the amount of adsorbed water and whether the adsorbed water is liquid or ice-like. These measurements are conducted using a new setup that allows to expose test substances to a broad temperature and humidity range while recording the diffuse infrared reflectance spectrum of the adsorbed water. The phase state of the adsorbed water with decreasing temperature is then compared to the ice nucleation temperature of the test substance, which is measured using a continuous flow diffusion chamber.

References:

Barnes, G. T., and Sänger, R., ZAMP 12, 159 (1961).

 

How to cite: Welti, A., Viisanen, Y., Piedehierro, A. A., and Laaksonen, A.: Phase state of water adsorbed on ice nucleating particles, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16526, https://doi.org/10.5194/egusphere-egu24-16526, 2024.

EGU24-18918 | Posters on site | AS1.16

Causes of large climate model spread in equatorial Pacific cloud feedback 

Peter Hill, Declan Finney, and Mark Zelinka

Climate models remain the best tools for predicting the impact of climate change on quantities relevant to human activity, such as precipitation, surface temperature and occurrence of severe weather events. Since many of these changes scale with the models equilibrium climate sensitivity, it is crucial to understand the differences in climate sensitivity between the models, which are primarily driven by inter-model differences in cloud feedbacks.

Inter-model differences in cloud feedbacks are largest in the equatorial Pacific. Focussing on the area from 10°S - 10°N, and 160°E – 270°E, we find an inter-model standard deviation in cloud feedback of ~1.36 W m-2 K-1. Using appropriate weighting to account for the area of this region, this equates to a contribution to the global mean cloud feedback uncertainty of ~ 0.07 W m-2 K-1, which represents approximately 20% of the inter-model spread in global mean cloud feedback. Local differences in cloud feedback between models in this region are even larger and may have implications for regional circulation and precipitation changes. This region is also notable as an exception to the high correlation in cloud feedbacks between coupled and atmosphere-only models.

In this presentation we will describe analysis of the causes of the inter-model spread in cloud feedbacks in this region. We shall demonstrate that the spread in domain-mean feedback in this region is due to inter-model differences in both dynamic and thermodynamic cloud feedbacks and show how this relates to changes in the properties of different cloud types amongst different models. We will also describe the use of empirical orthogonal function analysis to identify consistent cloud feedback patterns in this region across the ensemble of models and explain the causes of these patterns.

How to cite: Hill, P., Finney, D., and Zelinka, M.: Causes of large climate model spread in equatorial Pacific cloud feedback, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18918, https://doi.org/10.5194/egusphere-egu24-18918, 2024.

EGU24-18961 | ECS | Orals | AS1.16

Airborne observations of ice-nucleating particles in the vicinity of developing deep convective clouds during the North American monsoon 

Martin Daily, Joseph Robinson, Declan Finney, James McQuaid, Benjamin Murray, and Alan Blyth

Deep convective clouds play crucial roles in atmospheric processes, generating lightning, severe weather, and significant rainfall, while their extensive anvils reflect solar radiation. However, models face limitations due to a lack of understanding of microphysical processes in these clouds. Ice-nucleating particles (INP), essential for initiating primary ice production, have only rarely been measured in air directly relevant for convective clouds. This makes separating the roles of primary and secondary ice difficult to resolve. Here we report the abundance and likely composition of INP during the Deep Convective Microphysics Experiment (DCMEX) campaign in New Mexico, USA, using measurements made from the FAAM BAe 146 aircraft during flights over and around the Magdalena Mountains. Orographic convective clouds frequently form directly above these mountains during the monsoon season (July-August), making the locality uniquely suited for sampling the aerosol, including INP, that become entrained into the clouds. INP were collected on filters during sampling circuits around the mountain range at varying altitudes and then analysed offline for immersion mode ice-nucleating activity using droplet freezing assays. Repeated measurements over a period of weeks enabled us to observe changes in the INP population with changes in airmass origin and also the vertical INP profile.

Overall INP concentrations observed were high (0.1 – 1 L-1 at -10 °C) but consistent with previous observations of INP in dominantly continentally influenced air, with some INP active up to -5 °C frequently observed. Vertically resolved sampling revealed a deep and consistently present coarse aerosol layer extending from 0.5km up to 3km above ground, within which we found that the INP were evenly distributed.

Aerosol number and size-resolved compositional properties, derived using data from underwing optical probes and filter analysis with scanning electron microscopy with energy dispersive spectroscopy (SEM-EDX) respectively, were then related to the INP activity of our samples to infer composition and origin. When comparing our samples to laboratory parameterisations of aerosol classes’ ice-nucleating activity, mineral dust could account for the INP activity seen at low temperatures but were too active at higher temperatures, instead more consistent with fertile soil dust.

Throughout the campaign, there was a change in air mass origin from the northwest to the southeast and back again, however this shift did not significantly affect the INP population. When comparing our INP spectra to the parametrization of primary ice crystal number concentration by Cooper (1986), it was noted that overall, it predicts the range of our INP observations well but does not capture the observed curved shape of INP spectra at higher temperatures.

This study underscores the persistent presence of INP in growing deep convective clouds, providing insights to refine microphysics in cloud models. Comparisons with actual cloud microphysical observations would confirm primary and secondary ice production processes.

How to cite: Daily, M., Robinson, J., Finney, D., McQuaid, J., Murray, B., and Blyth, A.: Airborne observations of ice-nucleating particles in the vicinity of developing deep convective clouds during the North American monsoon, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18961, https://doi.org/10.5194/egusphere-egu24-18961, 2024.

EGU24-187 | ECS | Posters on site | AS1.17

Recent enhancement and prolonged occurrence of MJO over the Indian Ocean and their impact on Indian summer monsoon rainfall 

Keerthi Sasikumar, Debashis Nath, Xu Wang, Wen Chen, and Song Yang

The Madden–Julian oscillation (MJO) is one of the leading modes of tropical intra-seasonal variability, which exerts significant impacts on the weather and climate across the globe, particularly in the tropics. MJO affects the Asian monsoon by producing enhanced and suppressed convection during the active and break periods, respectively. In the recent decades, the heat content of Indo-western Pacific Ocean has increased significantly, which strengthened the MJO activity. Previous studies also have shown that the expansion of Indo-western Pacific warm pool led to the warping of MJO life cycle, which decreases its residence time over the Indian Ocean (IO) and increases over the Pacific Ocean. Here we show that in the boreal summer months, MJO amplitude has strengthened during the global warming hiatus or rapid IO warming period (1999–2015) compared to the previous period (1982–1998). In the later period, MJO exhibits a faster regeneration over the western IO, and its residence time has increased in the western hemisphere and western IO but decreased in the eastern IO and eastern Pacific Ocean. The strengthening of MJO and the readjustment in its residence time are due to the local MJO feedback on the IO and the La Nina like sea surface temperature pattern in the Pacific Ocean. The prolonged MJO activity leads to bursts of rainfall over the Indian subcontinent in Phase 3 and Phase 4, influencing the active spells of the Indian summer monsoon and causing heavy rainfall over central India and East Asia.

How to cite: Sasikumar, K., Nath, D., Wang, X., Chen, W., and Yang, S.: Recent enhancement and prolonged occurrence of MJO over the Indian Ocean and their impact on Indian summer monsoon rainfall, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-187, https://doi.org/10.5194/egusphere-egu24-187, 2024.

EGU24-471 | ECS | Posters on site | AS1.17

Sensitivity Analysis of Filtering Methods for Tropical Easterly Waves Classification 

Maria Juliana Valencia Betancur, Johanna Yepes, John F. Mejia, Alejandro Builes-Jaramillo, and Hernan D. Salas

Tropical easterly waves (TEWs) are quasi-periodic wave disturbances found within the easterly trade winds during boreal summer and autumn. They influence the synoptic-scale circulation dynamics in tropical America and contribute up to 50% of the seasonal precipitation (June to November) over northern South America. This study evaluates the sensitivity of different spectral bands in classifying TEWs based on daily vorticity at 700 hPa during the Organization of Tropical East Pacific Convection (OTREC) campaign. TEWs were identified in real-time using data from NOAA's Marine Tropical Surface Analysis. Complementarily, we refined TEWs identification by correlating it with 700 hPa filtered relative cyclonic vorticity from ERA5. To consider the uncertainties associated with the TEWs chronology selection, we employed two filtering methodologies: the Fast Fourier Transform (FFT) with periodicity bands of 3–10 days, 2.5–12 days, and 2.5–15 days, as well as the Ensemble Empirical Mode Decomposition (EEMD) with periodicity bands of 3–6 days, 4-12 days, and 3–15 days. Thirteen TEWs were initially reported by NOAA as crossing the Caribbean at 80°W. In our study, we further analyzed these waves by correlating areas characterized by westward-moving features of filtered relative cyclonic vorticity at the same longitude. Through this analysis, distinct classifications emerged using different filters, revealing the presence of 5 to 9 TEWs. The results show that TEWs classification is sensible to the filtering methods and periodicity band windows.

How to cite: Valencia Betancur, M. J., Yepes, J., Mejia, J. F., Builes-Jaramillo, A., and Salas, H. D.: Sensitivity Analysis of Filtering Methods for Tropical Easterly Waves Classification, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-471, https://doi.org/10.5194/egusphere-egu24-471, 2024.

EGU24-994 | ECS | Posters on site | AS1.17

Toward the Local Identification of Equatorial Waves  

Joao B. Cruz, José M. Castanheira, and Carlos C. daCamara

Equatorial waves (EWs) are synoptic to planetary-scale disturbances in the tropical atmosphere and are associated to a variety of tropical atmospheric phenomena. For instance, EWs can couple with convection, modulating a substantial fraction of cloud and rainfall variability in the tropics. Space-time filtering techniques that rely on the projection of data onto the structures of EWs are widely used in the literature. Such projection methods are employed with multiple purposes, including the unravelling of physical mechanisms underlying tropical atmospheric phenomena and the evaluation of numerical weather predictions in the tropics. However, most projection techniques rely on the global structures of these waves and, to our knowledge, there have not been efforts toward developing methodologies that identify EWs locally, i.e. over regions covering specific longitude ranges. This type of approach would highly decrease both the amount of data required and the computational power needed to identify EWs. Furthermore, it could potentially reduce the artificial effects local forcings may have on global projections.

This work makes use of the meridional and zonal structures of the solutions to the free Laplace tidal equations, known as Hough vector harmonics. By exploiting the properties of these solutions, we propose a methodology that allows for the identification of EWs over specific longitude ranges and perform a local analysis of fundamental wave properties.

 

This work was supported by IDL (UIDB/50019/2020) and CESAM (UIDP/50017/2020+UIDB/50017/2020+LA/P/0094/2020) through national funds by Fundação para Ciência e Tecnologia I.P./MCTES (FCT), Portugal.

How to cite: B. Cruz, J., Castanheira, J. M., and C. daCamara, C.: Toward the Local Identification of Equatorial Waves , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-994, https://doi.org/10.5194/egusphere-egu24-994, 2024.

This study investigated radiative effects on kinetic and potential energy budgets associated with rapid intensification of Typhoon Mujigae in 2015 by conducting sensitivity experiments with Weather Research and Forecasting (WRF) model simulation. We found that the inclusion of radiative effects mainly increases the symmetric rotational kinetic energy, while the radiative effects are from infrared longwave radiative effects. The comparison in symmetric potential energy and symmetric rotational kinetic energy budget between the sensitivity experiments excluding the radiative effects and solar shortwave radiative effects only reveals that the inclusion of infrared longwave radiative effects destabilizes the moist atmosphere and increases the conversion from symmetric potential energy to symmetric divergent kinetic energy , which reduces symmetric potential energy and enhances symmetric rotational kinetic energy through the strengthened conversion from symmetric divergent kinetic energy to symmetric rotational kinetic energy.

How to cite: Zhang, C. and Li, X.: Radiative Effects on Kinetic and Potential Energy Budgets Associated with Rapid Intensification of Typhoon Mujigae in 2015, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1354, https://doi.org/10.5194/egusphere-egu24-1354, 2024.

Tropical cyclone (TC) Khanun in 2017 was simulated in this study by the Weather Research and Forecasting (WRF) model. The observation-validated simulation data were used to examine dominant dynamic processes resulting in the contraction of the radius of maximum kinetic energy of symmetric rotational flow. The contraction rate was quantified by calculating the radial derivatives of symmetric rotational kinetic energy budget. The radius of maximum symmetric rotational energy was contracted rapidly before rapid intensification (RI) and moved inward slowly, then barely moved, and moved inward slowly again during RI.

The conversion from kinetic energy of asymmetric rotational flow to symmetric rotational flow induced by advection of asymmetric rotational tangential wind by asymmetric divergent radial wind at dominant azimuthal wavenumber-1 asymmetry and convergence of inward flux of symmetric rotational flow led to the rapid contraction before RI. During RI, symmetric rotational energy grew in the lower troposphere significantly, and upward flux convergence was equally important as inward flux convergence of symmetric rotational flow, which caused the first slow contraction. The conversion from kinetic energy of symmetric divergent wind to symmetric rotational flow associated with co-locations of maximum symmetric rotational energy and maximum symmetric inward radial flow produced stationary maximum symmetric rotational energy. Finally, horizontal and vertical flux convergence of symmetric rotational flow, and the conversion from environmental kinetic energy to symmetric rotational kinetic energy through the interaction between symmetric rotational flow and symmetric radial environmental flow generated the second slow contraction.

How to cite: Shi, Y. and Li, X.: Contraction of the radius of maximum symmetric rotational kinetic energy during the intensification of Tropical Cyclone Khanun (2017), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1355, https://doi.org/10.5194/egusphere-egu24-1355, 2024.

EGU24-1431 | ECS | Posters on site | AS1.17

Impact of the Indian Ocean Basin Mode on Tropical Cyclone Genesis in the North Indian and Western North Pacific Oceans 

Erandani Lakshani Widana Arachchige, Wen Zhou, Johnny C. L. Chan, and Xuan Wang

This study investigates the diverse influence of the Indian Ocean Basin Mode (IOBM) on tropical cyclone (TC) genesis in the north Indian Ocean (NIO) and western North Pacific (WNP) Ocean. Three types of warm (W1-W3) and cold IOBM (C1-C3) years are identified based on their persistence and connectivity with the Indian Ocean Dipole (IOD) mode. Type 1 is when the IOBM is decayed without conversion to the IOD, and type 2 is the conversion of the IOBM to the IOD with a phase change as a W event converts to a cold IOD or vice versa. Type 3 is a W event transforming into a positive IOD or a C event transforming into a negative IOD. During W1, in the WNP, TC genesis locations shift northward. They are less intense, whereas W3 TCs shift toward the southern WNP, far away from land, and significantly intensify from July to September (JAS). On the other hand, NIO TCs from October to December (OND) during W2 events are more concentrated in the Bay of Bengal (BoB). The W1–associated Genesis Potential Index (GPI) shows enhancement over the southern NIO from April to June (AMJ), extending into the WNP from JAS to OND. Most importantly, there is an increase in TCs south of 10°N in the WNP due to W3 and C2 events modulating vertical wind shear, mid-tropospheric relative humidity, relative vorticity at 850 hPa, and other related physical mechanisms. In contrast, a decrease in TCs south of 10°N in the WNP is caused by mechanisms associated with W2 and C3 events.Overall, changes in the large-scale environmental factors provide the background for the observed TC variation in both ocean basins during three types of IOBMs. This study, therefore, presents a detailed picture of the impact of IOBM events on TC activity over the NIO and WNP.

Key Words: Indian Ocean Basin Mode, north Indian Ocean, western North Pacific, warm and cold IOBM, Genesis Potential Index

How to cite: Widana Arachchige, E. L., Zhou, W., C. L. Chan, J., and Wang, X.: Impact of the Indian Ocean Basin Mode on Tropical Cyclone Genesis in the North Indian and Western North Pacific Oceans, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1431, https://doi.org/10.5194/egusphere-egu24-1431, 2024.

EGU24-2215 | ECS | Posters on site | AS1.17

Modulations of local rainfall in Northeast Australia associated with the Madden Julian Oscillation 

Thi Lan Dao, Claire L. Vincent, Yi Huang, Joshua S. Soderholm, and Dale S. Roberts

This study investigates the interaction of the Madden Julian Oscillation (MJO) with local scale forcings in regulating precipitation and its diurnal variation over coastal areas in Northeast (NE) Australia. Radar results show that the variation of rainfall with MJO phases exhibits both large-scale and local-scale influences. During the enhanced convection phases of the MJO, widespread increased rainfall signals are generated by large-scale forcings associated with the MJO convection, but the environmental factors controlling the type and amount of precipitation during each phase is different. By contrast, the locally enhanced rainfall probability during suppressed convection phases of the MJO possibly results from mesoscale convective systems such as sea breezes and the interaction of easterly trade-winds and topography. The amplitude of the rainfall diurnal cycle in suppressed convection phases is generally larger than in enhanced convection phases of the MJO. However, the impact of the MJO on diurnal rainfall characteristics (e.g., diurnal timing and amplitude) varies from phase to phase suggesting that each MJO phase needs to be considered separately. Simulations from the UK Met-Office Unified Model with grid-spacing of 2.2 km have been used to understand the processes driving this observed interaction of large-scale and mesoscale variability. The simulations show that coastal rainfall during suppressed convection phases of the MJO is sensitive to the trade-wind inversion height as well as moisture distribution. The findings are important for assessing numerical model skills at small scales and highlight the importance of process-based understanding at these scales.

How to cite: Dao, T. L., L. Vincent, C., Huang, Y., S. Soderholm, J., and S. Roberts, D.: Modulations of local rainfall in Northeast Australia associated with the Madden Julian Oscillation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2215, https://doi.org/10.5194/egusphere-egu24-2215, 2024.

Synoptic-scale disturbances prevail over the tropical western North Pacific during boreal summer. Those disturbances are generated over the equatorial western-central Pacific and propagate northwestward to the tropical western North Pacific. They may cause extremely heavy rainfall events and serve as initial disturbances for tropical cyclone genesis. The intensity of the synoptic-scale disturbance over the tropical western North Pacific is closely related to the El Niño–Southern Oscillation (ENSO) that modulates the seasonal atmospheric fields over the source regions, along the propagation paths, and over the impact regions of the synoptic-scale disturbances. ENSO displays a diverse range of amplitude, spatial pattern and temporal evolution. In view of the increasing frequency of extreme ENSO events under global warming and their substantial consequences, it is essential to investigate the relationship between the intensity of the synoptic-scale disturbances over the tropical western North Pacific and ENSO of varying amplitudes. In this talk, we will present evidences for the nonlinear response of the synoptic-scale disturbance intensity over the tropical western North Pacific during boreal summer to the amplitude of ENSO. A distinct difference is revealed between the nonlinear response of the synoptic-scale disturbance intensity over the tropical western North Pacific to the amplitude of El Niño and La Niña events. Physical explanation will be provided for the above feature based on observational analysis and numerical model experiments.

How to cite: Gu, Q., Wu, R., and Yeh, S.-W.: Nonlinear response of summertime synoptic-scale disturbance intensity over the tropical western North Pacific to ENSO amplitude, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2239, https://doi.org/10.5194/egusphere-egu24-2239, 2024.

    This study investigates interagency discrepancies among best-track estimates of tropical cyclone (TC) intensity in the western North Pacific, provided by the Joint Typhoon Warning Center (JTWC), the China Meteorological Administration (CMA), and the Regional Specialized Meteorological Center (RSMC) Tokyo during 2013–2019. The results reveal evident differences in maximum wind speed (MSW) estimates, where linear systematic differences are significant. However, the Dvorak parameter (CI) numbers derived from the MSWs reported by the three agencies are internally consistent. Further analysis suggests that the remained CI discrepancies are related to differences in the estimation of intensity trends, initial intensities, and TC positions among these datasets. In addition, the CI estimates provided by the JTWC for TCs over the open ocean are generally higher than those reported by the CMA and RSMC. However, the CMA (RSMC) tends to estimate stronger intensity for TCs in the China (Japan) mainland and coastal zone than those in the open ocean with the same intensity in JTWC dataset. This pattern potentially reflects the extensive use of surface observations by these two agencies for landfalling and offshore TCs. These results may help the research community to get more accurate details about the TCs in WNP from the best track datasets of different agencies.

How to cite: Bai, L., Xu, Y., Tang, J., and Guo, R.: Interagency discrepancies in tropical cyclone intensity estimates over the western North Pacific in recent years, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2780, https://doi.org/10.5194/egusphere-egu24-2780, 2024.

EGU24-2914 | Posters on site | AS1.17

Maintenance of MJO Convection by Radiative Feedbacks   

Eric Maloney and Wei-Ting Hsaio

The maintenance mechanisms for the Madden-Julian oscillation (MJO) remain an area of active research, and may include a combination of radiative feedbacks, wind-evaporation feedbacks, and moistening produced by lower tropospheric convective heating. This presentation will revisit the importance of radiative feedbacks for supporting MJO convection with a new GPCP precipitation dataset and NASA CERES radiative heating profiles. Prior work by Adames and Kim with the GPCP v1.3 precipitation product and NOAA OLR indicated that radiative feedbacks are strongly supportive of MJO convection as viewed through the vertically integrated moist static energy budget, and provide a strong scale selection mechanism. This presentation uses the newer GPCP v3.2 product to show that while radiative feedbacks still provide a strong scale selection mechanism, the overall strength of radiative feedbacks are weaker than with GPCP1.3. This suggests that the relative role of other feedbacks such as wind-evaporation feedbacks for supporting MJO convection may be more important than once thought.

 

This presentation also uses NASA CERES radiation profiles in a vertically-resolved moisture budget framework that employs the tropical weak temperature gradient assumption to determine the impact of radiative feedbacks on the MJO moisture budget. It is shown that longwave cloud radiative feedbacks onto MJO moisture anomalies are enhanced in the Indian Ocean and southern Maritime Continent region compared to other parts of the tropics, suggesting stronger support for MJO convection there. This finding is consistent with prior work by Mayta and Adames suggesting that the MJO most closely resembles a moisture mode in that region. It is hypothesized that enhanced vertical shear in the Indian Ocean and southern Maritime Continent supports convective organization that fosters greater cloud-radiative feedbacks.

How to cite: Maloney, E. and Hsaio, W.-T.: Maintenance of MJO Convection by Radiative Feedbacks  , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2914, https://doi.org/10.5194/egusphere-egu24-2914, 2024.

EGU24-3598 | Orals | AS1.17

Dynamical controls of mesoscale water vapor variability in the tropical western Pacific 

Adrian Tompkins, Alejandro Casallas, and Michie Vianca De Vera

Idealized simulations of radiative-convective equilibrium (RCE) with cloud resolving models have been used as a numerical laboratory to understand how diabatic processes can drive convective clustering, which in turn leads to significant drying of the free troposphere and increase in spatial humidity variability.  These processes, such as feedbacks between radiation, clouds and water vapor have been found to have relevance for numerous large-scale modes of convective organization, such as the width of the upward branch of the Hadley cell, ENSO and the Madden Julian Oscillation.  However, the controls of water vapor associated with convective variability on the sub-1000km mesoscale are less well known.  We adopt a simple multivariate analysis technique previously used to assess convective organization in RCE, and apply it to analyze convective organization and its impact on column integrated humidity (precipitable water, PW) variability for order 106 km2 mesoscale-size boxes in the tropical western Pacific warm pool region lying on or to the north of the equator.  We find that during the boreal summer/autumn periods, when sea surface temperature (SST) gradients are very limited in the target regions, convection remains mostly random and the horizontal PW gradients are small on these scales, this despite the action of diabatic feedbacks such as LW-cloud feedbacks and surface latent heat fluxes that are acting to  force clustering of convection. In stark contrast, during the other months of the year, when the zones are subject to a weak meridional SST gradient of SST (> 10-3 K km-1), convection is mostly aggregated over the warmer SSTs, with much larger PW gradients associated with an increase of clear sky OLR exceeding 10 W m-2. However, this situation is regularly disturbed by intermittent, multi-day episodes of more homogeneous convection distribution and limited spatial PW gradients. During these periods the SST-PW relationship flips, and the convecting regions are found over the coldest SSTs.  By using an index based on the SST-PW covariance, we construct a composite of 44 such events over a 4 year period which shows that they are associated with a westward-propagating, convectively-coupled Rossby wave like mode that is symmetric about the equator.  An independent multivariate (SST-PW) rotated EOF analysis confirms this, indicating the robustness of the result. We hypothesize that the longer-term variations in an convective organization index which was directly related to the tropics-wide energy budget (Bony et al. 2020) may be driven by the frequency of occurrence of these westward propagating modes, that seem to act as a primary control on mesoscale water vapor variability in the warm pool region in the boreal winter and spring months.

How to cite: Tompkins, A., Casallas, A., and Vianca De Vera, M.: Dynamical controls of mesoscale water vapor variability in the tropical western Pacific, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3598, https://doi.org/10.5194/egusphere-egu24-3598, 2024.

Quickly intensifying tropical cyclones (TCs) are exceptionally hazardous for Atlantic coastlines.  An analysis of observed maximum changes in wind speed for Atlantic TCs from 1971-2020 indicates that TC intensification rates have already changed as anthropogenic greenhouse gas emissions have warmed the planet and oceans.  Mean maximum TC intensification rates are up to 28.7% greater in a modern era (2001-2020) compared to a historical era (1971-1990).  In the modern era, it is about as likely for TCs to intensify by at least 50 kts in 24 hours, and more likely for TCs to intensify by at least 20 kts within 24 hours than it was for TCs to intensify by these amounts in 36 hours in the historical era.  Finally, the number of TCs that intensify from a Category 1 hurricane (or weaker) into a major hurricane within 36 hours has more than doubled in the modern era relative to the historical era.  Significance tests suggest that it would have been statistically impossible to observe the number of TCs that intensified in this way during the modern era if rates of intensification had not changed from the historical era.    

How to cite: Garner, A.: Observed Increases in North Atlantic Tropical Cyclone Peak Intensification Rates, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4089, https://doi.org/10.5194/egusphere-egu24-4089, 2024.

This study classifies 407 developing disturbances (DEV) and 2309 nondeveloping disturbances (NONDEV) over the western North Pacific into five large-scale circulation patterns, namely the pre-existing cyclone (PC), easterly wave (EW), zonal wind convergence (CON), zonal wind shear line (SL), and mixed zonal wind convergence and shear line (CON-SL) patterns. The SL pattern has the highest TC yield percentage, followed by the CON-SL, while the EW is the least favorable pattern. The composite analysis shows that upper-level divergence, midlevel relative humidity, and surface heat flux (SHF) growth are crucial to the disturbance development in all the five patterns. Besides, large lower-level barotropic kinetic energy conversion and a well-developed primary circulation are good indicators for disturbance development in the PC, EW, and CON rather than in the SL and CON-SL patterns. Furthermore, for the PC, EW and CON patterns, the DEV features strong and rapidly growing SHF and mesoscale convective systems (MCS) closer to the disturbance center, which allows deep-layer warming and moistening, and drives a deep secondary circulation. Interestingly, due to an environment with high lower-level vorticity, the SL and CON-SL patterns typically foster a relatively mature primary circulation with strong SHF and MCS concentrated close to the center, especially for the NONDEV at the pre-genesis stage. However, a drier mid-to-upper-level environment for the NONDEV inhibits deep convection and causes insufficient upper-level suction, which may explain its shallow secondary circulation and therefore poor potential to develop further.

How to cite: Wang, Z. and Chen, G.: Comparison between Developing and Nondeveloping Disturbances for Tropical Cyclogenesis in Different Large-Scale Flow Patterns over the Western North Pacific, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4324, https://doi.org/10.5194/egusphere-egu24-4324, 2024.

EGU24-4564 | ECS | Orals | AS1.17

On the vorticity statistics in the Atlantic and East Pacific ITCZ 

Divya Sri Praturi and Bjorn Stevens

We investigate the small scale dynamical controls on the Intertropical convergence zone (ITCZ) in the Atlantic and East Pacific basins using 5 year, global km-scale coupled atmosphere-ocean-land simulations. To this end, using an ITCZ-based coordinate system, we develop a composited view of the zonal mean statistics of potential vorticity (PV) during Boreal Summer (JJA) at geopotential heights of 0.3, 1.5 and 4 km. The ITCZ-based coordinate system is defined locally at each longitude, such that the ITCZ latitude — identified as the latitude where the column water vapor is a maximum — constitutes the origin. The zonal, 5-year JJA mean latitudes of the Atlantic and East Pacific ITCZ determined based on this definition are 7.8°N and 10.8°N, respectively. The thus obtained composited PV profiles are robust with low inter-annual variability. The PV profiles exhibit a similar structure in both the basins of interest: a gradual increase in the PV values with latitude, followed by a sharp increase in the PV values in the ITCZ due to latent heating. The necessary conditions for the instability of the zonal flow are met, as the sign of the meridional gradient of PV is reversed at the ITCZ. The magnitudes of PV statistics in and around the Atlantic ITCZ are slightly smaller than those of the East Pacific ITCZ. The differences in PV values in the basins can be explained using one of the processes governing the vertical vorticity in the ITCZ, i.e., vortex stretching due to convergence. The vortex stretching term is proportional to the Coriolis parameter, i.e., for a given convergence rate in the ITCZ, more northern ITCZ latitudes could experience greater jumps in vertical vorticities. We also find that at geopotential heights of 0.3 and 1.5 km, the monthly mean relative vertical vorticity in the ITCZ increases as the ITCZ moves to the North. 

How to cite: Praturi, D. S. and Stevens, B.: On the vorticity statistics in the Atlantic and East Pacific ITCZ, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4564, https://doi.org/10.5194/egusphere-egu24-4564, 2024.

EGU24-4620 | ECS | Posters on site | AS1.17

Association between torrential rainfall and tropical cyclone induced remote moisture transport over East Asia 

Shiqi Xiao, Aoqi Zhang, Yilun Chen, and Weibiao Li

There is increasing attention to torrential rainfall remote from tropical cyclones (TCs). However, the relationship between precipitation and TC induced remote moisture transport over decades is still unknown. To find the relationship above, we used objective identification of TC induced remote moisture transport to obtain spatiotemporal evolution of clusters and rainfall characteristics inside the clusters. The contribution of TC induced remote moisture transport to annual mean rainfall over North China and surroundings are 5–15 % higher than that over South China and surroundings. TC cases that induced remote heavy rainfall over two regions are listed. The tracks of TC induced remote moisture transport are generated using spatiotemporal digraphs. We used double Gaussian function to fit the relationship heavy rainfall frequency and moisture transport height, and used sigmoid function for the relationship between heavy rainfall frequency and moisture transport intensity derived from thousands of clusters over decades. The moisture transport height of peak heavy rainfall frequency over TC induced remote moisture transport are significantly higher than the transport without TC effect. The moisture transport intensity threshold for heavy rainfall frequency over 20 % is smaller over South China and surroundings than that over North China and surroundings. Those results above have quantified the relationship between heavy rainfall and moisture transport inside clusters, which is beneficial to forecast of torrential rainfall remote from TCs.

How to cite: Xiao, S., Zhang, A., Chen, Y., and Li, W.: Association between torrential rainfall and tropical cyclone induced remote moisture transport over East Asia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4620, https://doi.org/10.5194/egusphere-egu24-4620, 2024.

EGU24-5312 | ECS | Posters on site | AS1.17

Assessing the validity of simple models for tropical cyclones in high resolution simulations 

Giousef Alexandros Charinti

Existing theoretical models for tropical cyclones have been instrumental in understanding the mechanisms under which their intensification occurs. The potential intensity (PI) which was first introduced by Emanuel 1986, provides an upper bound for the intensity a tropical cyclone can achieve based on the environmental conditions. However, this model and others naturally assume idealized settings which do not necessarily occur in the real world. Using simulations from the high resolution cloud resolving model SAM in rotating radiative-convective equilibrium settings, we assess the validity of these idealizations in the simulations. We find that some idealizations, such as assuming convection on a moist adiabat in the eyewall, are only partially valid. In order to understand why these deviations from the theory occur, we look at different possible mechanisms missing in simple models, such as upper level processes and entrainment.

How to cite: Charinti, G. A.: Assessing the validity of simple models for tropical cyclones in high resolution simulations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5312, https://doi.org/10.5194/egusphere-egu24-5312, 2024.

EGU24-5466 | ECS | Posters on site | AS1.17

Abrupt ending of MJO by CCKW precipitation leaves swath of flooding across Indonesia 

Natasha Senior, Adrian Matthews, Ben Webber, Jaka Paski, Danang Nuriyanto, Donaldi Permana, and Richard Jones

Convectively coupled equatorial Kelvin waves (CCKWs) are eastward propagating weather systems that locally organise convection and have been linked to precipitation extremes across the Maritime Continent (MC). They are often embedded in convectively active phases of the Madden-Julian Oscillation (MJO) which too propagates eastwards but influences convection in the MC over longer timescales and larger areas. Previous high impact weather case studies have linked CCKWs to local precipitation extremes. In this study, we examine a case study during July 2021 of multiple CCKWs embedded within an active MJO. The final CCKW traversed the western MC causing precipitation extremes across equatorial Indonesia that lead to numerous reports of flooding and landslides, with the West Kalimantan region the worst affected. The MJO event itself was abruptly terminated following the passage of this CCKW. Through analysis of the moisture budget we find that the rainfall exceeded the convergence of moisture to produce the pronounced drying. Prior to the local MJO termination, we find there was enhanced westward propagating diurnal activity across the equatorial MC coinciding with a steady increase of total column water. We also examine observations of the extreme rainfall event in the West Kalimantan province. Comparing different deterministic model configurations, we find that the convection permitting models generally perform better when there are not multiple CCKWs present within the initial conditions. This research highlights how CCKWs should not simply be viewed as convective systems that locally affect weather but have the potential to have devastating impacts over the entire equatorial MC especially when involved in multiscale interactions both with the diurnal cycle and with the MJO.

How to cite: Senior, N., Matthews, A., Webber, B., Paski, J., Nuriyanto, D., Permana, D., and Jones, R.: Abrupt ending of MJO by CCKW precipitation leaves swath of flooding across Indonesia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5466, https://doi.org/10.5194/egusphere-egu24-5466, 2024.

EGU24-5958 | Posters on site | AS1.17 | Highlight

Freddy: breaking record for Tropical Cyclone precipitation? 

Enrico Scoccimarro, Paolo Lanteri, and Leone Cavicchia

Depending on the location on the Earth planet, the amount of precipitation associated with Tropical Cyclones (TCs) can reach 20% of the total yearly precipitation over land and up to 40% over some ocean regions. TC induced freshwater flooding has been suggested as the largest threat to human lives due to TCs. Therefore, a reliable quantification of the precipitation amount associated with each past TC is important for a better definition of the TC fingerprint on the climate. The temporal and horizontal resolution of state-of-the-art observational datasets and atmospheric reanalysis give the possibility to quantify the TC-associated precipitation over the Earth planet following the observed TC tracks. In this work we compare results from different observational and reanalysis datasets in terms of TC-associated precipitation, to verify the consistency between them. A particular focus is given to the record-breaking TC Freddy (Southern Indian Ocean, 2023).  Here we show that the time-varying bias in TC associated precipitation, due to the positive trend in assimilated observations, makes it difficult to assess long-term trend investigation based on reanalysis: to this aim we need to build on state-of-the-art General Circulation Models, free to evolve under historical radiative forcing. This work is part of CLINT EU project activity (grant agreement ID: 101003876; DOI: 10.3030/101003876).

How to cite: Scoccimarro, E., Lanteri, P., and Cavicchia, L.: Freddy: breaking record for Tropical Cyclone precipitation?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5958, https://doi.org/10.5194/egusphere-egu24-5958, 2024.

EGU24-6300 |