EGU General Assembly 2024  

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.

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.

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.

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.

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.

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 0.1^o 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 99.9^th 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.

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.

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.

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.

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.

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.

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 (T_soil) 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 T_g and T_soil 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,  T_g and T_soil 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 (T_soil) 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 T_soilvariation 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 T_soil by one phase at the QBW timescale, indicating that the warming surface T_soil could enhance the subseasonal precipitation. The numerical experiments using the WRF model further demonstrate the effect of warming surface T_soil  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 T_soil  at the subseasonal timescale. This study confirms the importance of surface T_soil 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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