Acidity is one central parameter in atmospheric multiphase reactions, and strongly influences the climate, ecological and health effects of aerosols. Yet, the drivers of aerosol pH remain to be fully resolved. Here we investigated into this issue with thermodynamic models and observations. We find that aerosol pH levels in populated continental regions are widely buffered by the conjugate acid-base pair NH₄⁺/NH₃, and in aerosols an individual buffering agent can adopt different buffer pH values. To explain these large shifts of buffer pH, we propose a multiphase buffer theory, and show that aerosol water content and mass concentration play a more important role in determining aerosol pH in ammonia-buffered regions than variations in particle chemical composition. These results imply that aerosol pH and atmospheric multiphase chemistry are strongly affected by the pervasive human influence on ammonia emissions and the nitrogen cycle in the Anthropocene. We further investigated into the applications of the multiphase buffer theory. Exemplary applications include to help explain the formation of severe hazes in China, to quantify the contribution of different factors in driving the aerosol pH variations, and to provide the framework to reconstruct long-term trends and spatial variations of aerosol pH, etc. Further investigations on its applications in aerosol and cloud chemistry studies are needed.

How to cite: Zheng, G.: Multiphase buffer theory: explanations of contrasts in atmospheric aerosol acidity and its applications, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4082, https://doi.org/10.5194/egusphere-egu23-4082, 2023.

Currently major efforts are underway toward refining the horizontal grid spacing of climate models to about 1 km, using both global and regional models. Such resolutions have been used for about a decade in limited-area numerical weather prediction applications and have demonstrated significant improvements in the representation of convective precipitation events (thunderstorms and rain showers). There is the well-founded hope that these benefits carry over to climate models, as the approach enables replacing the parameterizations of moist convection and gravity-wave drag by explicit treatments.

In this presentation, we will review three areas of km-resolution climate modeling. First, consideration will be given to an ensemble of km-resolution simulations from the CORDEX-FPS program on convection-permitting climate modelling, with a computational domain covering the greater Alpine region. This addresses the occurrence of short-term heavy precipitation events including their impacts such as flash floods, hail, and lightning. Results demonstrate the benefits of high computational resolution, in particular for the representation of short-term heavy events of severe weather. Second, we will present recent results from the projects trCLIM / CONSTRAIN carried out over the tropical and subtropical Atlantic, with the goal to assess the potential of the methodology to constrain estimates of the equilibrium climate sensitivity. It will be argued that km-resolution is a highly promising approach for constraining uncertainties in global climate change projections, due to improvements in the representation of tropical and subtropical clouds that goes along with an improved representation of the intertropical convergence zone (ITCZ).

Third, technical aspects of developing km-resolution global models will be addressed. Developing this approach requires a concerted effort between climate and computer sciences. Key challenges are the exploitation of the next generation hardware architectures using accelerators (e.g. graphics processing units, GPUs), the development of suitable approaches to overcome the output avalanche, and the consistent maintenance of the rapidly-developing model source codes on a number of different compute architectures. Despite these challenges, it will be argued that km-resolution GCMs with a capacity to run at 1 SYPD (simulated year per day), might be much closer than commonly believed. However, as the computational load of CMIP-style simulations is tremendous, alternative ways to exploit these models will be needed.

How to cite: Schär, C.: Kilometer-resolution climate models: prospects and challenges, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9284, https://doi.org/10.5194/egusphere-egu23-9284, 2023.

Atmospheric resolved-scale air flow (dynamics) and sub-grid parameterizations (physics) are two essential components of a weather or climate model. These two independent components are coupled and advanced using the same time step, either parallel or sequentially split. However, traditionally dynamics and physics are engineered in isolation and developed independently in models. As a result, many parts of the physics run at a physically-inappropriate time frequency or with heat transfers that are inconsistent with the dynamics, leading to errors. In addition, physical parameterizations should contain dynamical and non-dynamical processes. We believe there are compelling reasons that dynamical processes, if resolved, should be taken care of by the dynamical core.

Our study proposes a novel integrated dynamics-physics coupling framework (Zhou and Harris, 2022) within the GFDL (Geophysical Fluid Dynamics Laboratory) weather-to-seasonal prediction system SHiELD (System for High-resolution prediction on Earth-to-Local Domains; Harris et al., 2020) that promises to resolve the above issues. We will present our integrated coupling framework and the development of integrated physical parameterization for this framework in detail. The performance of forecast experiments using the modeling system SHiELD with this integrated coupling framework will be highlighted, focusing on large-scale circulation, cloud and precipitation, hurricane, and convective-storm predictions.

How to cite: Zhou, L. and Harris, L.: An Integrated Coupling Framework for Atmospheric Dynamics and Physics, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13, https://doi.org/10.5194/egusphere-egu23-13, 2023.

The improvement of numerical weather forecasts is a key element to predict high-impact weather events, associated with deep moist convection. The observations that are assimilated into numerical weather prediction systems are conformed by numerous data sets and their impact should be objectively evaluated. This can be efficiently estimated by the Forecast Sensitivity to Observation Impact (FSOI) methodology. In this study, we explore the application of the ensemble formulation of FSOI (EFSOI) in a convective scale regional data assimilation system over Sierras de Córdoba (Argentina), a data-sparse region with complex terrain characterized by the periodic occurrence of extreme precipitation and flash floods events. To evaluate the observation networks that result beneficial and detrimental for the forecast, the Weather Research and Forecasting model coupled with the Local Ensemble Transform Kalman Filter was used with 40 members. Convective scale analyses were obtained every 5 minutes, assimilating reflectivity data from a C-band radar and conventional and non-conventional surface weather stations (CSWS and NSWS). The experiment  was initialized on December 13 at 23 UTC and ran for 5 hours, until December 14 03 UTC. The experiment conducted was a case study within the intensive observing period of the RELAMPAGO-CACTI field campaign that was carried out during the 2018-2019 austral warm season in the center of Argentina. An independent data assimilation cycle using more observations and a different configuration is used in the experiments as verifying truth for the computation of forecast errors in EFSOI.

Results showed that all the observation sources had, on average, a positive impact on the 30 minute forecasts with a positive impact rate above 50%. However, when observations impacts are analyzed by geographic location, different results are evidenced. Most of the surface stations that evidence a detrimental impact in forecasts are located in the northern part of the region, probably due to a misrepresentation of the thermodynamic environment. Regarding radar reflectivity observations, values of positive impact rate above 50% dominate over all the region, demonstrating that, in general, they reduce the forecast errors. The results suggest that the observations with values of reflectivity beneath 15 dBZ have a larger amount of beneficial observations in lower levels than in upper levels.

This methodology is an approximation to quantify the impact of reflectivity and surface observations on a convective permitting forecast over the region. The results of this (and future) work can help to identify observation data sources detrimental for the data assimilation system, suggesting data selection criteria to assess improvements in this regional convective-scale data assimilation system where nonconventional observations such as radar data plays an essential role.

How to cite: Casaretto, G., Dillon, M. E., Garcia Skabar, Y., Ruiz, J., Maldonado, P., and Sacco, M.: Ensemble Forecast Sensitivity to Observations Impact (EFSOI) of a high impact weather event using a convection permitting data assimilation system., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-415, https://doi.org/10.5194/egusphere-egu23-415, 2023.

This study explores the potential impact of global navigation satellite system (GNSS) radio occultation (RO) data on the performance of satellite radiance data assimilation for the tropical cyclone formation forecast over the western North Pacific. The forecast experiments of 32 tropical disturbances in September−October 2019 are performed through a regional model. Either assimilation of GNSS RO data, radiance data, or both of them can improve the forecast skill and environmental moisture of tropical cyclone formation. However, the interaction between radiance and GNSS RO data can further increase moisture throughout the forecast period, compared to the experiment with only radiance data assimilation. Moreover, the improved vorticity patterns are different between the experiments with GNSS RO data and with radiance data. When both the GNSS RO and radiance data are assimilated, the improved vorticity pattern tends to the pattern improved by GNSS RO data assimilation. This may be attributed to the anchoring effect of GNSS RO data on satellite radiance data assimilation. Although radiance data volume is much larger than GNSS RO data, the interaction between GNSS RO and radiance data in the data assimilation process can significantly improve forecast performance.

How to cite: Teng, H.-F., Kuo, Y.-H., and Done, J.: Impact of radio occultation data on satellite radiance data assimilation performance in tropical cyclone formation forecast over the western North Pacific, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1079, https://doi.org/10.5194/egusphere-egu23-1079, 2023.

Airflow is one of the most important weather parameters in a city. It is important for the air quality, the city's heat balance, pedestrian comfort and the safety of high-rise buildings. Local flow at the scale of streets and districts is difficult or impossible to capture in regional weather models. Computational Fluid Dynamic models are the solution. In this paper, the OpenFoam model was used to model wind direction and speed in specific meteorological situations. The results were compared with measurement stations in Warsaw, and the model was improved on their basis. An averaged Navier Stokes turbulence model was used under steady-stable flow conditions. The Darcy-Forchheimer model was used to take vegetation into account. The poster presents the first results of analyzes related to the spatial distribution of wind direction and speed, delineates areas at risk of low air quality and compares it with the results from measuring stations. In addition to the basic model, a model containing ground thermals was also created to study the extent and intensity of the urban heat island and to study the phenomenon of smog during temperature inversion in selected meteorological conditions. A comparative analysis of both models was made. The first results show that it is possible quite accurately to map airflow in a city. It also indicates that some existing ventilation channels of the city have been blocked or limited due to new investments. The most important ventilation channel is the Vistula valley, which is 500-600 m wide in Warsaw. However, due to the terrain, its most important role is not fulfilled during prevailing westerly winds, and then the air quality decreases, especially at low wind speeds. In most cases, the northern districts are also generally better ventilated (spatial distribution of buildings, higher wind speeds) than the southern districts, but this is not always visible when assessing air quality. The immediate vicinity also influences the aspects of mechanical ventilation of the city and the way buildings are heated. Districts that theoretically should have better conditions for air exchange are often areas of single-family houses and independent boiler rooms. The city centre, despite tighter development, is heated by the municipal heating plant, and they are not direct emitters of pollution. Another aspect is vehicle traffic. In the city centre, more vehicle traffic is another pollutant emitter. For this reason, pollutants specific to heating and traffic were analysed separately. The general problem in high-resolution city-scale modelling is the use of adequate computational power. This initially precludes using CFD models in meteorological nowcasting and short-term modelling.

How to cite: Strzyzewski, T. and Jaczewski, A.: Modeling of wind conditions in Warsaw, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1128, https://doi.org/10.5194/egusphere-egu23-1128, 2023.

At Deutscher Wetterdienst (DWD), the SINFONY project has been set up to develop a seamless ensemble prediction system for convective-scale forecasting with forecast ranges of up to 12 hours. It combines Nowcasting (NWC) techniques with numerical weather prediction (NWP) in a seamless way. So far NWC and NWP run on two different IT-Infrastructure levels. Due to the data transfer between both infrastructures, this separation slows down SINFONY, makes it complex and prone to disturbances. These disadvantages are solved by applying the interconnected part of the SINFONY on one single architecture using a Docker Container.

With this aim in view a Docker-Container of the respective NWC components is created and executed on the infrastructure of NWP, the high performance linux computing cluster (HPC) of DWD. In test applications we already observed a speed up of roughly 20% by using the Container on the HPC-cluster instead of using NWC-Tools on the initial NWC IT-Architecture. The Container is already implemented in DWD’s experimental tool BACY for the assimilation cycle.

A major innovation of SINFONY is the rapid update cycle (RUC), an hourly refreshing NWP procedure with a Forecast range of 8 hours, which will be extended to 12 hours soon. The container will be implemented to the RUC and used for the subsequent combination of NWP and NWC forecasts.

In the presentation I will explain what a container is and discuss opportunities and risks of this technology. I will introduce how building the Container is integrated to the CICD procedures at DWD, how and where the Container is implemented to BACY and discuss latest results for the implementation to the RUC.

How to cite: Zacharuk, M., Welzbacher, C., Schnoor, I., Rathmann, N., Eser, C., Prill, F., and Blahak, U. and the SINFONY: Docker container in DWD's Seamless INtegrated FOrecastiNg sYstem (SINFONY), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1463, https://doi.org/10.5194/egusphere-egu23-1463, 2023.

With the ongoing climate change, the Arctic region is experiencing rapid warming. This has a profound effect on the sea-ice cover and, as a result, on the surface albedo. Surface albedo has a large impact on the energy balance of the region: a decrease in surface albedo leads to increased absorption of solar radiation and thus higher temperatures, ultimately leading to the albedo decreasing further. Information on the surface albedo is therefore necessary for various applications and climate studies. Atmospheric reanalysis products answer this need, providing consistent multiyear datasets with good spatial coverage.

We have studied the Arctic sea-ice albedo in two reanalyses. First is ERA5, a global atmospheric reanalysis by the ECMWF (European Centre for Medium range Weather Forecasts). ERA5 has a horizontal resolution of 31 km, and sea-ice is modelled with a one-dimensional sea-ice parameterisation scheme.

The second reanalysis is CARRA (Copernicus Arctic Regional ReAnalysis), a regional atmospheric reanalysis covering a part of the Arctic with two overlapping domains: the western domain centred around Greenland and the eastern over the European Arctic. The horizontal resolution is 2.5 km, and similarly to ERA5, sea-ice is modelled with a one-dimensional thermodynamic sea-ice scheme.

We compare the surface albedo of these two reanalyses to the satellite-based black-sky surface albedo product of the CLARA-A2.1 dataset (CM SAF cLoud, Albedo and surface RAdiation dataset from AVHRR data). Comparisons are made for April to September, 2000-2015, for the sea areas of the CARRA domains. In addition to a general assessment, four different regions within the domains are studied separately.

How to cite: Kallio-Myers, V., Batrak, Y., and Cheng, B.: Comparison of Arctic sea-ice albedo between CARRA and ERA5 reanalyses and satellite based CLARA-A2, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1510, https://doi.org/10.5194/egusphere-egu23-1510, 2023.

We have published [ https://doi.org/10.1002/qj.4342 ] recent results on winter jet stream wind speed changes in the eastern North Atlantic: there is no change for the past 40 years but a statistically significant increase for the past roughly 20 years (2002-2020).  The increase shows up in both the Global Aircraft Data Set (GADS) observations from flight data recorders and the ERA5 reanalysis.  The wind speeds seem to track the North Atlantic Oscillation (NAO).  We can consider four possibilities: ( 1 ) synoptic fluctuation; ( 2 ) improved aircraft routing, though inconsistent with NAO correlations; ( 3 ) greater number of automated aircraft observations; ( 4 ) actual secular change in the polar jet exit region of the atmosphere.  This type of study must deal with subtleties of North Atlantic track system that includes aircraft step climbs.  We will present newer results on the secular increase in automated aircraft observations and the effects of including more recent Northern Hemisphere winters (2021 through, possibly, 2023).

How to cite: Tenenbaum, J. and Williams, P.: Winter jet stream wind speed changes in the eastern North Atlantic, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1687, https://doi.org/10.5194/egusphere-egu23-1687, 2023.

Slope and aspect are important topographic elements for thermodynamics and dynamics of atmospheric circulation, especially for local radiation and topographic precipitation. We propose a simple realistic statistical method based on trigonometric function transformation to calculate sub-grid slope and aspect for describing the orographic characteristics of complex areas over the globe. It is found that the transformed conditional probability density function (PDF) conforms to the Gaussian distribution in most of the global areas (~98%), and this feature is not eliminated with the increasing of horizontal resolution. The reasonability of this method is tested over the Tibetan Plateau. The results show that the improvement ratio of surface solar radiation downward (SSRD) over the Tibetan Plateau improved significantly compared with the results from the grid average scheme, especially in autumn. The improvement of root mean square error (RMSE) is approximately 18.2 W/m², and the improvement ratio reached 38.4%. The improvements of maximum and regional-averaged SSRD over the whole Tibetan Plateau were ~130 W/ m² and ~44.3W/m² respectively. Although we only consider the effect of sub-grid slope and aspect on solar shortwave radiation, which has a certain bias with the observation data, it is sufficient to prove the rationality of the statistical method compared with the unobstructed horizontal surfaces scheme (CTL). After that, we applied this sub-grid parameterization scheme for topographic vertical motion in CAM5 to revise the original vertical velocity by adding the topographic vertical motion and then resulting a significant improvement of simulation in precipitation over steep mountains.

How to cite: Wang, Y., Wang, L., Feng, J., and Song, Z.: A statistical description method of global sub-grid topography for numerical models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1830, https://doi.org/10.5194/egusphere-egu23-1830, 2023.

In numerical models, the amount of clouds affects atmospheric temperature through interaction with radiation. In the Korean Integrated Model (KIM), which has been in operation at the Korea Meteorological Administration since April 2020, the amount of clouds is determined from prognostic equation consisting of source and sink terms  by  physical processes such as planetary boundary layer (PBL) mixing, convection, advection, condensation, and evaporation. In the control KIM version, the temperature forcing used for calculating the rate of changes in time of saturation specific humidity which determines formation of cloud area due to condensation is calculated by considering those from all physical processes, such as radiation, cumulus convection, and turbulence, as well as cloud microphysical processes. However, we found the inconsistency between the cloud fraction and mixing ratio by using the methodology, so in this study, we modify the temperature forcing from all physical processes into that only due to the microphysical process. It is confirmed that the change in the amount of clouds changes the temperature and humidity of the atmosphere through the interaction between physical processes such as radiation, which also affects precipitation. 
In this study, to examine the effect of changes in cloud cover on precipitation in the Korean Peninsula, we perform one case study July 4, 2021 when precipitation in Gangwon occurs due to the convergence of air currents caused by east wind of high pressure in the eastern of Korea. Up to 172.5 mm of daily maximum precipitation was reported in the Gangwon region. In the 3-day forecast of the case, the control KIM  underestimates inland precipitation. But the trend of under-estimation is improved by increasing the amount of precipitation when the cloud amount is modified. The increase in precipitation mainly occurs in the large-scale precipitation due to the microphysical process. This is because the cloud amount generally increases in the Asian area including the Korean Peninsula, which makes the environment favor to the precipitation, by decreasing the temperature through the radiative cooling, in turn resulting the decrease in saturation vapor pressure. For the statistical evaluation of the precipitation performance, precipitation verification is also performed for one month in July, and it is found that ETS (Equivalent Threat Score) performance against the  reanalysis data on the Korean Peninsula is also improved.

How to cite: Park, Y., Lim, J.-O., Choi, H.-J., Cho, K., Baek, S.-K., and Cheong, S.-H.: Impact of changes in the cloud amount due to condensation processes on precipitation forecasting on the Korean Peninsula during the summer in the Korean Integrated Model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2466, https://doi.org/10.5194/egusphere-egu23-2466, 2023.

Ensemble sensitivity is a tool to quantitatively determine which initial conditions influence a forecast quantity of choice. This information can then be used to understand the sources and dynamics of forecast uncertainty, quantify the impact of observations (e.g., E-FSOI), and determine where to best deploy observations to improve the forecast (e.g., observation targetting and network design). The ensemble sensitivity is calculated from the covariances of the initial ensemble to the forecast ensemble. Unfortunately, these covariances are prone to sampling errors due to the limited ensemble size. The most common approach in data assimilation to mitigate sampling errors is to apply distance-based damping, i.e., localization. This poster explores how to localize the sensitivity correctly and how it differs from analysis localization. Using simplified problems, we highlight the benefits and drawbacks of sensitivity localization and discuss its usefulness to numerical weather prediction applications.

How to cite: Griewank, P. J., Necker, T., and Weissmann, M.: Ensemble sensitivity localization, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2827, https://doi.org/10.5194/egusphere-egu23-2827, 2023.

National Oceanic and Atmospheric Administration’s (NOAA’s) Environmental Modeling Center (EMC) is a lead developer of Numerical Weather Prediction (NWP) systems that also transitions to operations and maintains more than 20 numerical prediction systems that are used across the National Weather Service (NWS), the broader NOAA, by other United States (U.S.) federal agencies, and various other stakeholders. These systems are developed through a close collaboration with partners from the academic, federal and commercial sectors. 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 the global community and for the protection of life and property and the enhancement of the economy.

NOAA’s Next Generation Global Prediction System (NGGPS) Project initiated a major shift in the development of operational Earth system predictions with a goal to simplify the National Centers for Environmental Prediction (NCEP) Production Suite using the Unified Forecast System (UFS) framework (https://ufscommunity.org/). EMC has taken a lead in further development and consolidation of NCEP’s operational systems into UFS based applications.  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 local to global domains and predictive time scales ranging from sub-hourly analyses to seasonal predictions.  Disparate 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 a few years ago and is planned to continue over the next few years. The resulting applications will consolidate NCEP’s Production Suite into far fewer applications that share a set of common scientific components and technical infrastructure.  This approach 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 few years and the plans for the next five years (2023-2027), how those fit within the broader strategic plans of NOAA, and how these projects link with other model-related projects internally within NOAA and with the broader U.S. and international modeling community.

How to cite: Stajner, I., Gross, B., Tallapragada, V., Levit, J., Chawla, A., Mehra, A., Kleist, D., and Yang, F.: Advancing Operational Modeling Systems at NOAA’s Environmental Modeling Center: Transitioning to Unified Forecast System Applications, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2982, https://doi.org/10.5194/egusphere-egu23-2982, 2023.

Since the dynamical core of Korean Integrated Model (KIM) was developed in the 1st phase (2011~2019) of KIAPS, we have been aiming to develop a variable resolution prediction system covering short to medium range in the 2nd phase (2020.9~2026). As a first step towards moving to km-scale resolution, we have increased the model resolution from 12 km to 8 km horizontally and 91 to 137 layers vertically. For increasing the resolution horizontally, dynamics core configurations and terrain elevation data were newly set up. For vertically, vertical coordinates of 137 layers followed that of the European Center for Intermediate Forecasting (ECMWF) Integrated Forecasting System (IFS), which has been increased vertical resolution throughout the troposphere and stratosphere comparing to 91 layers.
This study discusses the forecast impact of high-resolution KIM in terms of objective verification scores against observations and analyses. The overall conclusion for horizontal high-resolution is that it shows slightly positive in southern hemisphere and mainly neutral for northern hemisphere, but also some negative in tropics. One of distinguished results is increasing horizontal resolution leads to cooling in the temperature in the lower and upper troposphere. The cooling in the lower tropospheric over the tropics seems to come from smaller time step that has to be for smaller dx, which results in enlarged low cloud formation and thus more radiative cooling. In case of the upper troposphere, the cooling results from outgoing long-wave radiative cooling by decreasing hydrometeors in physical response to smaller grid spacing. The increase of vertical resolution had an effect of neutral to slight positive in northern hemisphere but showed significant degradation in tropics. To achieve the consistency and improvement for high-resolution model, it is necessary to understand the physical processes related to time step and horizontal and vertical grid spacing.

Acknowledgement
One of the authors, S-J Choi, wishes to acknowledge this study was supported by 2023 New Professor Support Program of Natural Science Research Institute in Gangneung-Wonju National University).

How to cite: Park, J.-R., Kong, H.-J., Nam, H., and Choi, S.-J.: Effects of increasing horizontal and vertical resolution in Korean Integrated Model (KIM), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3025, https://doi.org/10.5194/egusphere-egu23-3025, 2023.

NOAA is collaborating with the US weather and climate science community to develop the next generation fully coupled earth system modeling capability for both research and operational forecast applications across different temporal and spatial scales.    In this presentation we explore the possibility of running the UFS at convection-permitting resolution for global medium-range weather forecasting.  A few sensitivity experiments were performed at a global uniform 3-km resolution with and without parameterized convection.  Results were compared with the 13-km control experiments to investigate the impact of model resolution and convection parameterization on precipitation and cloud-radiation interaction.   Aerosol indirect effect on clouds is also tested and evaluated within this framework to understand its sensitivity to model resolution and parameterized convection.  Aerosol indirect effect occurs when aerosols act as cloud condensation nuclei and ice nuclei within clouds and consequently alter cloud radiative properties and cloud lifetime.   Using the Thompson double-momentum microphysics scheme, the number concentrations of water friendly aerosol and ice friendly aerosol are either diagnosed from the MERRA2 aerosol climatology or predicted and advected with source and sink terms derived from the climatology. The relations between clouds, radiation and precipitation with and without the presence of aerosol indirect effects are analyzed for simulations made at both the control 13-km and experimental 3-km UFS model resolutions.  

How to cite: Yang, F., Chen, A., and Moorthi, S.: Prototyping Convection-Permitting Global Weather Forecast and the Representation of Aerosol-Cloud-Radiation Interaction in the NOAA Unified Forecast System, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3038, https://doi.org/10.5194/egusphere-egu23-3038, 2023.

Urbanization results in drastic land alteration in which natural land cover is replaced by impermeable surfaces such as compacted soils, buildings and associated infrastructures. While the impact of urbanization on extreme rainfall is captured in satellite data to a great extent, its signal is frequently less obvious in station-level data. Also, the lack of local meteorological data hinders the development of adequate mitigation measures to reduce the impact of extreme rainfall scenarios. To regenerate the local meteorological data, numerical model-based simulations using global boundary conditions are required at finer Spatio-temporal scales. To this end, integrated land surface models which can provide the maximum likelihood of observed rainfall can be of great significance, especially in urban complexes. Weather Research Forecasting (WRF) model is one such numerical model that can lay down a framework to provide short-range weather forecasts by fixing site-specific physics-based parametrization schemes. This study demonstrates the application of the WRF model to provide building-scale weather forecasts based on the finer-scale Urban Canopy Model (UCM) and Local Climate Zonation (LCZ). The numerical modelling framework is set up for Bangalore city, India. Bangalore city is categorized as one of the major urban complexes with a total built-up area of 77.5%. The World Urban Database Access Portal Tool (WUDAPT), which is based on random forest classification of the ground truth training samples, is used to develop the LCZ database for the WRF model. A single-layer UCM is developed to indicate the importance of structural and aerial characteristics of static datasets with appropriate land features. WRF model runs are carried out based on global boundary conditions to provide a 24hr forecast with 3km and 1km spatial domain for the study area at an urban scale. The overall accuracy of 92% (for the built-up area) and 85% (for water bodies) is obtained for LCZs developed using the random forest classification in WUDAPT. In comparison to default configurations of WRF, the forecasts of WUDAPT-based LCZs have shown an improvement at both spatial and temporal scales. The bias (particularly the spatial shift) observed using the default WRF is reduced drastically, and the forecasts are well-matched with the observed Telemetric Rain Gauge (TRG) station rainfall datasets. Assessment of the maximum likelihood of extreme rainfall forecasts can provide a platform for the development of an integrated WRF hydrological configuration in the future. Such frameworks will be greatly beneficial for obtaining more accurate rainfall and flood forecasts.

How to cite: Gaddam, N., Wadhwa, A., Pentakota, L., Reghunath, G., and P Mujumdar, P.: Integration of the WRF Model With Fine-Scale Land Use Data to Simulate Extreme Rainfall Events, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3183, https://doi.org/10.5194/egusphere-egu23-3183, 2023.

How to cite: Nardi, K., Zarzycki, C., Larson, V., and Bryan, G.: The Role of Parameterized Momentum Flux on Biases in Tropical Cyclones and the Mean State in the Community Atmosphere Model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3545, https://doi.org/10.5194/egusphere-egu23-3545, 2023.

The eddy diffusivity and mass flux (EDMF) scheme for simulating turbulent transport in the operational Global Forecast System (GFS) shows a behavior due to a physical and numerical inconsistency in the scheme's numerical procedure to obtain detrained cloud water due to the moist nonlocal mixing and its associated moist adjustment.  One-dimensional simulations show that such inconsistency leads to an unphysical distribution of thermal tendencies and detrained cloud water near the simulated planetary boundary layer (PBL) top.  To solve this problem, a new procedure to obtain a positive definite solution from the scheme is proposed to solve the EDMF equations in the scheme. We will show the impact of this new solution procedure on the GFS performance.

How to cite: Bao, J.-W., Grell, E., Michelson, S., and Hong, S.-Y.: A positive definite solution for an EDMF PBL scheme that includes a moist adjustment process, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3553, https://doi.org/10.5194/egusphere-egu23-3553, 2023.

The Common Community Physics Package (CCPP) is a collection of atmospheric physical parameterizations and a framework that couples the physics for use in Earth system models. The CCPP Framework was developed by the U.S. Developmental Testbed Center (DTC) and is now an integral part of the Unified Forecast System (UFS). The UFS is a community-based, coupled, comprehensive Earth modeling system designed to support research and be the source system for NOAA‘s multi-scale operational numerical weather prediction applications. The CCPP is also being used in the experimental U.S. Navy Environmental Prediction sysTem Utilizing the NUMA  corE (NEPTUNE, which employs a modified version of the Non-hydrostatic United Model for the Atmosphere dynamical core) and is currently being integrated into the Community Atmosphere Model (CAM) utilized in the National Center for Atmospheric Research (NCAR) Community Earth System Model (CESM).

A primary goal for this effort is to facilitate research and development of physical parameterizations, while simultaneously offering capabilities for use in operational models. The CCPP Framework supports configurations ranging from process studies to operational numerical weather prediction as it enables host models to assemble the parameterizations in flexible suites. Framework capabilities include flexibility with respect to the order in which schemes are called, ability to group parameterizations for calls in different parts of the host model, and ability to call some parameterizations more often than others. Furthermore, the CCPP is distributed with a single-column model (SCM) that can be used to test innovations,  conduct hierarchical studies in which physics and dynamics are decoupled, and isolate processes to more easily identify issues associated with systematic model biases. The CCPP SCM can be driven using files in the DEPHY format (an internationally agreed-upon format for inputs and outputs of SCMs). This opens doors for collaborations using multiple initial and forcing datasets based on observational field campaigns. The CCPP SCM is also being updated to be forced by the UFS.

The CCPP v6.0.0 public release includes 23 primary parameterizations (and six supported suites), representing a wide range of meteorological and land-surface processes. Experimental versions of the CCPP also contain chemical schemes, making it possible to represent processes in which chemistry and meteorology are tightly coupled.

The CCPP is developed as open-source code and has received contributions from the wide community in the form of new schemes and innovations within existing schemes. In this presentation, we will provide an update on CCPP development and plans, as well as review existing resources for users and developers, such as the public releases, documentation, tutorial, and support mechanism. We will also provide information about the upcoming CCPP Visioning Workshop, indeed to be a forum for current and future CCPP users to learn about its capabilities and discuss requirements for new development. 

How to cite: Bernardet, L., Firl, G., Swales, D., Zhang, M., Kavulich, M., Trahan, S., Li, W., Dudhia, J., and Ek, M.: Facilitating the development of complex models with the Common Community Physics Package and its Single-Column Model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3703, https://doi.org/10.5194/egusphere-egu23-3703, 2023.

An unprecedented heavy rainfall event occurred in Henan Province of central China during 19-20 July 2021. To investigate the impacts of predicted large-scale circulation on the regional convection-permitting prediction of this event, two sets of nested experiments with different convective parameterizations (GF and MSKF) in the outer domain and at convection-permitting resolution in the inner domain are performed with the Weather Research and Forecasting (WRF) model. The analysis found the prediction of “21.7” rainstorm at convection-permitting resolution in the inner domain is largely affected by convective scheme in the outer domain. The large-scale circulation forcing from the outer domain with different convective schemes is significantly different, which ultimately affects the circulation and precipitation in the refined region through lateral boundary forcing. The difference in regional prediction at convection-permitting resolution can be mitigated by adjusting convective latent heat parameterization in the outer domain. This work highlights that appropriately parameterizing convective latent heat is the key to provide reasonable large-scale forcing for regionally predicting this catastrophic heavy rainfall event at convection-permitting resolution, which may also be applicable to other events and other regions.

How to cite: Xu, M., Zhao, C., Gu, J., Feng, J., Li, G., and Guo, J.: Appropriately representing convective heating is critical for predicting catastrophic heavy rainfall in 2021 in Henan Province of China, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3781, https://doi.org/10.5194/egusphere-egu23-3781, 2023.

The characteristic adjustment time scale τ is always defined as the time allowed for dissipation of Convective Available Potential Energy (CAPE) in convective parameterization schemes. Previous studies indicate that, in the cloud ensemble, τ is one of the most important parameters that have  the greatest influences on the global mean precipitation. Some research work has improved the Kain–Fritsch scheme in the regional model to realize the variable parameters. In the global model, some studies have used machine learning methods to optimize the parameters of the deep convection trigger function. However, changing constant parameters into variable parameters in the global model has not been explored. In our study, the Zhang-McFarlane (ZM) deep convection scheme is improved to realize the variable characteristic adjustment time scale parameter, so as to reduce the precipitation deviation in a global model. In the ZM deep convection scheme, τ is usually the default constant. While in this paper, we use CAPE to modulate τ and propose a calculation formula of τ. In the region where the mean precipitation amount bias is improved, the new scheme mainly increases the deep convective precipitation and reduces the large-scale and shallow convective precipitation. The modified scheme significantly improves the simulation of precipitation over the eastern equatorial Pacific Ocean and some steep terrain regions. The root mean square error of the mean precipitation amount over the eastern equatorial Pacific Ocean and the central Indo-Pacific Warm Pool in boreal summer is reduced after the new scheme is adopted in a global model with the horizontal resolution of 1° longitude and 1° latitude. Moreover, the simulations of precipitation over the Tibet Plateau and South America are also improved. The new scheme reduces the frequency of deep convective precipitation and increases the amount of deep convective precipitation each time.

How to cite: Wang, M. and Wang, L.: Simulation of Precipitation with a Variable Characteristic Adjustment Time Scale Parameter of Deep Convection in a Global AGCM, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4649, https://doi.org/10.5194/egusphere-egu23-4649, 2023.

The ensemble forecast system based on the Korea Integrated Model (KIM), which is developed for Korea’s own numerical weather prediction (NWP) model, has been in operation at Korean Meteorological Administration (KMA) since October 2021. KMA ensemble forecast system consists of 50 perturbation members (25 members for long-range forecast) and 1 control simulation. Four-dimensional LETKF (Local Ensemble Transform Kalman Filter) with additive and RTPS inflation scheme is used to make initial perturbation. 
Evaluation of forecast scores shows that our operational ensemble forecast system is generally more skillful compared to the deterministic simulation as forecast time is longer. Also, forecast with increased ensemble size produces better representation of atmospheric fields especially in higher latitudes. Details of results from operational ensemble system and impacts of increased ensemble size will be discussed with introducing a brief overview of our ensemble forecast system and development plan in future. 

How to cite: Kim, E.-J., Shin, H.-C., Park, J. I., Ha, J.-C., and Kwon, Y.-C.: Status and plan of ensemble forecast system in Korea Meteorological Administration (KMA), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4730, https://doi.org/10.5194/egusphere-egu23-4730, 2023.

Data assimilation (DA) is a tool that is capable of combining observations and numerical weather models (NWMs) in an optimal manner. Current DA systems used by operational forecasting centres are constantly evolving and getting better than before. High-quality observations are very important for the accurate representation of variables in a weather model. In this study, we are incorporating Global Navigation Satellite System (GNSS) tropospheric gradients and Zenith Total Delays (ZTDs) into the Weather Research and Forecasting (WRF) model. WRF model has its operator already developed for the ZTDs and in this research, we are developing a new operator for the assimilation of tropospheric gradients. The assimilation of ZTDs, which are closely related to Integrated Water Vapor (IWV) above the GNSS station, has a positive impact on weather forecasts. On the other hand, tropospheric gradients are not yet assimilated by the weather agencies. Our research is based on a project titled “Exploitation of GNSS tropospheric gradients for severe weather Monitoring And Prediction” (EGMAP) focusing on the impact of GNSS tropospheric gradients and how it can be effectively used for operational forecasting of severe weather. EGMAP is funded by the German Research Foundation (DFG).

The observation operator currently in use for tropospheric gradients is based on a linear combination of ray-traced tropospheric delays (Zus et al., 2022). This observation operator is challenging to be implemented into an NWM DA system. We will thus rely on a more simple and fast observation operator which is based on the closed-form expression depending on the north–south and east–west horizontal gradients of atmospheric refractivity (Davis et al., 1993).

Initial testing of the operator is done on a 0.1 x 0.1-degree mesh configured over Central Europe in the WRF model with 50 vertical levels up to 50 hPa. The model configuration will be later upgraded to a convective-scale resolution after initial testing of the tropospheric gradient operator. Model forcing observations are derived from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) data at 0.25-degree resolution. Conventional observations are obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) and are the base dataset for the assimilation studies. The conventional datasets used for assimilation are restricted to surface stations (SYNOP observations) and radiosondes. Additionally, observations from roughly 100 GNSS stations are assimilated at each DA cycle. Three experiments are conducted: 1) Control run with only conventional data; 2) ZTD assimilation on top of the control run, and; 3) ZTD and tropospheric gradient assimilation on top of the control run. Initial DA tests are being performed with an automated rapid update cycle DA framework with 6 hourly intervals based on a deterministic three-Dimensional Variational (3DVar) DA system for the testing of ZTDs and tropospheric gradients. The DA system will be later upgraded to a probabilistic one based on the Hybrid 3DVar-Ensemble Transform Kalman Filter (-ETKF, Thundathil et al., 2021) and 4DEnVar. The EGMAP project status and initial results from the impact of GNSS-ZTDs and tropospheric gradients will be presented.

How to cite: Thundathil, R. M., Zus, F., Dick, G., and Wickert, J.: Exploitation of GNSS tropospheric gradients for severe weather Monitoring And Prediction (EGMAP): Project status and Initial results, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5548, https://doi.org/10.5194/egusphere-egu23-5548, 2023.

The representation of upper tropospheric deep convective divergent outflow (UTDCDO) is compared between ICON-simulations with convection-permitting and convection parameterised set-ups (1 and 13 km resolution) for a convective event over Germany and the Alps on June 10th-11th 2019. Three hypotheses on those UTDCDO have been formulated using idealised Large Eddy Simulations and are now tested on ICON in a convection-permitting set-up: 1. Dimensionality affects the magnitude of UTDCDO in ICON; 2. Convective aggregation and organisation affects the magnitude of those convective outflows in ICON and 3. Convective momentum transport does not affect the magnitude of UTDCDO. A moving box is used to integrate mesoscale divergence, precipitation rate and convective momentum transport. Additionally, ellipse fitting is used to make estimates of convective organisation (dimensionality, area of convective precipitation, etc.).
Variability in UTDCDO at a given net latent heating rate is reduced in ICON with parameterised deep convection, compared to the convection-permitting set-up. Hints, but no conclusive results are found on the effect of dimensionality on the magnitude of UT divergent deep convective outflows. An impact of convective organisation and aggregation on UTDCDO is significant in the dataset: as a consequence of outflow collisions, UTDCDO increases sub-linearly with net latent heating. We also found a statistical relation between normalised UTDCDO and normalised convective momentum transport.  

How to cite: Groot, E., Kuntze, P., Miltenberger, A., and Tost, H.: Upper tropospheric convective outflow in ICON convection-permitting and parameterised set-up, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6532, https://doi.org/10.5194/egusphere-egu23-6532, 2023.

The sub-seasonal characteristics and prediction of rainfall over the Asian Monsoon Area during spring-summer transitional season (April-May-June) are investigated using a full set of hindcasts generated by the Dynamic Extended Range Forecast operational system version 2.0 (DERF2.0) of Beijing Climate Center, China Meteorological Administration. The onset and development of Asian summer monsoon and the seasonal migration of rain belt  over East Asia can be well depicted by the model hindcasts at various leads. However, there exist considerable differences between model results and observations, and model biases depend not only on lead time, but also on the stage of monsoon evolution. In general, forecast skill drops with  increasing lead time, but rises again after lead time becomes longer than 30 days, possibly associated with the effect of slowly-varying forcing or  atmospheric variability. An abrupt turning point of bias development appears around mid-May, when bias growths of wind and precipitation exhibit significant changes over the northwestern Pacific and South Asia, especially over the Bay of Bengal and the South China Sea. This abrupt bias change is  reasonably captured by the first two modes of multivariate empirical orthogonal function analysis, which reveals several important features associated  with the bias change. This analysis may provide useful information for further improving model performance in sub-seasonal rainfall prediction.

How to cite: Li, Q., Wang, J., and Yang, S.: Sub-seasonal Variations and Predictions of Precipitation over the Asian Monsoon Area with BCC_DERF2.0 in Spring-Summer Transition Season, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6653, https://doi.org/10.5194/egusphere-egu23-6653, 2023.

The daily maximum and minimum temperatures are among the most relevant meteorological variables in weather forecasts and climate monitoring. Their spatial and temporal evolution from synoptical to decadal scales are driven by numerous physical processes and climate feedbacks. Despite the significant improvements in weather forecasting over the last decades, forecasts of daily temperature extremes are still hampered by systematic errors. In this work we perform an integrated evaluation of the daily temperature extremes of the (i) ECMWF ERA5 reanalyses and (ii) ECMWF operational weather forecasts. The observations for the evaluation are taken from the Global Historical Climatology Network (GHCN) addressing: (i) the long-term assessment of the analysis produced by the ERA5 reanalysis, comprising a 40-year period (from 1980 to 2019); and (ii) the assessment of the ECMWF operational forecasts for a 5- year period (from 2017 to 2021). The evaluation carried out is global, however considering the GHCN station distribution and temporal availability, particular focus was given to four regions: Europe, Australia, East and West United States. The results identify a general underestimation of the daily maximum and overestimation of the daily minimum temperatures in both ERA5 analysis and operational forecasts, highlighting a known limitation of the ECMWF model in underestimating the diurnal temperature range. Our results also indicate a reduction of the errors in ERA5 when comparing the latest decade with the 1980’s, which is likely to be associated with an enhanced quality of the analysis due to a higher constrain emerging from the satellite data. The ERA5 analysis outperforms 1 day-ahead weather forecasts, which show some degree of improvement in the considered 5-year period, being associated with model upgrades.

This work was developed in the framework of the CoCO2 project. CoCO2 project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 958927.

How to cite: Lopes, F., Dutra, E., and Boussetta, S.: Evaluation of Daily Temperature Extremes in the ECMWF ERA5 Reanalysis and Operational Weather Forecasts, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7064, https://doi.org/10.5194/egusphere-egu23-7064, 2023.

The Arome numerical weather prediction system is routinely used for weather forecasting over the mountains of the French Alps, Pyrénées and Corsica. However, its skills at temperature forecasting are altered by several 2 m temperature biases: (1) a cold bias at high altitude, (2) a low-altitude warm bias occurring in stably stratified layers and (3) a warm bias during snowfall situations.

Targeted numerical simulations (successive activation of some dynamic, physical and assimilation modifications) were carried out on the day of January 12, 2021, a problematic snowy situation in the Arve valley (Haute-Savoie, French Alps).

Over this period, the operational version of Arome has a mean absolute error (MAE) of 2.3°C in the valley. The increase of vertical resolution does not improve the performance of the model in the valley. The MAE is nevertheless decreased from 1.4 to 1.1°C in the mid-altitude range and from 1.5 to 1.2°C above 2000 m. Conversly, the use of a new surface scheme (ISBA-DIF) associated with a more complex snowpack model (ISBA-ES) allows to better represent the arrival of the warm front in the valley and reduces the error (to 1.8°C) whatever the altitude. The current surface scheme therefore seems too simplistic to correctly model soil-atmosphere interactions in the mountains. Forcing Arome with full-day data assimilation also reduces the bias in the valley (to 2.0°C). However, this experiment deteriorates the scores in the mid-altitude and high-altitude mountains. Furthermore, the situation has a poor initial state as biases are present even before the snow event starts. This may point towards deficiencies in the assimilation of in-situ data in mountain regions, that should be overcome in future work.

These results show that the warm bias during this snowy event has multiple origins. A carefull analysis of other situations will be needed to confirm and correct theses biases. 

How to cite: Préaux, D., Dombrowski-Etchevers, I., Gouttevin, I., and Seity, Y.: Study of the 2 m temperature bias of the numerical weather forecasting model Arome over the French Alps, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7481, https://doi.org/10.5194/egusphere-egu23-7481, 2023.

Infrasound waves generated by phenomena at the Earth’s surface can travel to these levels before returning to the surface and being detected. Observations like travel time, change in backazimuth angle, and trace velocity contain integrated information of all the levels the wave travelled through. These often include stratospheric and mesospheric levels which are otherwise poorly observed.
In this work we take a data assimilation technique, the Modulated Ensemble Transform Kalman Filter, which is commonly used in satellite data assimilation, and illustrate how it can be readily used for infrasound data assimilation. We highlight the similarities between the two problems, and the particular challenges in extracting information from summarised quantities. To our knowledge, this is the first work doing data assimilation with a full ray-tracing model as forward operator.

How to cite: Amezcua, J. and Näsholm, S. P.: Using satellite data assimilation techniques to combine infrasound observations and a full ray-tracing model to constrain atmospheric variables, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8665, https://doi.org/10.5194/egusphere-egu23-8665, 2023.

This presentation provides an overall summary of the project PREVENIR and recent activities about data assimilation and numerical weather prediction (NWP) research. 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 designed for two highly vulnerable urban basins in Argentina: one located in Buenos Aires Province and the other in Córdoba Province. PREVENIR takes advantage of leading research on simulations and 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, quantitative precipitation estimates (QPE), nowcasting, BDA and NWP, hydrological model prediction, warning communications, public education, and capacity building. Here, the Japanese leading institutions in the scientific research and operational services, i.e., RIKEN, Osaka University, the International Centre for Water Hazard and Risk Management (ICHARM), and the Japan Meteorological Agency (JMA) closely work with the Argentinian counterparts, i.e., the National Meteorological Service, the National Water Institute, and the National Research Council of Argentina under the strong support of JICA, JST, and Argentinian Foreign Affairs Ministry. Heavy rains and urban floods are important global issues under the changing climate. 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., Saulo, C., Otsuka, S., Ruiz, J., Skabar, Y. G., Amemiya, A., Ushio, T., Tomita, H., Ushiyama, T., and Konishi, M.: PREVENIR: Japan-Argentina Cooperation Project for Heavy Rain and Urban Flood Disaster Prevention, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8919, https://doi.org/10.5194/egusphere-egu23-8919, 2023.

Abstract: With climate change, rainfall is expected to get more intense, leading to cities being increasingly at risk of urban flooding. Understanding local climate change over cities has therefore become a priority for the scientific community and city planners on building resilient cities and mitigating hydrometeorological disasters. Very high resolution (km-scale, ‘convection-permitting’) climate models are required to adequately represent cities and local rainfall extremes. Here we assess the Weather Research and Forecasting (WRF) model for simulating urban rainfall. Despite the wide application of WRF in rainfall simulations (including urban areas), there are limited investigations on the impact of the domain size and how to search for a suitable domain size over a particular city region.

To fill this knowledge gap, Newcastle upon Tyne is selected as the study area to simulate a summer heavy rainfall event with ERA5 (a fifth-generation dataset of global reanalysis developed by the European Centre for Medium-Range Weather Forecasts) as the input data and a radar product from the UK Met Office for validation. Accordingly, different domain sizes with the convection-permitting resolutions from 1 km to 4.5 km (increment: 0.5 km) are explored, and the hourly model outputs are compared with the radar observation data.

This study has proposed and tested a method to decide the most suitable domain size. By using eight assessment indexes (including pattern, cumulative time series, hourly time series, particular values (max/min/mean) as well as the seven statistical indicators of each data and overall data), there are two preliminary conclusions: 1) 200 km × 200 km is the best domain size for the single domain simulation; 2) For 200 km × 200 km or smaller domain sizes, higher resolution produces better results, but for 250 km × 250 km or large domain sizes, resolution sensitivity is opposite. Regarding next steps, the above procedure will be further investigated by applying it to more extreme rainfall case studies and to other cities in order to assess whether results here are generally applicable, and therefore the optimal domain configuration can be usefully applied to produce reliable urban rainfall simulations.

How to cite: Du, S., Zhuo, L., Kendon, E. J., and Han, D.: Exploring Domain Size for WRF High-Resolution Urban Rainfall Simulation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8950, https://doi.org/10.5194/egusphere-egu23-8950, 2023.

We explored the sensitivity of the atmosphere general circulation model OpenIFS to horizontal resolution and time step. We conducted a series of experiments at different horizontal resolutions (i.e., 100, 50, and 25 km) while maintaining the same time step (i.e., 15 minutes), and using different time steps (i.e., 60, 30 and 15 minutes) at 100 km horizontal resolution. We find that the zonal wind bias over the Southern Ocean has significantly reduces at high horizontal resolution (i.e., 25 km), and that this improvement is evident too when using a coarse resolution model with smaller time step (i.e., 15 min and 100 km horizontal resolution). There is also evidence of improvements in the mid-latitude westerly jet in the Northern Hemisphere too, which is also sensitive to both model time step and horizontal resolution. We have also found that the biases in wave speed and wave amplitude reduce when we shorten the model time step or increase the model horizontal resolution. Therefore, it is clear that the improvement in the highest horizontal resolution (i.e., 25 km) simulation is a combination of both the enhanced horizontal resolution and shorter time step. We speculate that the improvement in the surface zonal wind bias in the coarse resolution with shorter time step (i.e., 15 min and 100 km horizontal resolution) simulation is mostly due to shallow convection that is intensified at shorter time step. In addition, we have also noticed improvements in the surface-air temperature when a high resolution and a smaller time step; however, the precipitation bias is independent of the model’s horizontal resolution and time step.

We propose based on OpenIFS that by reducing the time step in a coarse resolution atmospheric model (at least in OpenIFS), one can alleviate the surface-wind biases in the extratropics that is important for e.g., climate modeling in the Southern Ocean sector.

How to cite: Savita, A., Kjellsson, J., Pilch Kedzierski, R., Park, W., Latif, M., and Wahl, S.: ECMWF-OpenIFS Climate Sensitivity to Horizontal Resolution and Time Step, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9199, https://doi.org/10.5194/egusphere-egu23-9199, 2023.

Significant problems in numerical weather prediction modeling systems appear when the horizontal grid-spacing is between 20 km and 1 km and when deep convection is important. These scales are usually termed “Gray Scales”.  Techniques have been developed so that the behavior of the convective parameterization changes with the horizontal grid spacing of the model; such parameterizations are said to become “scale-aware”. Commonly used techniques involve applying a scaling approach to smoothly transition from parameterized to resolved convection. These are similar to an elegantly simple mathematical method originally developed by Arakawa et al. (2011), which scales the convective tendencies in dependence on the convective area fraction. Here we show that the scaling approaches are flawed, since they fail to consider the fact that the impacts of deep convection on those scales are not limited to one grid box, and usually – because of the scaling -- leads to light precipitation covering too much area, as we have previously shown in HRRR simulations. Any scaling approach is especially flawed in areas of light forcing (such as daytime heating) and in the tropics, when the explicit microphysics parameterization is not yet producing precipitation. We will show examples of these problems and discuss possible solutions as applied to NOAA’s new RRFS storm-scale modeling system.

How to cite: Grell, G., Li, H., and Freitas, S.: Flaws of scale-aware techniques in convective parameterizations, as discovered in NOAA’s operational convection-allowing modeling systems: an attempt to improve them., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10514, https://doi.org/10.5194/egusphere-egu23-10514, 2023.

A squall line system that occurred on 9-10 April 2016 over southern China was used to investigate the impact of incremental analysis update (IAU) initialization under the replay configuration on its forecasts. The ERA5 global reanalysis and the forecast field of the regional Weather Research and Forecasting (WRF) Model were used to construct the analysis increment. The results showed that IAU initialization reduced the imbalance caused by the introduction of the low-resolution global reanalysis into the high-resolution regional WRF model and retained the microphysical information in the forecast field of the regional WRF model, which reduced the spin-up time. Compared with the cold start run initialized directly by the ERA5 reanalysis, the linear structure and precipitation distribution of the squall line system using IAU initialization were closer to those in the observations. Further analyses indicated that the improvement of the squall line forecasts using IAU initialization was mainly related to the faster development of cold pool caused by retaining the microphysical information in the forecast field of the regional WRF model and the more favorable stratification conditions corrected by the IAU increment.

How to cite: gao, Y. and wang, X.: Impact of incremental analysis update initialization under the replay configuration on forecasts of a squall line event in southern China, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11619, https://doi.org/10.5194/egusphere-egu23-11619, 2023.

A better understanding of the thermodynamics of heavy precipitation events is necessary towards improving weather and climate models and quantifying the impact of climate variability on precipitation. However, there are limited observations available to assess the thermodynamics model structure within heavy precipitation conditions.

In 2009, the Earth Observation Group at ICE-CSIC/IEEC conceived the polarimetric radio occultations (GNSS-PRO) technique with the aim to obtain simultaneous measurements of the vertical structure of precipitation and its associated thermodynamic state. Based on the standard GNSS radio occultation technique (GNSS-RO), polarimetric RO consists of an identical instrument working at two orthogonal linear polarizations (H,V) instead of the conventional circularly polarized antenna. This allows us to measure the differential phase delay at both ports, hypothesized to be positive in the presence of asymmetric hydrometeors (large raindrops, snowflakes, ice aggregates). This technique is being tested for the first time on the proof-of-concept mission Radio Occultations and Heavy Precipitation (ROHP) aboard PAZ satellite, operating since 2018. The results of the first 4 years of PRO observations already showed sensitivity to heavy precipitation and its associated cloud structures.

Such technique provides high quality thermodynamic observations of water vapor, temperature and pressure with high vertical resolution, along with the vertical measurements of the phase delay linked to the precipitation structure. This study focuses on comparing these observations with the simulations based on the outputs of several operational models and reanalysis for a set of selected cases. The main objectives of the study are: (1) To check if the models reproduce the main features of the actual data; (2) to assess whether different models/schemes result in different GNSS PRO observables, and whether these differences are larger than the measurement uncertainty; and (3) to examine the utility of PAZ GNSS PRO observations for model validation and diagnosis.

This effort provides insight on future methods to assimilate the PRO profile alongside other conventional (non-polarimetric) RO data, including work towards building a forward operator. The exercise includes comparisons with ECWMF operational model, ERA-5 reanalysis, the operational NWP at the Japan Meteorological Agency, and a near-real-time implementation of the WRF regional model over the northeastern Pacific produced at the Center for Western Weather and Water Extremes (CW3E) called West WRF, forced with ECMWF and GFS.

How to cite: Padullés, R., Cardellach, E., Paz, A., Turk, F. J., Ao, C. O., Wang, K. N., de la Torre Juárez, M., Murphy, M. J., Haase, J. S., Lonitz, K., and Hotta, D.: A multi-center exercise on the sensitivity of PAZ GNSS Polarimetric RO for NWP modeling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12177, https://doi.org/10.5194/egusphere-egu23-12177, 2023.

Ensemble and hybrid ensemble-variational Data Assimilation (DA) methods incorporating ensemble-based flow-dependent error statistics into state estimation have emerged in recent decades. In a hybrid DA, the background error covariances are a combination of ensemble covariances and static climatology. The ensemble component provides flow-dependency and non-linear error growth critical for convective-scale models, and the static climatology mitigates the effects of a small ensemble size. Hybrid ensemble variational DA methods were recently implemented in the convective-scale NWP model AROME at Meteo-France.

We present our findings from testing Hybrid-3-Dimensional Variational Data Assimilation in convective-scale NWP model AROME over Austria. Given Austria's unique alpine orography, we investigate the impact of applying different weighting to flow-dependent covariances in hybrid DA for a summertime convection case over central Europe. In addition to the hybrid weights, we explore optimal ensemble size, the increase of ensemble size with a time-lagged approach as well as suitable localization settings. Finally, we compare our results to the 3-dimensional variational data assimilation (3DVar) operational model forecast of GeoSphere Austria and discuss the potential benefits, drawbacks, and challenges of using hybrid DA over traditional 3DVar.

Keywords: summertime convection; hybrid-3DEnVar; AROME NWP model; flow dependent background error covariance

How to cite: Jyoti, K., Weissmann, M., Griewank, P., and Meier, F.: Testing the AROME Hybrid 3DEnVar for convective-scale NWP over Austria, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12636, https://doi.org/10.5194/egusphere-egu23-12636, 2023.

The shape, sharpness and altitude of the extratropical tropopause (TP) is strongly linked to the position and the strength of the subtropical and polar jet streams that determine the weather in the midlatitudes. However, current numerical weather prediction models fail to correctly represent the sharpness of the TP (i.e., the gradients of wind and temperature). In this study, we address the question if and how the assimilation of radiosonde observations influences the TP representation and whether it acts to sharpen or smooth near near-tropopause gradient.

We investigate the influence by comparing temperature, Brunt-Väisälä frequency (N²) and wind profiles of the observations (y), the model background (x_b) and the analysis (x_a) in tropopause-relative coordinates.

In total, we analyse more than 9000 radiosondes that were assimilated by the European Centre for Medium-Range Weather Forecast’s Integrated Forecast System (ECMWF IFS) over Canada, the Northern Atlantic and Europe during a one-month period in fall 2016. To test whether the diagnosed influence is caused by the assimilated radiosondes, we conducted a data denial experiment that excluded 500 radiosondes that were launched in the framework of the North Atlantic Waveguide and Downstream EXperiment (NAWDEX) field campaign. In observation space, we investigate the departures (i.e., the differences between y, x_b and x_a) in the control run (CTR) with all radiosondes considered and the denial run (DEN) without the NAWDEX radiosondes.

The observed minimum temperature at the TP is overestimated in the background forecast (warm bias, ~1 K). Above, in a layer 0.5-2 km, the temperature is underestimated (~0.5 K). Consequently, the sharpness of the TP which is diagnosed by the maximum of N² is also underestimated. We show that data assimilation is able to improve the temperature and to slightly strengthen the TP in the analysis, particularly in situations where the observed and model TP altitude fairly agree. In the data denial experiment we show that this influence exists in the CTR, but not in the DEN run, and thus can be attributed to the assimilation of the radiosonde data.

Regarding wind, we find an underestimation of the maximum wind at and below the TP (0.5-1 m s^-1) and demonstrate that the assimilation of radiosonde winds is able to improve the wind profile across the TP. The bias and the positive influence are found to be stronger in situation of strong wind, i.e., the jet stream.

Although data assimilation is able to improve wind and temperature gradients across the tropopause by pulling the background closer to the observations, the individual analysis profiles still underestimate the sharpness of the tropopause. The misrepresented TP in models may impact the quality of weather and climate projections.

How to cite: Krüger, K., Schäfler, A., Craig, G., and Weissmann, M.: The influence of radiosonde observations on the sharpness and altitude of the tropopause, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12877, https://doi.org/10.5194/egusphere-egu23-12877, 2023.

ECMWF recent improvements on scientific and technological fronts will be presented. In 2021 two new operational upgrades of the Integrated Forecasting System (IFS), cycles 47r2 and 47r3, have been introduced. In 2022 the ECMWF High-Performance Computing (HPC) facility has migrated from Reading, UK to a new data centre in Bologna, Italy, and on 18 October 2022 the operational system has been ported to a new supercomputer with enhanced capacity, that will pave the way for an increase in resolution in 2023 with the implementation of IFS cycle 48r1.

IFS Cycle 47r2 was first introduced on 11 May 2021 and its key features included changing the vertical resolution of the Ensemble forecast system (ENS) from 91 to 137 levels, the same used in the high-resolution forecast (HRES). This was made possible by introducing single precision arithmetic in both the HRES and ENS forecast systems. The single precision itself is neutral but enabled the ENS change which led to significant forecast skill improvement. Five months later, ECMWF introduced Cycle 47r3 operationally on 12 October 2021. This included major changes to the model physics that had been under development for several years. A more consistent formulation of boundary layer turbulence, new deep convection closure and cloud microphysics changes have increased the realism of the water cycle.

The next science upgrade, cycle 48r1, will be implemented in 2023 on our new HPC system in Bologna. This will see an enhancement of the ENS horizontal resolution to the TCo1279 grid (approximately 9km), the same resolution currently used by the HRES. There will also be an increase of the data assimilation resolution used in the incremental 4D-Var minimisation, and the use a new object orientated approach to run the 4D-Var atmospheric data assimilation (OOPS). Other important changes in 48r1 include running a daily 100 members extended range ensembles, introducing a new multi-layer snowpack model, and improving the atmospheric energy and water conservation.

Looking further ahead, future higher resolution capabilities will be accelerated by the digital twin developments under the European Commission Destination Earth programme, which will build km-scale capability for a range of potential future HPC architectures. Major efforts have been invested in the code scalability of the Integrated Forecasting System to be able to run on GPUs and investigating alternative dynamical core options. Data assimilation will evolve towards a fully coupled approach to maximise the exploitation of observations and benefit all components of the Earth system (atmosphere, land, ocean) in a consistent way. Machine Learning (ML) will be exploited to enhance the performance and efficiency of our systems. 

Finally, our Copernicus partnership with the European Commission has just entered its second phase. Synergistic interactions between meteorology and composition will be pursued for the mutual benefit of both and preparatory steps for next ECMWF climate reanalysis, ERA6, and new seasonal forecasting system, SEAS6, have already started. Several major upgrades in ERA6 and SEAS6 will aim at mitigating against systematic model biases to produce climate records with significantly improved time consistency, and enhanced reliability for extended-range predictions.

How to cite: Balsamo, G., Rabier, F., Balmaseda, M., Bauer, P., Brown, A., Dueben, P., English, S., McNally, T., Pappenberger, F., Sandu, I., Thepaut, J.-N., and Wedi, N.: Recent progress and outlook for the ECMWF Integrated Forecasting System, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13110, https://doi.org/10.5194/egusphere-egu23-13110, 2023.

The development of regional reanalyses aims at the provision of high-resolution data sets that are suitable for climate applications and climate services. As the desired high-resolution information can barely be provided by either synoptic or remote sensing observation data, a growing interest in high-quality regional reanalyses is recognisable. Particular demand arises from the renewable energy sector. Further quality gains are expected by using an ensemble approach, e.g. by making available the desired uncertainty information when moving towards higher resolution. 
Within the framework of the Innovation Programme for applied Researches and Developments (IAFE) at Germany's national meteorological service (DWD) our project aims to develop and evaluate an operational ensemble-based regional reanalysis system incorporating the current NWP model of DWD (ICON). One final goal of the project is to provide a basic framework for user-oriented verification.  
We first present the Basic Cycling Environment (BACY) being mainly characterized by its modularity, robustness, user-friendlyness as well as its high complexity. Thus, our future reanalysis system will be a certain BACY version with "frozen" specifications. To assess BACY specifications such as model resolution, number of ensemble members, domain size and choice of output variables NWP simulations will be performed and first simulation results will be presented.

How to cite: Kelbch, A., Spangehl, T., Borsche, M., Rösch, T., and Imbery, F.: Ensemble-based regional reanalysis system for Central Europe: Development framework and outlook, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13449, https://doi.org/10.5194/egusphere-egu23-13449, 2023.

How to cite: Rogers, C. and Tingwell, C.: Forecast sensitivity to the assimilation of observational data - two case studies for Australia, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14259, https://doi.org/10.5194/egusphere-egu23-14259, 2023.

Atmospheric predictability is fundamentally limited by the upscale growth of initial small-scale, small-amplitude errors. Studying upscale error growth mechanism is essential to better understand this fundamental limitation. Upscale error growth is frequently investigated by spectral analysis. By design, however, spectral analysis is non-local. A local investigation of error growth in different flow configurations is desirable, though, to study the well-known flow dependence of error growth. We thus take here a complementary approach to spectral analysis and identify local regions of prominent errors as “error features”.

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 in previously published upscale error growth experiments with the global prediction Model ICON from the German Weather Service. Errors in these experiments grow from very small initial-condition uncertainty (three orders of magnitude smaller than current-day uncertainty) and due to differences in the seeding of a stochastic convection scheme.

Spatial composites centered on the centroid of error features indicate that features are primarily generated ahead of an upper-tropospheric trough. The environment surrounding the features at the time of their first detection is characterized by locally enhanced lower to mid tropospheric moisture, latent heat release, and upper tropospheric divergence. Subsequently, this moist-diabatic nature of the error environment becomes gradually less prominent. The evaluating of process specific error growth rates enables to quantify the upscale growth mechanics in more detail. 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 reproduces the previously found three-phased multi-scale upscale growth paradigm: Errors are first generated on the small scale by differences in latent heat release, then projected onto the tropopause region by associated differences in upper tropospheric divergent outflow, and finally amplified by nonlinear Rossby wave dynamics. The growth rates from a single feature, however, can substantially differ from the mean picture. Some features, e.g., go through the described stages in a cyclic sequence, and the main focus of the presentation will be on the differences between fast and slowly amplifying error features.

How to cite: Schmidt, S., Riemer, M., and Selz, T.: A feature based perspective on upscale error growth., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14671, https://doi.org/10.5194/egusphere-egu23-14671, 2023.

Precipitation characteristics are expected to change in the future as a consequence of global climate change. For example, high-intensity precipitation is expected to become more frequent in some areas of the world. The short time scales and small spatial scales of intense precipitation events pose challenges for numerical weather prediction (NWP) models. Measurements of precipitation characteristics from in-situ and remote sensing instrumentation are often available at much higher time resolution than common NWP model output, and need to be aggregated for validation studies. Here we present a methodology to enable the comparison of precipitation observations and model output at the time scale of the model time steps. Our analysis is focused on an extreme, convective precipitation event during 30th July 2019 in Bergen, Norway (60.38ºN, 5.33ºE, 12 m a.s.l.). We use high-resolution measurements of precipitation characteristics from a Micro Rain Radar Metek MRR-2, an Ott Parsivel2 Disdrometer, and a TPS-3100 Hotplate Pluviometer. Model precipitation was extracted from the operational NWP model MetCoOp that uses a horizontal grid spacing of 2.5 km and 65 vertical levels as part of the HARMONIE AROME model configuration. Using DDH (Diagnostics par Domaines Horizontaux), a novel tool for extracting prognostic variables from the model at a time-step resolution, we extracted a detailed dataset from a NWP model reforecast at every time step (75s), for a 62.5 by 62.5 km subdomain centred around the measurement site. We characterised precipitation by investigating five parameters, namely rain rate, liquid water content, mean volume diameter, the normalised intercept parameter, and terminal fall velocity. The newly developed methodology enabled a direct comparison of the observed precipitation characteristics with corresponding parameters from the model prediction for the convective rainfall event. Despite a generally reasonable correspondence between all parameters in the model and observations, the model struggled with underestimation of rainfall intensity during the high-intensity periods. The onset and intensity of precipitation depended strongly on location for the investigated event. Higher time resolution provided more detailed insight into intensity, timing and spatial variability of the modelled precipitation compared to the more commonly used hourly interval. Our new methodology can be easily applied to other precipitation events, such as frontal rainfall events, and thus provide process-level understanding of precipitation characteristics simulated by high-resolution NWP models. 

How to cite: Steinslid, M., Sodemann, H., and Kähnert, M.: Enabling the comparison of high-resolution precipitation observations with numerical weather prediction model simulations at every model time-step, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14882, https://doi.org/10.5194/egusphere-egu23-14882, 2023.

Weather forecasting in the Middle Ages was most likely mostly based on persistence, and there are indications that persistence and correlation between elements of the sensible weather, in particular fog, helped in navigation in the N-Atlantic during the Viking age.

Investigation of the weather in the CARRA dataset, produced by dynamic downscaling, reveals that the connection between wind directions and fog is different on the leg between Iceland and Greenland from what it is between Iceland and Norway.  Consequently, the same navigational rules could not be applied on both these legs, making navigation from Iceland to Greenland even more difficult than navigation from Norway to Iceland.  This, in addition to very high frequency of fog and of strong winds in the vicinity of Greenland, made sailing and navigation between Iceland and the Medieval Nordic settlements in Greenland exceptionally difficult.    

How to cite: Ólafsson, H., Weitzel, P., Rousta, I., Soula, B., and Jacopin, L.: Methods of weather forecasting and navigation in the N-Atlantic in the Middle Ages tested with a modern NWP tool, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15208, https://doi.org/10.5194/egusphere-egu23-15208, 2023.

Numerical weather prediction (NWP) models are frequently used tools in operational weather forecasting. The NWP bases on current weather observations and processing of this data using computational models to forecast possible weather conditions. The aim of the study was to determine the optimal configuration of the Weather Research and Forecasting (WRF) model , version 4.2 (Skamarock et al. 2008), for more effective weather forecasting for the area of Poland. For model evaluation, we used observations from the IMWM-NRI network (above 50 meteorological stations). Numerical simulations were run using GFS model data was obtained from NOAA's NCEP servers. The WRF model was configured for a 3 km horizontal resolution grid, using unique parameterization settings for this model. Validation of forecast data was performed using statistical measures recommended by the WMO, e.g. mean error, mean absolute error, mean squared error, showing the values of forecast error. In this study, the model settings were configured based of other papers for Europe (Stergiou et al. 2017, Mooney et al. 2013, Kioutsioukis et al. 2016, Garcia-Diez et al. 2015, Carvalho et al. 2014, Santos Alamillos 2013), especially from its central part (Wałaszek et al. 2014, Kryza et al. 2017). The results of the work present statistical summaries of optimal model parameterization schemes, depending on their verifiability. Model configuration characterized by the best performance will be further examined over a longer time period (in the study, the average MAE for air temperature was 0.8°C). The research was funded by National Science Center (project number: 2017/27/N/ST10/00565)

How to cite: Kendzierski, S.: Influence of resolution and parameterization of the WRF model on the verifiability of weather forecast, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15595, https://doi.org/10.5194/egusphere-egu23-15595, 2023.

Stochastic parameterisations are an important way to represent uncertainty in the deterministic forecasting models underlying ensemble prediction systems. Current stochastic parameterisation approaches use random correlation patterns that are unrelated to the atmospheric flow to induce coherent perturbations to parameterisations. Here we replace these patterns by accumulated tendency fields from parameterized physical processes in the HARMONIE-AROME system. Our rationale is that by perturbing the parameterisations with a field that reflects where parameterisations are most active, rather than random, the model obtains a more targeted increase in the degrees-of-freedom to represent forecasting uncertainty.

Here we study a large marine cold-air outbreak over the Norwegian Sea. Strong heat fluxes persisted near the ice edge, and shallow convection dominated in the center of the model domain. Perturbation fields are diagnosed from individual tendency diagnostics implemented in AROME-Arctic within ALERTNESS. Total physical tendencies for the horizontal winds, for temperature and humidity are accumulated with a time filtering throughout the 66 h forecast period.

Accumulated tendencies show overlapping and differing centers of activity. Wind parameterisations are active near the ice edge, and with smaller scale variability over land areas. Temperature tendency patterns show activity more confined to the ice edge, and the coast of northern Scandinavia. Such spatially coherent patterns of parameterisation activity are meaningfully related to current weather. To exploit the relation between parameterisation activity and weather patterns for ensemble perturbation, we conduct sensitivity tests of cloud parameterisation parameters in a single-column model version MUSC and the full model version. First results illustrate our progress towards the use of diagnostic perturbation patterns for stochastically perturbed perturbations in the HarmonEPS system.

How to cite: Sodemann, H., Kähnert, M., Remes, T. M., Ekrem, P., Grote, R., and Frogner, I.-L.: Potential of accumulated parameterisation tendencies from AROME-Arctic for stochastic parameterisation erturbation patterns, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15927, https://doi.org/10.5194/egusphere-egu23-15927, 2023.

Accurate and precise weather forecasting is essential for a wide range of applications and industries, from agriculture to transportation to renewable energy. However, current weather models often struggle to represent the weather accurately due to limitations in spatial resolution. Global models with broad resolution are unable to represent small-scale weather features, such as convective thunderstorms or local wind patterns, while regional high resolution models are highly dependent on boundary conditions and typically provide forecasts for a small domain. To fill this gap, Meteomatics has developed the EURO1k model, the first pan-European weather model with a 1km² resolution.

The EURO1k model consists of approximately 20 million grid points and is run 24 times per day, with a forecast horizon of 24 hours. It is based on the WRF (Weather Research and Forecasting) model and uses global ECMWF-IFS model data for boundary conditions. In addition to standard data sources such as weather stations, radar and satellite data, and radiosondes, the EURO1k model also ingests data from a network of Meteodrones, small unmanned aircraft systems (UAS) developed by Meteomatics which collect vertical atmospheric profiles up to 6000m in altitude. The high resolution of the EURO1k model allows it to accurately represent small-scale weather patterns, resulting in highly accurate and precise forecasts. This is evident in verifications against weather station observations, which show a very good agreement between model output and a range of weather variables including wind, temperature, and radiation.

Statistical analyses of EURO1k model output against observations from 5000 weather stations in Europe demonstrate better accuracy compared to other global and regional models. This has important implications for industry and the public. The EURO1k model can improve the forecasting of extreme weather events, allowing for better preparation and response. It can also enhance the prediction of renewable energy production, which depends on weather conditions. This increases the cost efficiency of renewable energies and help to reduce CO2 emissions. And, most importantly, it provides a more accurate and reliable weather forecast for communities across Europe. Overall, the EURO1k model represents a major advance in numerical weather prediction, bringing improved understanding and forecasting of the weather to a wide range of users.

How to cite: Pasquier, J. T., Rausch, J., Stauch, A., and Fengler, M.: EURO1k: A high-resolution European weather model developed by Meteomatics, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16670, https://doi.org/10.5194/egusphere-egu23-16670, 2023.

Atmospheric conditions and instabilities affect directly the performance of modern large offshore wind farms and several offshore operations, particularly farther offshore in deep waters. However, our current knowledge regarding to the atmospheric processes over a wide range of spatiotemporal scales needs further improvements by the use of measurements, and sophisticated modelling of Marine Atmospheric Boundary Layer (MABL) processes relevant to the offshore wind energy. Processes like gravity waves, Open Convective Cells (OCCs), Low Level Jets (LLJs) affect both horizontal and vertical structures of MABL flow fields and the interactions between the ambient flow and offshore constructions. For example, LLJs are common physical processes over the Southern North Sea. These transient events occur during stably stratified atmosphere with jet cores at heights between 150 m and 300 m. Strong positive and negative shears are observed below and above the nose of LLJ (i.e a maxima in the vertical wind profile). Structure, timing, shape, and characteristics of LLJs influence the loads on turbines and the overall power generation of offshore wind parks. Therefore, precise modelling and measurement of these episodes are highly important.

While advanced measurement systems such as LiDAR provides important information on formation and characteristics of LLJs, such measurements are sparse in time and space. On the other hand, modelling tools are sensitive in prediction of LLJ characteristics such as LLJ’s height, spatial position, and timing, the choice of initial and boundary conditions, and planetary boundary layer schemes used in the Numerical Weather Prediction models (NWPs).  Predictive skills of these models can be enhanced through assimilation of available quality observational data with NWPs like Weather Research and Forecasting (WRF) model.

In this study, we use the WRF model to model wind variability for a geographical area covering the FINO1 offshore meteorological met-mast and Alpha Ventus offshore wind park (in the Southern North Sea). We first examine the performance of WRF, with an appropriate configuration, in forecasting few LLJ events. We then apply a LiDAR-based data assimilation (for sometimes during 2015) and study how different DA techniques (namely observational nudging and 3DVAR) can improve the accuracy of wind forecasting and reduce the model uncertainity during the LLJ events.

How to cite: Bakhoday-Paskyabi, M., Bui, H., and Mohammadpour Penchah, M.: LiDAR-based data assimilation during offshore transient events, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16972, https://doi.org/10.5194/egusphere-egu23-16972, 2023.

A new double-moment parameterization with in-cloud microphysical processes is developed for use in weather forecasting and climate studies. A main ingredient of the scheme utilizes a concept to represent the partial cloudiness effect on the microphysical processes, following the study of Kim and Hong (2018). The underlying assumption is that all the microphysical processes occur in a cloudy part of the grid box. Based on the long-term evaluation of the WRF Single-Moment (WSM) and WRF Double-Moment (WDM) schemes by WRF community, several revisions are made in microphysics terms, along with a newly introduced aerosol effect in ice processes. An aerosol-aware feature with prognostic aerosol emissions of sea salt, dust, anthropogenic and wildfire organic carbon for CCN is also designed. A mass-conserving Semi-Lagrangian sedimentation is re-configured for double-moment physics, which is superior to the conventional Eulerian algorithm in the context of the computational accuracy and numerical accuracy. The new scheme reproduces the storm structure in an idealized 2D testbed, accompanying better organized front-to-rear jets, cold pools, and convective updrafts, as compared to the results in the case of conventional microphysics. The wall-clock time is about a half in the US NOAA/GFS model, as compared to that of Thompson scheme.

How to cite: Hong, S., Li, H., Bao, J.-W., and Dudhia, J.: Development of a New Microphysics Scheme with In-Cloud Processes for Weather Forecasting, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16973, https://doi.org/10.5194/egusphere-egu23-16973, 2023.

Points of interest are identified along the track. Most commonly these points are the locations of weather stations, as they are generally placed where weather conditions are of interest and the WOD system has additional machine learning interpolation mechanisms in development for weather stations. From this set, along with on-the-hour locations, a representative, refined, lower resolution track is assembled, for which high-resolution forecast data is pulled.

From that forecast data, the information most relevant to the user is highlighted. Any difficult conditions, as well as a segmented summary is generated in simple, succinct text, programmable in any language.

An ongoing extension of this feature is to develop a module that can create a simple text forecast for any user defined region.

The WOD software is maintained in Git and can be installed on suitable hardware in a matter of hours, bringing the full flexibility and power of the WRF modelling system at your fingertips.

How to cite: Guðmundsson, E. H., Rögnvaldsson, Ó., and Stanislawska, K.: Automatic generation of a text forecast along a track, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17383, https://doi.org/10.5194/egusphere-egu23-17383, 2023.

Accurate forecasting of solar irradiance is crucial for the integration of solar energy into the
power grid, power system planning, and the operation of solar power plants. The Weather
Research and Forecasting (WRF) model, with its solar radiation (WRF-Solar) extension, has
been used to forecast solar irradiance in various regions worldwide. However, the application
of the WRF-Solar model for global horizontal irradiance (GHI) forecasting in West Africa,
specifically in Ghana, has not been studied. This study aims to evaluate the performance of
the WRF-Solar model for GHI forecasting in Ghana, focusing on 3 health centers (Kologo,
Kumasi and Akwatia) for the year 2021. We applied a two one-way nested domain (D1=15
km and D2=3 km) to investigate the ability of the WRF solar model to forecast GHI up to 72
hours in advance under different atmospheric conditions. The initial and lateral boundary
conditions were taken from the ECMWF operational forecasts. In addition, the optical aerosol
depth (AOD) data at 550 nm from the Copernicus Atmosphere Monitoring Service (CAMS)
were considered. The study uses statistical metrics such as mean bias error (MBE), root mean
square error (RMSE), to evaluate the performance of the WRF-Solar model with the
observational data obtained from automatic weather stations in the three health centers in
Ghana. The results of this study will contribute to the understanding of the capabilities and
limitations of the WRF-Solar model for forecasting GHI in West Africa, particularly in
Ghana, and provide valuable information for stakeholders involved in solar energy generation
and grid integration towards optimized management of in the region.
Keywords: WRF-Solar; Global horizontal irradiance; Forecasting; West Africa; Ghana

How to cite: Sawadogo, W., Fersch, B., Bliefernicht, J., Meilinger, S., and Kunstmann, H.: Evaluating the Performance of WRF-Solar Model for 72-Hour Ahead Global Horizontal Irradiance Forecasting in West Africa: A Case Study of Ghana, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17484, https://doi.org/10.5194/egusphere-egu23-17484, 2023.

An experiment reported in Mesinger and Veljovic (JMSJ 2020) showed an
advantage of the Eta over its driver ECMWF ensemble members in placing 250 hPa jet
stream winds during a period of an upper tropospheric trough crossing the Rockies. 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. Nevertheless, 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 vertical
advection of the Eta, implemented in 2007, is a significant contributor to this advantage.
In this 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.
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,” a
very comprehensive 2006 NCEP parallel test gave just the opposite result. With
seemingly equal ABL schemes, the Eta showed a higher surface layer accuracy over
high topography than the NMM, using a hybrid terrain-following system (Mesinger, BLM
2022).
Hundreds of thousands of the Eta forecasts and experiments performed
demonstrate that the relaxation lateral boundary conditions almost universally used in
regional climate modeling (RCM)–in addition to conflicting with the properties of the
basic equations used–are unnecessary. Similarly, frequently applied in RCMs so-called
large scale or spectral nudging, being 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 results we refer to above.

How to cite: Mesinger, F., Veljovic, K., Chou, S. C., Gomes, J. L., Lyra, A. A., and Jovic, D.: Cut-cell Eta ensemble skill vs. ECMWF: Lessons learned, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17562, https://doi.org/10.5194/egusphere-egu23-17562, 2023.

The HAIL application was developed and implemented in the Institute of Meteorology and Water Management – National Research Institute (IMGW) as a component of the MeteoWarn system of detection and forecasting of dangerous weather phenomena. The application contains two algorithms: (i) hail detection and probability estimation; (ii) estimation of the maximum hail size that occurs in the event.

The probability of hail is determined using own hail detection algorithm based on fuzzy logic using the following weather radar products: the differential reflectivity (ZDR) and the exceedance of 0°C isotherm for echo top 40, 45, 50 dBZ (EHT40, EHT45, EHT50). Threshold have been introduced for the parameters to prevent false hail detection, above which hail is possible to occur. Additionally some other radar parameters: maximum reflectivity (CMAX), vertically integrated liquid water (VIL), constant altitude plan position indicator (CAPPI) on 4 km, and EHT are checked. The maximum hail size is calculated from the parameters: VIL, EHT50, and isotherm 0°C.

The developed algorithms were verified by observations in meteorological stations staffed by trained observers. The stations are limited to specific locations, but they are the most reliable and precise source of data about weather phenomena. Verification data for calibration are observations from synoptic stations and for hail size additionally observations from the European Severe Weather Database (ESWD). The results of the verification show good enough reliabilities of the two HAIL products. Validation based on the contingency table provided the following results: the probability of detection (POD) is 0.99, the false alarm ratio (FAR) is 0.02, and the critical success index (CSI) is 0.98. POD of no hail is 0.39, FAR is 0.38, and CSI is 0.31.

How to cite: Specht, K., Szturc, J., and Jurczyk, A.: High-resolution hail detection: probability of occurrence and size of hailstones based on weather radar data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1129, https://doi.org/10.5194/egusphere-egu23-1129, 2023.

Ocean waves, especially extreme waves, are vital for air-sea interaction and shipping. However, current wave models still have significant biases, especially under extreme wind conditions. Based on a numerical wave model and a deep learning model, we accurately predict the significant wave height (SWH) of the Northwest Pacific Ocean. For each day in 2017-2021, we conducted a 3-day hindcast experiment using WAVEWATCH3 (WW3) to obtain the SWH forecasts at lead times of 24, 48, and 72hr, forced by GFS real-time forecast surface winds. The deep learning-based bias correction method is BU-Net by adding batch normalization layers to a U-Net, which could improve the accuracy. Due to the use of BU-Net, the mean Root Mean Squared Errors (RMSEs) of the SWH forecast from WW3 at lead times of 24, 48, and 72hr are reduced from 0.35m to 0.21m, 0.39m to 0.24m, and 0.43m to 0.30m, corresponding to drop percentages of 40%, 38%, and 30%, respectively. During typhoon passages, the drop percentages of RMSEs reach 45%, 42%, and 35% for three lead times. Therefore, combining numerical models and deep learning algorithms is very promising in ocean wave forecasting.

How to cite: Sun, D., Huang, W., Luo, Y., Luo, J., Wright, J. S., Fu, H., and Wang, B.: A Deep Learning-Based Bias Correction Method for Predicting Ocean Surface Waves in the Northwest Pacific Ocean, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1345, https://doi.org/10.5194/egusphere-egu23-1345, 2023.

Ensemble forecasts of precipitation with sub-seasonal lead times offer  useful information for decision makers when they sufficiently sample the possible outcomes of trajectories. In this study, we aim to improve  precipitation ensemble forecast systems using a stochastic weather generator (SWG) based on analogs of the atmospheric circulation. This approach is tested for sub-seasonal lead times (from 2 to 4 weeks). The SWG ensemble forecasts  yield promising probabilistic skill scores for lead times of 5-10 days for precipitation (Krouma et al, 2022) and for lead times of 40 days for temperature   (Yiou and Déandréis, 2019) . In this work, we adapt the parameters of the SWG to optimize the simulation of European precipitations from ensemble dynamical reforecasts of ECMWF and CNRM. We present the HC-SWG forecasting tool (HC refers to Hindcast and SWG to the stochastic weather generator) based on a combination of dynamical and stochastic models.

We start by computing analogs of Z500 from the ensemble member reforecast of ECMWF (11 members) and CNRM (10 members). Then, we generate an ensemble of 100 members for precipitation over Europe. We evaluate the ensemble forecast of the HC-SWG using skill scores such as the continuous probabilistic score CRPS and ROC curve.

We obtain reasonable forecast skill scores for lead times up to 35 days for different locations in Europe (Madrid, Toulouse, Orly, De Bilt and Berlin). We compare the HC-SWG forecast with other precipitation forecasts to further confirm the benefit of our method. We found that the HC-SWG shows improvement against the ECMWF precipitation forecast until 25 days.

How to cite: Krouma, M., Batté, L., Magnusson, L., Specq, D., Ardilouze, C., and Yiou, P.: Improving the ensemble forecast of precipitation in Europe by combining a stochastic weather generator with dynamical models , EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1777, https://doi.org/10.5194/egusphere-egu23-1777, 2023.

Deep learning has been rapidly adopted in short-term precipitation prediction, such as simulating precipitation movement and predicting extreme weather events. Recently, generative adversarial neural networks (GANs) have been shown to be effective at dealing with field smoothing with increasing lead time. Several studies (Jing et al., 2019; Ravuri et al., 2021) demonstrated the potential of GAN by solving spatial smoothing problems and demonstrating reliable predictive performance. However, despite promising results from GANs, unbalanced datasets and human annotations can limit the predictive ability of deep learning and induce biased results. In addition, precipitation is a complex process that depends on various factors. Thus, approximating the model into a single latent space is a challenge, and furthermore, there is a risk of mode collapse. This study introduces an algorithm for predicting precipitation by clustering precipitation types using self-supervised learning (SSL) and estimating rainfall distribution according to precipitation types. First, we derive precipitation-type labels by self-clustering a generator that is a multi-layer ConvGRU. And then, we predict six-hour precipitation based on the gaussian distribution of each type. SSL improves the performance of precipitation forecasting based on type-specific representation learning through adaptive sampling in latent space. The proposed methodology was verified using hybrid surface rainfall (HSR) dataset at a spatial resolution of 500m with a resolution of 2,305 (longitude) × 2,881 (latitude) and a temporal resolution of 5 min. The images consist of 256×256 pixels from scaling down to a resolution of 4 km and are extracted at 30-minute intervals. Experimental results show that our method outperforms a state-of-the-art method on a six-hour prediction basis with a mean squared error and critical success index on unseen datasets. Also, the proposed algorithm can predict various precipitation types without spatial smoothing.

How to cite: An, S., Oh, T.-J., Na, I., Jang, J., Park, W., and Kim, J.: GAN-based forecasting model via self-adaptive clustering approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4735, https://doi.org/10.5194/egusphere-egu23-4735, 2023.

The UK Met Office is developing an open-source probability-based post-processing system called IMPROVER (Integrated Model Post-Processing and Verification) to fully exploit our convection permitting, hourly cycling ensemble forecasts.  Post-processed MOGREPS-UK model forecasts are blended with deterministic UKV model forecasts and data from the coarser resolution global ensemble, MOGREPS-G, to produce seamless probabilistic forecasts from now out to 7 days ahead. For precipitation, an extrapolation nowcast is also blended in at the start.

A majority of the post-processing within IMPROVER is performed on gridded forecasts, with site-specific forecasts extracted as a final step, helping to ensure consistency. IMPROVER delivers a wide range of probabilistic products to both operational meteorologists and as input to automated forecast production. The system achieved operational acceptance in spring 2022 and will be used in operational products from spring 2023.

Weather symbols provide the general public with a simple, pictorial view of the weather for a time of interest and include sun and cloud conditions, mist and fog, hail and lightning, and three phases of precipitation, both as showers or continuous, and light or heavy. This talk describes how a deterministic most-likely weather type code is generated using a decision tree approach from probabilistic multi model IMPROVER data for 1 hour, 3 hour and daytime periods that are consistent with each other. Recent work to make these weather codes representative of a time-window, rather than an instant, will be discussed. We will present some verification, comparing IMPROVER weather symbols and the current operational Met Office symbols with SYNOP present weather reports.

How to cite: Moseley, S., Ayliffe, B., and Evans, G.: Generating weather symbol data in IMPROVER, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5837, https://doi.org/10.5194/egusphere-egu23-5837, 2023.

In the framework of the On Demand Services For Smart Agriculture (OD4SA) project, funded by PO FESR 2014-2020 from Regione Basilicata, Italy, a weather forecast service has been developed, for applications in smart agriculture and precision farming. It is based on the Weather Research and Forecasting (WRF) model and provides a daily 96-hour forecast of temperature and water vapor at 2 m altitude, wind speed and direction at 10 m altitude, atmospheric pressure, solar irradiance, and 1-hour accumulated rainfall, for the Southern Italy. Although encouraging advances in microscale modeling have been achieved in the last decade, the computational costs imposed by the state of the art do not allow for continuous operational forecasting at the sub-kilometer scale, useful for precision farming, especially in southern Italy that is characterized by a complex orography. To overcome this limit, an algorithm based on some Artificial Neural Networks (ANNs) has been developed, by using the WRF Large Eddy Simulation (LES) to build the training database at 240 m spatial resolution. Particular attention was paid to the analysis of the true spatial resolution of the WRF-LES outputs, to the definition of the ANNs topology and to the input selection, from over 250 inputs more than half has been discarded. The preliminary results show RMSE equal on average to 70% of those obtained by using the most common spatial interpolation methods.

How to cite: Di Paola, F., Gentile, S., Genzano, N., Ricciardelli, E., Romano, F., and Tramutoli, V.: Weather forecast downscaling for applications in smart agriculture, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7214, https://doi.org/10.5194/egusphere-egu23-7214, 2023.

Brazil has a country-wide interconnected grid of over 169,000 km of high voltage transmission lines. By 2026, an additional 20,000 km will expand the grid significantly. The main type of electrical energy transmission in Brazil is aerial for all sources of generation, including hydroelectric, wind and solar power plants, resulting in a network between the tropics to the subtropical regions up to -33 degrees latitude. In Southern Brazil there are 12.994.382 consumer units in the States, Rio Grande do Sul, Santa Catarina and Paraná.  One of the main causes of structural failures is associated with severe storms that produce loads that exceed the structural loading design criteria. In this work it has been investigating hindcast predictions with GFS and WRF for a high speed wind gust event that blew down towers in Southern Brazil during severe weather conditions between 2016 and 2022. It has analyzed eight high-impact events where towers or lines have failed or been shut down looking for convection parameters that indicate severe weather specifically for these impacts. In order to simulate a 48h forecast it was used the current operational GFS/GFDL V3 global model from NCEP/NOAA and 3-km resolution WRF runs. In seven of eight events the models were capable of simulating an environment conditions which meteorologists could elaborate an alert of high-impact severer weather for transmission lines and  could help the electric company's teams to execute a contingency plan.  Both GFS and WRF have indicated severe environments, but WRF has indicated better detailed areas of deep convection. In a sense of search thresholds that could be used in the future, some values of shear were found: 0-6km Shear 70-84 kt, 0-1 km Shear up to 40 kt, 0-3 km Shear up to 61 kt. The authors have not found specific thresholds for other variables such as the Convective available potential energy (CAPE) convective inhibition. The impact of the forecasts was analyzed according to the possible activities to be carried out by technicians in the prevention and repair of electrical systems and reduce the impact in outages.

How to cite: Calvetti, L., Cassol Machado, L. G., Beneti, C., Andrzejewski, K., Pereira Harter, F., Felix Alonso, M., and Radman Paz, S.: Discussion about bulk shear thresholds for severe weather environment that cause power outages and blow down towers of transmission and distribution lines in Southern Brazil, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8628, https://doi.org/10.5194/egusphere-egu23-8628, 2023.

Deep generative modeling is able to generate highly realistic atmospheric fields, one prominent example being precipitation. So far, almost all studies have used generative adversarial networks (GANs) for this purpose, but recent progress in machine learning research has had a new class of methods called diffusion models replace GANs in many applications. Diffusion models have been often shown to be able to generate a wider variety of samples than GANs, suggesting that they might be able to better capture uncertainty in applications such as weather and climate where quantifying it is important.

In this presentation, we describe our research on using diffusion models for short-term prediction (nowcasting) of precipitation fields. We adapt the latent diffusion model used by Stable Diffusion (Rombach et al. 2022) to the this problem, predicting precipitation up to 3 hours ahead to the future at 5-min temporal resolution and 1-km horizontal resolution. Predictions can be produced as an ensemble where each member represent a possible future evolution of the precipitation field.

We show that our model:

• generates highly realistic precipitation fields that are consistent with the past precipitation used as input.
• outperforms the state-of-the-art GAN-based Deep Generative Models of Rainfall (DGMR) model by most relevant metrics.
• performs particularly well at representing the uncertainty of its own predictions, as shown by uncertainty quantification methods developed for ensemble forecast verification.

Therefore, it appears that diffusion models are indeed suitable for generative modeling of precipitation fields with highly realistic representation of uncertainty. Our model architecture also permits multiple inputs data sources to be combined, in particular allowing seamless generative predictions to be made by exploiting observations and numerical weather predictions.

How to cite: Leinonen, J., Hamann, U., and Germann, U.: Latent diffusion models for generative nowcasting and uncertainty quantification of precipitation fields, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9531, https://doi.org/10.5194/egusphere-egu23-9531, 2023.

Recently, weather radar has been increasingly used to estimate precipitation for a variety of hydrological and meteorological applications, including real-time flood forecasting, severe weather monitoring and warning, and short-term precipitation forecasting. In very short range (0–6 h), many critical decisions are taken to ensure people’s safety. For example, the damage of a localized hazard of flood is high so that the warning of these severe weather is important. Forecasting precipitation in this time range the commonly relies on extrapolation-based nowcasting tools that exploit the persistence of the most recent weather radar observations. To obtain the best possible prediction skill in the 0–6-h range, one cannot solely rely on numerical weather prediction (NWP) but must also use the available observations in a more direct way. Weather radars are instruments capable to provide rainfall measurements with suitable spatial and temporal resolutions. The potential benefit of using radar rainfall in hydrology is huge, but practical hydrological applications of radar have been limited by the inherent uncertainties and errors in radar rainfall estimates. As radar nowcasts are essentially based on extrapolation from a series of consecutive radar scans, they are characterized by a high skill at the start of the forecast, but this decreases with lead time very rapidly, as extrapolation techniques generally do not account for growth and decay processes in the atmosphere (Golding 1998).

Machine learning algorithms can be trained with weather radar data to identify regions of precipitation growth and decay based on historical observations. Artificial neural networks (ANN) can be employed to learn the complex nonlinear dependence relating the growth and decay to the predictors, which are geographical location, motion vectors, temperature, precipitation and time (Foresti et al.2019). The precipitation motion field can be calculated by using the optical flow driven by weather radar data. Around 15-year of weather radar precipitation observations from Great Britain (GB) are used to derive precipitation growth and decay mainly due to orography. This paper will present the preliminary findings of predicting precipitation growth and decay in different regions in the UK.

How to cite: Li, C., Rico-Ramirez, M., Wang, Q., Liu, W., and Han, D.: Predicting precipitation growth and decay with weather radar rainfall measurements, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9985, https://doi.org/10.5194/egusphere-egu23-9985, 2023.

Although tropical cyclone (TC) forecasts can fairly well capture the TC track and primary rainfall distribution, limited skills are found in forecasting TC structural changes and asymmetric gusty winds. The barrier to further understanding TC structural change is due mainly to the lack of observation, and it is difficult to have systematic 2-D wind analyses. Here, we developed a deep learning model — Deep Learning 2-D Structure Analysis Model for Tropical Cyclones (DSAT-2D) — to produce TC wind analysis in high-temporal-spatial resolutions based on generative adversarial networks (GAN). We use IR1 satellite observation and ERA5 reanalysis data as the model input for the DSAT-2D. The ASCAT surface wind data were collected and used as the label data. Note, however, that the ASACT analysis tends to underestimate winds greater than 15 m/s. Thus, we proposed several methods to fix this issue before training the model. Furthermore, other innovative designs in the DSAT-2D model include: (i) we regrid all data in a polar coordinate to better handle the TC tangential and radial features, and (ii) we also set the target of the DSAT-2D model as the TC radial wind and tangential wind.

Experiment results demonstrate that the DSAT-2D model can capture the TC asymmetric wind structure while possessing the capability of increasing the maximum estimation frequency from approximately 12 hours (e.g., ASCAT data) to less than one hour. The DSAT-2D model may help understand the TC asymmetric wind evolution and improve TC forecasts. Future applications of assimilating this value-added information into the numerical weather prediction model will also be discussed.

How to cite: Cheng, Y.-Y. and Chen, B.-F.: An End-to-end Deep Learning Approach for Analyzing Tropical Cyclone 2-D Surface Winds Utilizing Satellite Data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10632, https://doi.org/10.5194/egusphere-egu23-10632, 2023.

Taiwan is a 35,808-km² island with more than 100 peaks over 3,000 meters. The complex terrain in Taiwan makes forecasters more challenging to forecast rainfall in mesoscale and storm-scale. Besides, the spatial distribution of rainfall stations is quite uneven as well. Moreover, the forecast performance of both the European Centre for Medium-Range Weather Forecasts (ECMWF) and the Global Forecast System (GFS) is limited by Taiwan's complex terrain, having certain systematic deviations in rainfall forecasts. For example, the ECMWF forecast has underestimated heavy rainfall and over-predicted light rain in Taiwan. Consequently, to correct model deviations and provide better rainfall forecast products, advanced statistical or artificial intelligence (AI) methods should be studied.

This research applies the U-net neural network to generate downscaling rainfall prediction. We collected precipitation forecast data from the ECMWF (9 km resolution) and the GFS (22 km resolution) during 2021 as the model input. The Quantitative Precipitation Estimation and Segregation Using Multiple Sensor (QPESUMS) radar data from CWB is used as the label data. QPESUMS data can effectively help describe the complete spatial distribution of rainfall. The testing data is from the 2022 whole year. An innovative design of the proposed model is a geographical attention layer (GAL) in the U-net. The GAL helps to learn the geospatial characteristics from the QPESUMS rainfall observation. Moreover, this study uses a scale-separated loss function for model optimization, for which the rainfall is divided into large-scale smoothing and small-scale disturbance fields.

Results show that this U-net downscaling model successfully learns the feature and corrects the systematic bias in both global models, such as shifts in the rainfall caused by topographical lift and local circulation. Furthermore, based on the overall statistics of 2021, the performance diagram shows that the AI model corrects the over-prediction of light rain, while the critical success index in heavy rain is improved by 25 to 30%. The ongoing work of this research will apply generative adversarial networks to break the limitation of learning wrong features from the original forecast input data.

How to cite: Chang, R.-C., Cheng, Y.-Y., and Chen, B.-F.: An Advanced Deep Learning Rainfall Forecasts Downscaling Method in Taiwan, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10645, https://doi.org/10.5194/egusphere-egu23-10645, 2023.

Daily to monthly variations of precipitation directly affect the propagation of an emerging drought. To cope with adverse impacts, a skillful sub-seasonal forecast of precipitation is essential to track the evolution of the emerging drought and provide actionable information for stakeholder and water resources managers. This study evaluates the predictive performances of the Subseasonal Experiment (SubX) models (ECCC-GEPS6, EMC-GEFSv12, ESRL-FIMr1p1, GMAO-GEOS_V2p1, and RSMAS-CCSM4) for the precipitation variations during two recent long-term drought events (2007−2010 and 2013−2016) over the Korean Peninsula. Sub-seasonal prediction skill of SubX models are quantitatively evaluated via multiple verification metrics for ensemble, deterministic, and categorical forecasts. Results show that during the emergence of multi-year droughts, the intensification and persistence of drought severity are generally better predicted by SubX models than the weakening and recovery of the drought severity in all forecast times (1−4 weeks). The multi-model ensemble approach shows the best prediction skill, and EMC-GEFSv12 which has the most ensemble member presents the better predictive performance than other models. In addition, results from the sensitivity test to ensemble member size show that multiple ensemble member can enhance the prediction skills significantly up to eight ensemble members. Overall results suggest that the forecast of SubX on multi-year Korean Peninsula droughts can provide actionable information that helps manage water resources in a timely manner.

How to cite: Park, C.-K. and Kam, J.: Evaluation of the sub-seasonal forecasting skill of SubX models for precipitation during recent multi-year droughts over the Korean Peninsula, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10752, https://doi.org/10.5194/egusphere-egu23-10752, 2023.

This study investigated the prediction skill of the Asian dust seasonal forecasting model (GloSea5-ADAM) on the Asian dust and meteorological variables related to the dust generation using hindcasts of GloSea5-ADAM for the period of 1991~2016 for East Asia. GloSea5-ADAM incorporates the dust generation algorithm of the Asian Dust and Aerosol Model (ADAM) into the Global Seasonal Forecasting System version 5 (GloSea5). The Asian dust and meteorological variables (10 m wind speed, 1.5 m relative humidity, and 1.5 m air temperature) depending on the combination of the initial dates in the sub-seasonal scale were compared to that from synoptic observation and ERA5 reanalysis data. The evaluation criteria used Mean Bias Error (MBE), Root Mean Square Error (RMSE), and Anomaly Correlation Coefficient (ACC). The Asian dust and meteorological variables in the source region (35~44°N, 90~115°E) showed high ACC in the prediction scale within one month. The best performances for all variables showed when the use of the initial dates closest to the prediction month based on MBE, RMSE, and ACC. ACC was as high as 0.4 in Spring when using the closest two initial dates. In particular, the GloSea5-ADAM shows the best performance of Asian dust generation with an ACC of 0.60 in the occurrence frequency of Asian dust in March when using the closest initial dates for initial conditions. This result showed that the performances could be improved by adjusting the number of ensembles considering the combination of the initial date.

How to cite: Kang, M., Lee, W., Chang, P.-H., Kim, M.-G., and Boo, K.-O.: Prediction skill of Asian Dust Generation in hindcast data of Asian Dust Seasonal Forecasting Model (GloSea5-ADAM), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10909, https://doi.org/10.5194/egusphere-egu23-10909, 2023.

    Due to the threat of extreme rainfall associated with mesoscale convective systems and summer afternoon thunderstorms, very short-term quantitative precipitation forecasting during 0−3 h is critical in Taiwan. In this study, deep learning models are developed for high-resolution quantitative precipitation nowcasting in Taiwan up to 3 h ahead. The baseline model based on the convolutional recurrent neural network is trained with a dataset containing radar reflectivity and rain rates at a granularity of 10 min. As previous works tend to produce overprediction in low-rainfall regions, the currently proposed model is improved and further driven by highly related heterogeneous weather data, including visible channel satellite observation, environmental winds, and environmental thermo-dynamical profiles. Note that an innovative “PONI module” is added to the deep learning model to integrate a variety of heterogeneous data with various spatial and temporal characteristics. Moreover, model performance is evaluated from statistical and spatial rescaling perspectives represented by R =  R_avg + R'_, where R denotes original rainfall, R_avg and R' are spatial moving averages and the values deviated from R_avg, respectively. Statistical verification shows that the R_avg of the new model outperforms the previous model, while the performance of R' is comparable. The new model integrated with heterogeneous data selected upon domain knowledge can restrain the nowcasts that overestimate in low-rainfall regions. Last but not least, quasi-operational verifications against other state-of-the-art techniques in Taiwan Central Weather Bureau are presented as follows: (1) the CSI of the first-hour prediction from the deep learning model is comparable with QPESUMS-QPF and better than RWRF and iTeen. (2) 3h ahead prediction CSI of RWRF and iTeen are inferior to the performance of deep learning model owing to their misprediction of rainfall regions. The deep learning model can accurately predict medium and extreme amounts of precipitation at a fraction of the computational cost.

How to cite: Chen, D.-Y., Chang, C.-T., and Chen, B.-F.: Precipitation Nowcasting Based on an Optimized Deep Learning Model Trained with Heterogeneous Weather Data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11043, https://doi.org/10.5194/egusphere-egu23-11043, 2023.

So far, plenty of efforts have been pursued on the numerical weather prediction (NWP). However, systematic errors could never be ignored in the output applications. To supply the numerical forecasts with higher accuracies, statistical postprocessing is often expected to correct systemic biases and has been one of the key components of the forecasting suites. Based on the NWP models and taking advantages of the raw stepwise pattern projection method (SPPM), the neighborhood pattern projection method (NPPM) is newly proposed to postprocess the model outputs and to improve forecast skills of daily maximum and minimum temperatures (Tmax and Tmin) over East Asia for short-term timescales, as well as the Kalman filter based pattern projection method (KFPPM) for longer-term forecasts. For the short-term lead times of 1–7 days, the SPPM is slightly inferior to the benchmark of decaying averaging method, while its insufficiency decreases with increasing lead times. The NPPM shows manifest superiority for all lead times, with the mean absolute errors of Tmax and Tmin decreased by ~0.7° and ~0.9°C, respectively. Advantages of the SPPM and NPPM are both mainly concentrated on the high-altitude areas such as the Tibetan Plateau, where the raw model outputs show the most conspicuous biases. As for longer-term forecasts at the subseasonal timescale, the NPPM effectively calibrates the temperature forecasts at the early stage. However, with the growing lead times, it shows speedily decreasing skills and can no longer produce positive adjustments over the areas outside the plateaus. By contrast, the KFPPM consistently outperforms the other calibrations and reduces the forecast errors by almost 1.0°C and 0.5°C for Tmax and Tmin, respectively, both retaining superiorities to the random climatology benchmark till the lead time of 24 days. The optimization of KFPPM maintains throughout the whole range of the subseasonal timescale, showing most conspicuous improvements distributed over the Tibetan Plateau and its surroundings. Case experiments further demonstrate the above-mentioned features and imply the potential capability of the NPPM and KFPPM in improving forecast skills and disaster preventions for extreme temperature events. Besides, compared with the initial SPPM, they not only produces more powerful forecast calibrations, but also provides more pragmatic calculations and greater potential economic benefits in practical applications.

How to cite: Zhu, S., Lyu, Y., and Zhi, X.: Calibrations of Surface Air Temperature Forecasts at Short- and Long-term Timescales Based on Statistical Pattern Projection Methods, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11970, https://doi.org/10.5194/egusphere-egu23-11970, 2023.

Severe convective weather events, such as hail, lightning and heavy rainfall pose a great threat to humans and cause a considerable amount of economic damage. Nowcasting convective storms can provide precise and timely warnings and, thus, mitigate the impact of these storms. Dual-polarization weather radars are a crucial source of information for nowcasting severe convective events. These radars provide important information about the microphysics of the convective systems, on top of the rainfall rate and vertical structure of the reflectivity. Nevertheless, polarimetric variables, which can provide additional information about the size, shape and orientation of particles, are often not considered in nowcasting.

This work presents the importance of polarimetric variables as an additional data source for nowcasting thunderstorm hazards using machine learning, compared to using radar reflectivity alone. We add these data to the neural network architecture of Leinonen et al. 2022 (Seamless lightning nowcasting with recurrent-convolutional deep learning), which uses convolutional and recurrent layers and analyzes inputs from multiple data sources simultaneously. This network has a common framework, which enables nowcasting of hail, lightning and heavy rainfall for lead times up to 60 min with a 5 min resolution. The study area is covered by the Swiss operational radar network, which consists of five operational polarimetric C-band radars. In addition, we analyze the contribution of quality indices as an additional information source, which takes the uncertainty of the radar observations throughout the complex mountainous terrain and scanning strategy in Switzerland into account. Results indicate that including polarimetric variables and quality indices improves the accuracy of nowcasting convective storms.

How to cite: Rombeek, N., Leinonen, J., and Hamann, U.: Exploiting radar polarimetry for nowcasting of convective hazards using deep learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12127, https://doi.org/10.5194/egusphere-egu23-12127, 2023.

Accurate precipitation forecasts at kilometre scales are still a key challenge for convective scale ensemble prediction systems. We assess the spatial forecast skill-spread relationship for summer convection in 2021 and address the impact of considering model uncertainties from two physics parametrisations -- microphysics and planetary boundary layer turbulence -- together with initial and lateral boundary conditions uncertainties. To investigate their flow dependence all analyses are done conditionally to strong and weak synoptic convective forcing cases.
It is found that the spatial skill-spread relationship is highly dependent on synoptic forcing and the current operational ensemble forecasts are spatially underdispersive especially during weak synoptic control, whereas a good agreement is found during strong synoptic control. Case studies during weak synoptic control demonstrate that perturbations in the planetary boundary layer contribute to improving forecast skill and increase spread at small scales while microphysical perturbations contribute to spread increase across all scales. Overall, the combination of both perturbations seems to combine their individual impacts and thus benefits the spatial skill-spread relationship at most times and scales.

How to cite: Matsunobu, T., Keil, C., Puh, M., Gebhardt, C., and Marsigli, C.: The combined impact of model uncertainty on flow-dependent spatial predictability of convective precipitation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12517, https://doi.org/10.5194/egusphere-egu23-12517, 2023.

Atmospheric motion vector (AMV) is an important factor that affects most meteorological phenomena in numerical weather prediction. Despite of its significance, the conventional algorithm of moisture tracking for AMV calculated with most of remote sensing data uses the cross-correlation coefficient (CCC) method, resulting in low-resolution (target-based) output and much of errors. In addition, forecasting AMVs is impossible in conventional method because it requires water vapor data 10 minutes from the current time to calculate current winds. For better moisture flow tracking, convolutional neural network (CNN) frames were used that track motion, which is called optical flow estimation in computer vision. The pixel-based high-resolution AMVs are calculated by using the water vapor channel images into the PWC-Net (CNNs for optical flow using pyramid, warping, and cost volume). For each pixel, linear regression is used to forecast AMVs. The performance of the AMVs calculated by CNN was validated by comparing those results and the Korean geostationary satellite GEO-KOMPSAT-2A (GK2A) AMVs with wind fields of ERA5 data at 100-1000 hPa. Experiments used infrared brightness temperature images of three water vapor channels at 6.2 µm, 7.0 µm, and 7.3 µm over Korean Peninsula for 2022. As to root-mean-square vector differences (RMSVDs), the tracking performance of this study was found to be more accurate than the GK2A AMVs ­— 1.3 to 21.93 m/s more accurate for the cloudy sky and 0.32 to 14.9 m/s more accurate for the clear sky above 400 hPa. The results using the CNN model showed better moisture tracking performance than the conventional method, especially for low altitudes. It also enables to obtain higher resolution AMVs with pixel-based tracking rather than conventional target-based tracking. Furthermore, the mean RMSVDs of forecasted AMVs are 1.97 m/s, 2.66 m/s, 3.32 m/s, and 5.28 m/s when the forecast lead time is 10 min, 20 min, 30 min, and 1 hr, respectively. Consequently, high-resolution AMV forecasts with accuracy, which could not be calculated by the conventional method, were obtained by CNN model, and can be used to advance the accuracy of weather forecasting.

KEYWORDS: Moisture Tracking; Optical Flow; Atmospheric Motion Vectors; Wind Forecasting; Remote Sensing

How to cite: Choi, H., Choi, Y.-S., and Kim, G.: Development of Geostationary Satellite Atmospheric Motion Vectors Forecasting Algorithm by CNN Model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12665, https://doi.org/10.5194/egusphere-egu23-12665, 2023.

Solar energy generation is highly volatile during the day due to the strong dependence on cloud dynamics, which limits its integration into the power grid (Smith et al., 2022). On the other hand, higher utilization of renewable energy is essential to tackle climate change. To increase the share of photovoltaic energy in the grid without jeopardizing grid stability, accurate forecasts are essential to ascertain the balance between energy demand and supply (David et al., 2021).

Photovoltaic energy production mainly depends on downwelling surface solar radiation (). SSR is accurately measured by pyranometers, but their spatial representativeness is limited to a few kilometers. By estimating the SSR from geostationary satellites, we can cover larger areas with high spatial and temporal resolutions, allowing us to track cloud motion.

Previous studies on probabilistic cloud motion focused on optical-flow methods without considering the temporal evolution of clouds as such. We address this issue by presenting a scale-dependent approach to forecast. Our approach is inspired by the works of Bowler et al., 2006 and Pulkkinen et al., 2019 on precipitation nowcasting. The novelty of our study is the utilization of different autoregressive models to forecast the temporal evolution of cloudiness of different spatial scales. Our work is motivated by the scale-dependent predictability of cloud growth and decay. By exploiting more than one autoregressive model, we can predict the noisy evolution of small scales independently of the more deterministic evolution of larger spatial scales.

Our preliminary results over Switzerland indicate that our model outperforms the probabilistic advection model based on Carriere et al., 2021 noise generation by reducing the continuously ranked probability score (CRPS) on the test set by 14%. Moreover, we demonstrate the advantage of cloudiness scale decomposition by comparing our model with the same approach without decomposition. We can reduce the CRPS by 6% and the RMSE by 5% by decomposing the images into multiple cascades

References

Bowler, N., C. Pierce, A. Seed, 2006, “STEPS: A probabilistic precipitation forecasting scheme which merges an extrapolation nowcast with downscaled NWP”, Quarterly Journal of the Royal Meteorological Society, 132, 620, pp. 2127–2155, doi:10.1256/qj.04.100.

Carriere, T., R. Amaro e Silva, F. Zhuang, Y. Saint-Drenan, P. Blanc, 2021, “A New Approach for Satellite-Based Probabilistic Solar Forecasting with Cloud Motion Vectors”, Energies, 14, doi:10.3390/en14164951.

David, M., M. Luis, P. Lauret, 2018, “Comparison of intraday probabilistic forecasting of solar irradiance using only endogenous data”, International Journal of Forecasting, 34, doi:10.1016/j.ijforecast.2018.02.003.

Pulkkinen, S., D. Nerini, A. Perez Hortal, C. Velasco-Forero, A. Seed, U. Germann, L. Foresti, 2019, “Systems: an open-source Python library for probabilistic precipitation nowcasting (v1.0)”, Geoscientific Model Development, 12, 10, pp. 4185–4219, doi:10.5194/gmd-12-4185-2019.

Smith, O., O. Cattell, E. Farcot, R. D. O’Dea, K. I. Hopcraft, 2022, “The effect of renewable energy incorporation on power grid stability and resilience”, Science Advances,  https://www.science.org/doi/abs/10.1126/sciadv.abj6734.

How to cite: Carpentieri, A., Folini, D., Nerini, D., Pulkkinen, S., Wild, M., and Meyer, A.: Short-term probabilistic forecast of cloudiness: a scale-dependent advection approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12924, https://doi.org/10.5194/egusphere-egu23-12924, 2023.

Quantitative precipitation nowcasting (QPN) is crucial for forecasting precipitation within the next several hours (generally up to 6) to prevent substantial socioeconomic damage. In general, ground radar data has been widely employed in QPN due to its high spatial-temporal resolution and more precise precipitation estimation than satellite. With the remarkable success of deep learning (DL), recent QPN studies have actively adopted DL using radar data. Although these studies yielded high skill scores in forecasting precipitation areas with a weak intensity (about 1 mm/h), they failed to effectively simulate the horizontal movement of precipitation areas and showed poor ability in forecasting precipitation with stronger intensities. In addition, despite the fact that the skill score is highly dependent on the characteristics of each precipitation event, there was a lack of evaluation over various precipitation cases. From the motivation that there can be room for improving QPN using the advanced DL model in video prediction, this study suggests the QPN model based on simple yet better video prediction (SimVP), which is a state-of-the-art DL model. We trained the SimVP model using radar data in South Korea from June to September (JJAS) for the period of 2019-2022, which includes the summer and early fall. In terms of the critical score index (CSI) with a lead time of 120 minutes (0.46, 0.23, and 0.09 for 1, 5, and 10 mm/h thresholds, respectively), the proposed model showed significant improvement over the existing DL models based on an evaluation from JJAS 2022. Considering different precipitation conditions, three case studies were conducted for heavy rainfall, typhoons, and fast-moving narrow convection events. The suggested model showed comparable or the highest CSI in 120 min with a 1 mm/h threshold in all cases, demonstrating robust performance (0.49, 0.69, and 0.29 for heavy rainfall, typhoon, and narrow convection, respectively). Qualitative evaluation of the proposed model also showed better results in terms of horizontal displacement movement and less underestimation than the other models. In addition, we further explored the possibility of real-time learning (RTL) with newly added radar data. By repeatedly optimizing DL model for currently facing precipitation events, RTL contributed to deep learning models predicting results more similar to actual radar patterns. It is expected that the proposed SimVP and RTL would serve as a new baseline for DL-based QPN due to their ease of implementation and enhanced performance. 

How to cite: Han, D., Choo, M., and Im, J.: A data-driven precipitation nowcasting framework using advanced deep learning model for video prediction and real-time learning approaches, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13644, https://doi.org/10.5194/egusphere-egu23-13644, 2023.

The accurate forecasting of the intensity of tropical cyclones (TCs) is able to effectively reduce the overall costs of disaster management. In this study, we proposed a deep learning-based model for TC forecasting with the lead time of 24, 48, and72 hours following the event, based on the fusion of geostationary satellite images and numerical forecast model output. A total of 268 TCs which developed in the Northwest Pacific from 2011 to 2019 were used in this study. The Communications system, the Ocean and Meteorological Satellite (COMS) Meteorological Imager (MI) data were used to extract the images of TCs, and the Climate Forecast System version 2 (CFSv2) provided by the National Center of Environmental Prediction (NCEP) was employed to extract atmosphere and ocean forecasting data. In this study, we suggested hybrid convolutional neural network (hybrid-CNN)-based TC forecasting models. It enables to efficiently consider not only the physical but also the spatial characteristics of variables. The Joint Typhoon Warning Center (JTWC) was used for validating the suggested model, and Korea Meteorological Administrator (KMA)-based operational TC predictions were utilized for evaluating the performance of the model. A hybrid-CNN-based prediction model obtained mean absolute errors (MAE) of 13.58, 16.48, and 21.64 kts and skill scores (SS) of 29%, 19%, and 1.6% for 24h, 48h, and 72h forecasts, respectively. Since the rapid intensification (RI) is one of the challenging tasks in the TC intensity prediction, the performance of suggested model for all RIs in 2019 were additionally evaluated. Compared to KMA-based predictions, the suggested models achieved average SS of 66%. Furthermore, using an explainable artificial intelligence (XAI) approach, it is possible to verify how the suggested model works for forecasting TC intensity and propose the feasibility of the suggested model in the meteorology field.

How to cite: Lee, J. and Im, J.: Deep learning-based tropical cyclone intensity prediction through synergistic fusion of geostationary satellite and numerical prediction model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14443, https://doi.org/10.5194/egusphere-egu23-14443, 2023.

One major task of the National Meteorological and Hydrological Services (NMHS) is the provision of consistent and integrated forecasting products from minutes to several days ahead (seamless forecasting). The former EUMETNET (European Meteorological Services’ Network) project ASIST (Application oriented analysis and very short-range forecast environment) which started in 2015 focused on the nowcasting and very short range forecasting. Then, it was extended to the EUMETNET Nowcasting Programme (E-NWC) which started in 2019 and will last until the end of 2023 with focus on nowcasting and also on seamless prediction.

In this presentation, the main objectives of the E-NWC Programme will be introduced. E-NWC supports NMHS in sharing expertise, experiences and best practices for developing and implementing nowcasting, very short-range forecasting and seamless prediction systems. Key activities lie in the exchange of information and experiences with the users during e.g. the every two years European Nowcasting Conference and the strong cooperation with the World Meteorological Organization (WMO) and EUMETSAT, and in summarizing the relevant findings in project reports and joint peer-reviewed papers. Highlights of this contribution comprehend a few results from studies and surveys carried out recently.

How to cite: Schmid, F., Sivle, A., Agersten, S., Simon, A., and Atencia, A.: EUMETNET Nowcasting Programme, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14544, https://doi.org/10.5194/egusphere-egu23-14544, 2023.

On 26 November 2022, an extreme rainfall event occurred over Ischia Island (Italy). It triggered a mudflow that swept over Casamicciola Terme town and caused 12 victims. Based on available rainfall data from 4 rain-gauge stations over the island, the precipitation values registered during the event were higher than the annual maxima values of the previous 15 years. With regards to 1 and 24 hours, the rain-gauge stations measured values between 40.6 and 57.6 mm, and between 145.4 and 176.8 mm, respectively. Since one of the main challenges during these phenomena is predicting rainfall sufficiently in advance in order to allow water managers to take action (issue warnings or real-time control), this study investigates how much time before the peak - or threshold exceedance - a machine learning model is able to capture the peak - or threshold exceedance. A model that predicts rainfall intervals and the corresponding probability of occurrence for lead times from 10 minutes to 6 hours is proposed. The model employs cumulative rainfall depths from recording stations in an area of 50 km radius from the Ischia Island as inputs for a Feed Forward Neural Network to nowcast rainfall in the 4 rain-gauges over the study area. Based on almost 400 rain events observed during years 2009-2022, 24 machine learning models were independently trained for each rain-gauge and each of the 6 lead-times - 10, 30, 60, 120, 180 and 360 minutes. The performance of each model was evaluated and compared using different metrics, both continuous (RMSE and MAE) and categorical (POD and FAR). In addition, the Eulerian Persistence (EP) was considered as a benchmark model. The rainfall nowcasts showed encouraging results. Even though for convective rain events the potential lead-time is short, the models produced consistent nowcasts for lead-times up to 2 hours. With probabilities of almost 90%, the thresholds exceedance was forecasted up to 1 hour before. As expected, predictive accuracy and probabilities gradually decreased as the lead-time increased, according to physically based models. Moreover, the proposed models outperformed the benchmark EP for all the lead-times and performance criteria. Results confirmed that the use of cumulative rainfall depths for precipitation nowcasting made this approach a promising tool for nowcasting purposes, and his flexibility and conceptual simplicity resulted in a rapid, easily replicable and convenient nowcasting approach. To conclude, the proposed models enhanced a first identification of critical thresholds, which should be further analysed in order to achieve a better, complementary understanding of the occurring phenomenon. 

Keywords: Precipitation nowcasting; Multi-step predictions; Rain-gauge measurements; Pattern recognition; Feed forward neural networks; Cumulative rainfall fields.

How to cite: Pirone, D., Del Giudice, G., and Pianese, D.: Machine Learning models for probabilistic rainfall nowcasting applied to a case study in Italy: the extreme rainfall event on 26 November 2022 over Casamicciola town, Ischia Island., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14545, https://doi.org/10.5194/egusphere-egu23-14545, 2023.

Extreme hydrological events, which are highly concerned by local governments, hydraulic units and hazard response centers due to their potential to bring heavy rainfall and cause serious floods, have frequently occurred and impacts on Taiwan urban area in recent years under the circumstance of climate change and global warming. The frequent occurrence of high intense storm always leads to flooding-related disasters within a short period, which makes rainfall monitoring a disaster prevention. Therefore, this study utilizes Long Short-Term Memory Neural Networks (LSTM) and Back Propagation Neural Networks (BPNN) to extract the characteristics of radar observations and forecast rainfall with time 1-step-ahead to 6-step-ahead (T+1~T+6) in Taiwan’s capital, Taipei City. The data collection was included in the Shulin dual-polarization radar (RCSL) observations, such as differential phase shift, specific differential phase, reflectivity and doppler radial wind field, and rain gauge data from May 2021 to November 2021 in the Taipei City. With a view to capturing the movement of hydrometeors continually changes within the time step, an algorithm which can calculate velocity and direction of specific hydrometeors on two-dimensional matrix were developed and applied to simulate location of the specific hydrometeors on n-step-ahead (T+n). Finally, the rainfall forecast can be achieved by using the simulated location of specific hydrometeors and its physical properties from radar observations as input data to fit rainfall from the gauge. This study aims to investigate the relationship between short-duration rainfall and radar observations by artificial neural network (ANN), and forecast the rainfall  within a short period.

Keywords: Dual-Polarization Radar; Rainfall Estimation; Artificial Intelligence (AI), Artificial neural network (ANN); Long Short-Term Memory Neural Networks(LSTM)

How to cite: Lin, J.-L., Hsu, C.-Y., and Chang, L.-C.: Improving Dual-Polarization Radar-based rainfall estimation using Long Short-Term Memory Neural Networks, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14709, https://doi.org/10.5194/egusphere-egu23-14709, 2023.

Reliable early forecasting of summer air temperature is important to effectively prepare and mitigate damage such as heat-related mortality and excessive electricity demand caused by heat waves and tropical nights. Numerical weather prediction (NWP) models have been used for operational forecasting of air temperature. However, NWP models have coarse spatial resolution due to massive computational resources arising from complex forecasting systems and unstable parameterization of NWP models, which make the uncertainty of prediction, consisting of systematic and random biases. Therefore, the objective of this study is to develop a novel deep learning-based statistical downscaling approach for the Global Data Assimilation and Prediction System (GDAPS) model’s summer air temperature forecasts over South Korea. This study developed the proposed statistical downscaling model through the decomposition into the temporal dynamics of daily air temperature forecast and spatial fluctuation by pixels. The daily temperature dynamic was estimated using a daily mean GDAPS temperature forecast with simple mean bias correction. The spatial fluctuation by pixels was obtained using the spatial anomaly of downscaled air temperature forecast by the U-Net model. The GDAPS model’s forecast data, present-day high spatial resolution satellite observations, and topography variables were used as input variables for training the U-Net model. The observations at weather stations were spatially interpolated using the regression-kriging, and then we used it as a target image for the U-Net model. The proposed U-net model was compared with the Local Data Assimilation and Prediction System (LDAPS), the dynamically downscaled model of the GDAPS, and the support vector regression (SVR)-based statistical downscaling model. For next-day Tmax and Tmin forecasts, the suggested U-net model showed better performance, having high coefficient of determination (R²) of 0.76 and 0.74 and root mean square error (RMSE) of 2.5 °C and 1.5 °C for next-day Tmax and Tmin forecasts, respectively. When analyzing the skill score (SS) values by stations of the U-Net model, it had remarkably high SS values at stations where the GDAPS had a high absolute value. For Tmax and Tmin forecasts with 1-7 days forecast lead time, the suggested model consistently provided better performance (higher spatial correlation and lower RMSE) than GDAPS and SVR. In addition, the U-net model showed a detailed spatial distribution most similar to that of the observations. These results demonstrated that the suggested model successfully corrected the bias of the GDAPS, improving not only the forecast accuracy but also the ability to capture the spatial distribution of Tmax and Tmin forecasts. Using the deep learning-based suggested model in this study, bias-corrected high spatial resolution air temperature forecasts with a relatively long forecast lead time in summer seasons can be successfully produced.

How to cite: Cho, D., Im, J., and Jung, S.: Deep learning-based statistical downscaling for short-term forecasting of summer air temperatures, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14894, https://doi.org/10.5194/egusphere-egu23-14894, 2023.

How to cite: Nerini, D., Zanetta, F., Schaer, M., Bhend, J., Spirig, C., Moret, L., and Liniger, M. A.: Operational machine learning for the postprocessing of surface wind forecasts, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14973, https://doi.org/10.5194/egusphere-egu23-14973, 2023.

This work introduces a novel deep-learning method for generating realistic ensembles nowcast of radar-based precipitation at a five-minute time resolution for the next 60 minutes and longer.

The proposed method is composed of a combination of two models: the first model is trained to compress and decompress the spatial domain into and from a discrete representation (tokens), while the second model evolves the compressed representation over time. Specifically, the compression and decompression model is based on a combination of a Quantized Variational Autoencoder with a Generative Adversarial Network, while the prediction over time leverages a Generative Pretrained Transformer (GPT) architecture.

This separation of concerns (discretized spatial compression/decompression and temporal extrapolation) adds several desirable features not present in more commonly used deep learning methods based on recurrent/convolutional deep learning architectures: 

• transformer output probabilities can be leveraged to generate ensemble/probabilistic forecasts (without the need of injecting noise)
• the discretized spatial representation can be used to characterize each token, adding interpretability and explainability to the model
• the combination of transformer probabilities and token characterization can be used at inference time for forecasts conditioning based on external factors (e.g. NWP forecast output)

The presented architecture is trained and tested on a 7-year radar dataset of reflectivity composites of the Emilia-Romagna Region, Italy. The method is then applied at two different scales: regional, over Emilia-Romagna, and national, on the entire Italian domain, showing the adaptability of the approach to multiple spatial domains. We will present the performance of this model for both deterministic and ensemble settings by comparing it with respect to other commonly used extrapolation and deep learning methods.

How to cite: Franch, G., Tomasi, E., Poli, V., Cardinali, C., Cristoforetti, M., and Alberoni, P. P.: Ensemble precipitation nowcasting by combination of generative and transformer deep learning models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15153, https://doi.org/10.5194/egusphere-egu23-15153, 2023.

It is generally accepted that weather forecasts contain errors due to the chaotic nature of the atmosphere. Regression models, such as neural networks, are traditionally trained to minimize the pixel-wise difference between their predictions and ground truth. The major shortcoming of these models is that they express uncertainty about prediction with blurring, especially for longer prediction lead times. One way to tackle this issue is to use a generative adversarial network, which learns what real precipitation should look like during training. Coupled with a loss, such as Mean Squared or Mean Absolute Error, these networks can produce highly accurate and realistic nowcasts. As there is an inherent randomness in those networks, they allow to be sampled from, just like ensemble models, and various probabilistic metrics can be calculated from the samples. In this work, we have designed a physically-constrained generative adversarial network for radar reflectivity prediction. We compare this network to one without physical restraints and show that it predicts events with higher accuracy and shows much less variance among its samples. Furthermore, we explore fine-tuning the network to the prediction of severe weather events, as an accurate prediction of these benefits both automated warning systems and forecasters.

How to cite: Choma, M., Murín, M., Bartel, J., Troller, M., and Najman, M.: Probabilistic Precipitation Nowcasting with Physically-Constrained GANs, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15514, https://doi.org/10.5194/egusphere-egu23-15514, 2023.

The Mediterranean area is often struck by severe weather events and deep convective events because of the presence of the warm sea, the complex orography of the area, and the specific synoptic scale environment. This scenario is worsened by climate change because, as climate change is affecting many weather and climate extremes, and the frequency and intensity of heavy precipitation events have increased in most of the world.

Over the past years, the use of Numerical Weather Prediction (NWP) models, along with an increasing availability of computing power, led to an improvement of the forecast accuracy. However, NWPs have well-known difficulties in capturing the physical processes at small spatial and temporal scales which are involved in convective or severe weather events. 

In this work we study the impact of assimilating GPS-ZTD (Global Positioning System-Zenith Total Delay) on the precipitation forecast over Italy for the month of October 2019, characterized by several moderate to intense precipitation events. The Weather Research and Forecasting (WRF, version 4.1.3) is used with its 3DVar data assimilation system. The horizontal resolution is 3km while the vertical domain spans the whole troposphere and lower stratosphere.

A dense network of about 500 GPS receivers was used for data assimilation and verification of the atmospheric water content. The dataset was built collecting data from all the major national and regional GNSS permanent networks, achieving dense coverage over the whole area.

Results show that WRF underestimates the atmospheric water content for the period, and GPS-ZTD data assimilation reduced this underestimation by increasing the water content of the atmosphere. The GPS-ZTD data assimilation increases the precipitation forecast amount, and the model performance are improved up to 6h.

Results for a case study show that the GPS-ZTD data assimilation can improve the precipitation forecast in different ways: predicting rainfall missed by the model without data assimilation or better focusing the precipitation already predicted by the model without GPS-ZTD data assimilation on the impacted area, the main drawback being the prediction of false alarms.

How to cite: Federico, S., Torcasio, R. C., Realini, E., Tagliaferro, G., and Dietrich, S.: Results of a GPS Zenith Total Delay data assimilation experiment over Italy, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16271, https://doi.org/10.5194/egusphere-egu23-16271, 2023.

The study focuses on identifying potential “windows of opportunity” for the enhanced predictability of extreme events, such as severe Northern Eurasian cold air outbreaks as these events have significant impacts on human health, energy use, agriculture and welfare.  The extended-range predictability of extreme events is closely related to the preceding large-scale circulation patterns and remote teleconnections. To assess the predictability of these events and attribute their causes we use ensemble hindcasts (i.e., reforecasts for dates in the past) from five prediction systems from the S2S database – namely, from the European Centre for Medium‐range Weather Forecasts (ECMWF), the United Kingdom Met Office (UKMO), Météo‐France (CNRM), Bureau of Meteorology (BoM), Japan Meteorological Agency (JMA). These models have long re-forecast periods and big ensemble sizes necessary to establish statistically robust results. Moreover, the comparison of the forecasts from these six models evaluates the ability of modern prediction systems to forecast extreme events well in advance and highlights the main sources of predictability. We subsample the hindcasts into two groups according to their skill to predict an extreme event beyond weather predictability horizon (lead time week 2 and 3) in order to study the systematic relationship between preceding conditions and the onset of extreme events. Next, we evaluate the flow configurations in the initial conditions: the state of the stratospheric polar vortex (SPV), the phase and amplitude of the Madden-Julian Oscillation (MJO) in the tropics, and the weather regimes over the North Atlantic and Europe. This analysis provides a systemic evaluation and understanding of the large-scale patterns that can potentially contribute to the onset of extreme events over Eurasia, therefore, extending their predictability. Our results show that in overall models tend to over-predict cold conditions after certain states of the remote drivers but there is case-to-case variability in the predictability of the individual events. Moreover, this study assesses and compares the results from several state-of-art predicting systems which provides useful information for model developers as well as for forecast users.

How to cite: Erner, I., Karpechko, A., and Järvinen, H.: Factors influencing subseasonal predictability of Northern Eurasian cold spells, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-360, https://doi.org/10.5194/egusphere-egu23-360, 2023.

Extreme stratospheric polar vortex events, such as sudden stratospheric warmings (SSW) or extremely strong polar vortex (SPV) states, can have a prolonged downward impact, influencing surface weather for several weeks to months. These events often lead to changes in the midlatitude storm track position and associated cyclone frequency over the North Atlantic and Europe. Such changes can result in infrastructure damage and health impacts due to cyclone-associated extreme winds and the risk of flooding or heavy snowfall. However, there exists a strong inter-event variability in these downward impacts on the tropospheric storm track, leading to opposite predictions of the storm track response. Therefore, identifying the biases in the forecast of the downward impact of stratospheric polar vortex extremes can improve the predictability of extratropical winter storms on subseasonal-to-seasonal timescales, and has the potential to benefit society and stakeholders.

Using ECMWF reanalysis data and ECMWF reforecasts from the Subseasonal to Seasonal (S2S) Prediction Project database, we investigate the stratospheric influence on extratropical cyclones, identified with a cyclone detection algorithm. Following SSWs, there is an equatorward shift in cyclone frequency over the North Atlantic in reforecasts, and a poleward shift is observed after SPV events, consistent with the response in reanalysis. However, less than 70% of the reforecasts capture the sign of the cyclone frequency response over the North Atlantic during weeks 1-2 after SSWs, and less than 50% of the reforecasts capture the response during weeks 3-4. The cyclone forecasts following SPV events are generally more successful. We further discuss the differences in predictability of extratropical cyclones between the two types of stratospheric extremes.

The results provide new insights on the role of the stratosphere in subseasonal variability and predictability of extratropical cyclones during winter that can be used for forecasting their frequency and surface impacts.

How to cite: Gerstman, H., Büeler, D., Wulff, C. O., Sprenger, M., and Domeisen, D.: The influence of the stratosphere on the North Atlantic storm track predictability in subseasonal-to-seasonal reforecasts, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-553, https://doi.org/10.5194/egusphere-egu23-553, 2023.

Boreal summer Intraseasonal Oscillation (BSISO), with its 20–90 day periodicity characterised by northward propagation over the northern Indian Ocean and eastward propagation over the equatorial region, acts as a major source of predictability in the intraseasonal time scale. Predicting the initiation of BSISO over the equatorial Indian Ocean is of vital importance in the prediction of BSISO's northward advancement over the ISM domain. This study tries to investigate where we stand in terms of predicting the BSISO initiation and propagation, making use of the reforecasts available from the different operational forecasting centres part of the Sub-Seasonal-to-Seasonal (S2S) prediction project. The BSISO convective initiations over the Equatorial Indian Ocean are objectively identified using OLR MJO Index(OMI), and the ability of the models to simulate the initiation and propagation of BSISO is assessed. The BSISO propagation skill, quantified in 9 S2S models, ranges from 11 to 29 days, while the BSISO initiation skill, quantified in 4 out of 9 models, ranges from 11 to 16 days, which is systematically lower compared to the skill of the BSISO non-initiation stages. Two major regions of BSISO initiation were identified, one over the Western Equatorial Indian Ocean and another over the Eastern equatorial Indian Ocean. Over these identified initiation regions, observation show a buildup (reduction) of lower tropospheric moisture before (after) the BSISO initiation. Out of the 9 models considered, few capture either the buildup or reduction, while the majority of the models show biases in capturing the moisture buildup and reduction. Previous studies have emphasised the role of background moisture in the propagation of BSISO. The relationship between the background moisture gradient over the ISM domain and the BSISO propagation prediction skill is examined in the S2S models and a positive relationship is found.

How to cite: Simon, D. and Joseph Mani, N.: Boreal Summer Intraseasonal oscillation convective initiations in S2S models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-861, https://doi.org/10.5194/egusphere-egu23-861, 2023.

Reliable (sub) seasonal (S2S) forecasts remain a huge scientific challenge. The lead-time is too long to benefit from the atmosphere’s inertial memory, but too short for the atmosphere’s boundary conditions to be felt strongly. Only for specific "windows of predictability" (i.e. specific regions, timescales and climatic background states), skillful forecasts are possible, in an otherwise largely unpredictable future. Due to a number of successes in S2S forecasting, the interest in machine learning (ML) is growing fast. However, we argue there is a need for more standardization, consensus on best practices, higher efficiency, and higher reproducibility. Typical S2S ML use-cases, such as (1) pure statistical forecasting based on observations, (2) transfer learning, and (3) post-processing of dynamical model ensembles, require a large coding and preprocessing effort. Such experiments are not trivial to set up, and without sufficient experience and expertise there is a large risk of improper cross-validation and/or improper and non-standard verification.

Within a 3-year project, we are developing a high-level Python package called s2spy. Our aim is to make ML workflows more transparent and easier to build, and to facilitate standardization and collaboration across the S2S community. s2spy also contributes to a higher reproducibility and works towards a wider acceptance of standards and best practices. We will present our vision and the capabilities of our package, show-casing that we can build a model from raw climate data up to verification and explanation in only a few lines of code.

How to cite: Liu, Y., Schilperoort, B., van Ingen, J., Vijverberg, S., Kalverla, P., and Coumou, D.: s2spy, a package to boost (sub) seasonal forecasting with artificial intelligence, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-953, https://doi.org/10.5194/egusphere-egu23-953, 2023.

Across East Africa, sub-seasonal rainfall variability predominantly depends on the phase of the Madden Julian Oscillation (MJO). Rainfall is enhanced during MJO phases 2 to 4, and suppressed during phases 6 to 8. Given that MJO-induced anomalous precipitation can persist beyond several days, a surface response is expected. Using earth observations and reanalysis data, in this presentation we will show how MJO-induced precipitation anomalies promote a surface response which feeds back onto local and regional atmospheric conditions.

              MJO-induced rainfall suppression across East Africa decreases surface soil moisture across the exit region of the Turkana jet. Reduced soil moisture increases surface sensible heat fluxes and elevates land surface temperatures. The drier and warmer surface reduces surface pressure and leads to an intensification of the Turkana jet. We conclude that on average approximately 11% of the anomalous jet speed is associated with surface-driven pressure fluctuations over the course of a single day. Since the Turkana jet controls moisture transport from low-lying regions of East Africa into Central Africa, we highlight that surface-induced jet variations impact rainfall totals across East Africa. Furthermore, due to the Turkana jet response to spatial variations in surface warming, we also identify that the magnitude of MJO-induced anomalous precipitation is influenced by surface conditions prior an MJO event. For example, when the surface over southern South Sudan is anomalously dry, MJO-induced precipitation suppression is greater. This presentation will highlight that to fully exploit predictability from the MJO, forecast models must correctly represent surface processes and land-atmosphere interactions. Future work evaluating sub-seasonal forecast models and improving the representation of land-atmosphere interactions will enhance the lead-time of early warning systems across East Africa.

How to cite: Talib, J., Taylor, C., Harris, B., and Wainwright, C.: MJO-induced land-atmosphere feedbacks across East Africa, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1102, https://doi.org/10.5194/egusphere-egu23-1102, 2023.

We implement deep neural networks to forecast monthly precipitation over Europe. This architecture conformed by several convolutional layers and fully connected layers uses four different variables (surface temperature, west-east wind at 200 hPa, precipitation and sea level pressure) coming from seven different operational forecast systems (1. ECCC 2. MeteoFrance  3. DWD  4. JMA 5. NCEP  6. ECMWF  7. CMCC). The neural network is trained using observations from E-OBS, a gridded land-only observational dataset covering the whole European continent. This convolutional neural network is trained using the period 1993-2012 and the validation period is 2013-2016, which is the range that is available for all operational forecast systems. 

Comparing with precipitation from observations we show that forecasted precipitation from this Deep-Learning model shows small biases in the whole European continent when forecasting monthly precipitation, especially over Sweden (with a small overestimation of less than 0.2 mm/day). With some higher negative biases over Southern Europe (<-1 mm/day). In turn, the representation of the mean precipitation over specific months and seasons was also assessed, showing that during the validation period this method is able to reproduce properly the spatial features of mean precipitation over Europe and its intensity.

How to cite: Fuentes-Franco, R. and Zimmermann, K.: Deep-learning-based monthly precipitation forecast for Europe, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1187, https://doi.org/10.5194/egusphere-egu23-1187, 2023.

How to cite: Romanovska, P., Schauberger, B., and Gornott, C.: Wheat yields in Kazakhstan can successfully be forecasted using a statistical crop model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1286, https://doi.org/10.5194/egusphere-egu23-1286, 2023.

A new method of classification based upon an empirical orthogonal functions (EOFs) analysis of zonal wind anomalies of the 70 winters from 1950 to 2020, extracted from ERA5, revealed that the winter stratosphere tends to follow four independent scenarios. The first three scenarios: the January, the February, and the Double modes, are all characterized by a perturbed evolution of the polar vortex due to significant sudden stratospheric warmings (SSWs) occurring in mid-winter, generally causing the reversing of zonal winds. Unsurprisingly, these modes contain the information of preferential important SSWs’ timings, events including minor and major SSWs, and final stratospheric warming’s timings. Thus, their patterns show that the mid-winter is often anti-correlated with the winter end. This result is consistent with the conclusion done in a recent study showing that the polar vortex on a given month is anti-correlated with its state 2-3 months earlier. While the last scenario illustrates an unperturbed polar vortex evolution during winter for which only the final stratospheric warming’s timing differs, either early and dynamical or late and radiative.

The study of the mean evolutions of wave-1 and wave-2 amplitude anomalies associated with these four scenarios reveals that they possess singular dynamic behavior, especially for the wave-1 activities, which are consistent with their mean evolutions of zonal mean zonal winds. Indeed, we found that the wave-1 activity drops systematically for each scenario when zonal winds weaken due to an important. In contrast, it is not the case for the wave-2 activity.

How to cite: Mariaccia, A., Keckhut, P., and Hauchecorne, A.: New classification showing the stratospheric memory concept: towards a better seasonal prediction, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1376, https://doi.org/10.5194/egusphere-egu23-1376, 2023.

During a Madden-Julian Oscillation (MJO) event, anomalous convection triggers a dynamical response with anomalous large-scale ascent and upper-tropospheric divergence outside the tropics creates interaction between the MJO and the extratropical weather, modes of global circulation and climate variability. The MJO is known to have an impact on China rainfall and regional circulation with enhanced/ suppressed rainfall in South China during the propagation of the MJO from the Indian Ocean into the western Pacific. As the MJO is considered a major source of predictability at subseasonal time scales, it is important to understand how climate models are representing the MJO and its remote effects. This study is aimed to investigate the modelled MJO and associated local effects in China precipitation using the Met Office Unified Model (MetUM). It was found that the response of the rainfall over South China is asymmetric, with the enhancement of rainfall during the Indian Ocean convective phases (phase 2) of the MJO being much stronger than the suppression during the west Pacific phases (phase 6). This response signal was mostly due to the increase in probability of extreme precipitation events rather than the increase in number of rainy days. Analysis of the modelled MJO and associated response shows, although the MJO is more realistically represented in the atmosphere-ocean coupled simulation, the atmosphere-only simulation showed more evidence of MJO-related remote effects in the rainfall patterns over China. The ocean-coupled simulation shows no significant response to the propagation of MJO-associated convection whereas the atmosphere-only simulation shows the correct pattern of enhancement and suppression of rainfall and associated regional circulation pattern changes. The differences found in the representation of remote effects between atmosphere-only and ocean-coupled simulations may be attributed to the air-sea interaction processes and to fundamentally different mean-state biases which affect not only the representation of the MJO but also the propagation of MJO-induced Rossby waves. 

How to cite: Carvalho, M. J., Xavier, P., and Furtado, K.: MJO-related China rainfall teleconnections in the MetUM, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2752, https://doi.org/10.5194/egusphere-egu23-2752, 2023.

Madden-Julian Oscillation (MJO), an intraseasonal oscillation over the equatorial Indian ocean and Pacific, has profound impacts around the globe. Its extended-range life cycle (20-90 days) makes it the most important predictability source on subseasonal-to-seasonal timescales. While the mechanisms responsible for MJO's life cycle have been well  explored through the frameworks of moisture modes, and tropical wave dynamics, the mechanisms of initiation remain unsolved. By using linear inverse modeling (LIM) and incorporating different frameworks, this study investigates the processes resulting in MJO convection initiation. It is suggested that multi-scale interactions play a vital role in intraseasonal convection initiation over the Indian ocean. On intraseasonal timescales, the remnant of former MJO can create an environment favoring the convection development for the next event through modulating the prevailing circulations and moisture state (e.g., moisture advection). On shorter timescales (< 20 days), the optimal initial condition arises from the synoptic convergence/divergence of moisture flux, and the upper troposphere instability. 

How to cite: Chang, C.-H. and Tseng, K.-C.: The Optimal Initial Condition of MJO Development, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3039, https://doi.org/10.5194/egusphere-egu23-3039, 2023.

France is committed to achieving climate neutrality by 2050. In this respect, the heating sector, one of the largest energy-consuming sectors in France, is undergoing rapid electrification. In 2022, electricity contributed to the heating of more than 40% of French dwellings. As a result, the French electricity demand is increasingly becoming thermosensitive. Accordingly, for every 1°C drop in temperature below the threshold (i.e., 15°C) during winter, the electricity demand increases by ~2.4 GW in France. With a notable share of French nuclear reactors reaching their end of service life, several recent episodes of widespread cold spells over France have raised concerns about energy security. Hence, anticipating cold spells well in advance is increasingly becoming important for the smooth operation of the energy sector. In this regard, we assess the predictability of several recent episodes of cold spells on seasonal timescales over France using the seasonal predictions from the European Centre for Medium-Range Weather Forecasts. Additionally, we test a recently developed statistical downscaling methodology in forecasting cold spells over France, using the forecasts of upper-level fields, which are better predicted than the surface fields. On comparing the dynamical and statistical predictions, we show that the statistical predictions, relying upon the information contained in the better-predicted upper-level fields, perform significantly better than the dynamical counterparts in predicting cold spells beyond a month.

How to cite: Goutham, N., Omrani, H., Himych, O., and Plougonven, R.: How well in advance can we predict cold spells over France?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5201, https://doi.org/10.5194/egusphere-egu23-5201, 2023.

Greenland blocking (GL) resembles the negative phase of the NAO and features a strong positive Z500 anomaly over Greenland and a zonally aligned negative anomaly stretching from the eastern North Atlantic into Northern Europe. The prevailing westerly flow is then deflected southward and extends into the Mediterranean. It causes melting events of the Greenland Ice Sheet which can impact global sea-level rise and has strong downstream impacts on Europe. It occurs year-round, although is more common in winter (11.7%) compared to summer (9.1%). GL is forecast with good ability by S2S models. This skill is driven by the performance in winter, when GL is persistent. In this study, we explore whether the skill of GL blocking can be linked to external meteorological drivers or the prevalence of specific meteorological features. Re-forecasts using the European Centre for Medium-Range Weather Forecasts for the 1999-2019 period are considered and compared against ERA Interim reanalysis over the same period. We focus on the factors affecting the skill, as depicted by the Brier Skill Score, from lead times 6 to 10 days, where the skill is 30% to 70% smaller than the skill at lead time 1 day.

Results show that most of the GL blocking events associated with low skill occur in spring. In this season, the model fails in forecasting the transition from Scandinavian Blocking to Greenland Blocking, in opposition to the rest of the seasons, when this transition is well predicted. The analysis of the role of large-scale processes that affect GL skill reveals that half of the forecasts of GL events initialized up to 30 days after a sudden stratospheric warming shows poor skill. In addition, the forecasts of GL events initialized with an active MJO in phase 6 and 7 present good skill whereas those forecast GL events initialized during an active MJO in phase 2 to 4 show poor skill. This link between large-scale factors and skill offers potential guidance in operational forecasting.

How to cite: Osman, M., Grams, C. M., and Beerli, R.: Factors influencing sub-seasonal forecast skill of Greenland Blockings, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5397, https://doi.org/10.5194/egusphere-egu23-5397, 2023.

The Indian Ocean Dipole (IOD) is a major source of seasonal climate variability in the
Indian Ocean. This dipole has strong impacts on the Indian Ocean region and through
teleconnections can influence the seasonal climate of remote regions like the North Pacific
and North Atlantic. A prominent example of this teleconnection from the IOD occurred
in the winter 2019/2020, where the IOD was in a positive state. This influenced the state
and predictability of the Northern Hemisphere extratropics. Thus, a good understand-
ing of the mechanism that transports information from the Indian Ocean to the North
Atlantic is desirable. In this contribution we investigate the special teleconnection of the
winter 2019/2020 and analyse the transport mechanism.
In model experiments with the OpenIFS from ECMWF we show that the NAO in the
winter 2019/2020 is influenced by the IOD and analyse the teleconnection mechanisms.
We use hindcast ensemble model experiments of the DJF season 2019/2020 to analyse
the behaviour of the IOD and its impact on the NAO. In the uncoupled OpenIFS the Sea
Surface Temperature (SST) boundary conditions are perturbed in regions of importance
to the NAO (like the ENSO region and the Indian Ocean). With these perturbations we
identify the relative importance of individual ocean regions to the state of the NAO in
the winter of 2019/2020.
We contrast the experiments with the perturbed SST conditions to the operational ECMWF
System5 forecast and ERA5. Experiments with the 2019/2020 SST’s in the In-
dian Ocean (with other boundary conditions set to climatology) reproduce many of the
observed atmospheric 2019/2020 features. In contrast, experiments with SST’s in the
Pacific show very different patterns to the observed 2019/2020 ones.
We identify eddy-mean-flow interactions as a mechanism that connects and transports
information from the Indian Ocean to the North Atlantic. With Hoskins E-Vectors we
show that anomalous eddy activity during IOD events impacts the position and strength
of the Northern Hemisphere extratropical jet. This interaction provides a teleconnection
mechanism in addition to the Rossby-wavetrain discussed in other studies.

How to cite: Hempel, T., Weisheimer, A., and Palmer, T.: The seasonal teleconnections of the Indian Ocean Dipole to the North Atlantic region, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5665, https://doi.org/10.5194/egusphere-egu23-5665, 2023.

How to cite: Drouard, M., Pérez-Aracil, J., Barriopedro, D., Zaninelli, P. G., Garrido-Perez, J. M., Fister, D., Salcedo-Sanz, S., and García-Herrera, R.: S2S prediction of summer heatwaves in the Iberian Peninsula using convolutional networks, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5742, https://doi.org/10.5194/egusphere-egu23-5742, 2023.

As first explored by Thompson and Barnes (2014), hemispheric mean storm activity (or related quantities, such as the meridional eddy heat flux) exhibits periodicity on 20-30 day time scales.  They characterized this variability with the so-called Baroclinic Annular Mode, or BAM, a ring of enhanced eddy activity which is present in both hemispheres, but most pronounced in the south, which is less perturbed by zonal asymmetries relative to the north.   The mechanism behind this internally generated periodicity, however, has remained elusive.  We probe the dynamics and structure of the BAM on two fronts.  To understand the mechanism, we develop a minimal model that captures the essential dynamics: 2 layer quasi-geostrophic flow in a channel. By varying the geometry of the channel and the thermal and frictional forcing, we tease out the parameters that control the period and amplitude of the BAM.  The resulting changes in the BAM support the general framework of the charge-discharge mechanism proposed by Thompson and Barnes, but demand a more detailed explanation for the coupling between eddies and the mean baroclinicity that generates enhanced variability on subseasonal time scales.  On a second front, we apply dynamical mode decomposition (DMD) to atmospheric reanalyses of the Southern Hemisphere to quantify the structure of the southern BAM.  DMD captures BAM variability, providing additional information on relationships between the eddy kinetic energy and other mean and eddy quantities.  It suggests that moisture plays a fundamental role in the relationship between the eddy activity and baroclinicity, and that changes in stratification are more important than horizontal temperature gradients in the dynamics.    In this sense, the underlying BAM dynamics of vacillation between eddy and potential energy are remarkably robust, active in our moist atmosphere and in dry quasi-geostrophic systems where only the meridional temperature gradient can capture the available energy.

How to cite: Gerber, E., Youngs, M., and Pauluis, O.: The Dynamics and Structure of the Baroclinic Annular Mode, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6161, https://doi.org/10.5194/egusphere-egu23-6161, 2023.

The Daily Tropical Cyclone Probability (DTCP), defined as the probability of tropical cyclone occurrence within 500 km of a location in one day, is proposed and used in evaluating subseasonal to seasonal (S2S) predictions from the S2S Prediction Project Database, from May 1 to Oct. 31, 1999, to 2010. The ensemble reforecasts are collected from eleven operational centers, the BoM, CMA, ECCC, ECMWF, HMCR, ISAC, JMA, KMA, METFR, NCEP, and UKMO.  In both observation and these eleven forecast models, the DTCP is modulated by the Boreal Summer Intraseasonal Oscillation (BSISO), depicted by the two indices, BSISO1 and BSISO2. During BSISO1 phases 1, 5, 6, 7, and 8, the DTCP in the northwestern Pacific region is ~3.5 times higher. Similarly, during phases 1, 2, 3, 4, and 8 of BSISO2, the DTCP is  ~2.5 times higher.  Among the eleven models, the ECMWF model best reproduces the climatological DTCP and its modulation by the BSISO in the western North  Pacific region, followed by NCEP, KMA, JMA  models. Using the DTCP metric, the highest debiased Brier Skill Score of the eleven models is from ECMWF, which has a slightly less skillful prediction than the reference climatological forecast with lead time 11 to 30 days. The skill of the eleven models is higher during the non-active phases of tropical cyclone activity than their skill during the active phases.  The updated results based on the real-time tropical cyclone forecasts of the S2S Prediction Project Database  from these eleven systems will also be discussed.

How to cite: Wang, X., Waliser, D., Vitart, F., Jiang, X., and Asharaf, S.: Evaluating Western North Pacific Tropical Cyclone Forecast in the Subseasonal to Seasonal Prediction Project Database, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6511, https://doi.org/10.5194/egusphere-egu23-6511, 2023.

Tropical cyclone precipitation (TCP) contributes a significant fraction of total annual rainfall and also is a frequent cause of extreme precipitation in many parts of the tropics. The climatology of TCP in the S2S models is characterized by dry biases in the North Atlantic and wet biases in most other basins,  specially in the Southern Indian Ocean and Australia. 
Biases in total precipitation (P), TCP and their ratio (TCP/P) are mostly positive in the multi-model ensemble mean and change very little with lead time. in these models the frequency biases are the dominant contribution to TCP biases. However, in some models, there are positive biases in average precipitation per each TC which contribute significantly to TCP biases at equatorial latitudes.

The prediction skill of these reforecasts is evaluated using skill scores such as the ranked probability skill score for TCP and the Brier Skill score for genesis and occurrence. The implication of these results is discussed for their relevance to mean and extreme precipitation prediction skill using S2S models.

How to cite: Garcia-Franco, J. L., Lee, C.-Y., Camargo, S., Tippett, M., Kim, D., Molod, A., and Lim, Y.-K.: Tropical cyclone precipitation skill in S2S models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7102, https://doi.org/10.5194/egusphere-egu23-7102, 2023.

Information at the sub-seasonal to seasonal (S2S) time scale can be of high socio-economic value to a variety of users whose decision-making depends on climate variability. The usability of S2S forecasts generated by Numerical Weather Prediction (NWP) systems has increased over the years not only due to their skill improvement but also due to their potential to bridge the medium-range and seasonal horizons. The skill of the sub-seasonal (4-6 weeks ahead) and seasonal (6-12 months ahead) NWP-based forecasts in space and time depends on different factors, including the representation of the physical process, the initialization frequency and the spatial resolution. However, the NWP model setups differ between the two time horizons, and this consequently intrinsic differences between the two forecast products. To date, it has been subjectively accepted that during the first time horizons, e.g. up to 6 weeks ahead, the sub-seasonal forecasts are more informative than the seasonal forecasts, and hence all efforts on generating a seamless product are implemented through a direct merging of the two products. This unfortunately masks the potential for tailored seamless products that extract the best S2S information available.

Here, we evaluate the S2S hydro-meteorological forecasts from the ECMWF sub-seasonal (ENS-ER) and seasonal (SEAS5) products, aiming to identify their skill complementarity in space and time and further seamlessly communicate them for improved decision-making. Both the ENS-ER and the SEAS5 precipitation and temperature forecasts were bias-adjusted prior to forcing the E-HYPE hydrological model. The investigation focuses on the period 1999-2015. Overall, results highlight both spatial and temporal complementarities between the two systems, which is very encouraging for a seamless communication. In particular, ENS-ER-based hydro-meteorological forecast skill patterns appear to be more homogeneous spatially, while SEAS5-based forecasts ensure skill at longer forecast horizons. This diagnostic analysis is a step forward in hydro-climate services, indicating the tipping points in all European river systems for switching from ENS-ER to SEAS5 forecasts.

How to cite: Pechlivanidis, I. and Crochemore, L.: European S2S streamflow forecasting: Towards a seamless communication, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7168, https://doi.org/10.5194/egusphere-egu23-7168, 2023.

Seasonal forecasts of meteorological drought can help decision-making for weather-driven wildfires (Turco et al., 2018). However, one of the main drawbacks of drought prediction lies in the uncertainty of monitoring precipitation in near-real time. In this contribution we assess the predictability of the Standardized Precipitation Index (SPI) on a global scale, combining 11 datasets (DROP; Turco et al., 2020) as observed initial conditions with empirical and dynamic predictions of precipitation. The empirical predictions are based on the ensemble-based streamflow prediction system (ESP, an ensemble-based reordering of historical data) and the dynamics on the new generation seasonal prediction model developed by ECMWF (System 5; S5). Although both systems show comparable quality, S5 performs better at longer forecast timescales, especially over tropical regions.

Subsequently, we investigate whether the S5 seasonal forecasts can predict area burned anomalies on a global scale. To do so, we link the seasonal climate predictions of S5 to an empirical climate-fire model, using standard regression techniques in the framework of generalised linear models. The seasonal climate predictions of S5 have shown high and significant performance (with a mean relative operating characteristic “ROC” area value of 0.87) over a large fraction of the burnable area (~47%).

In summary, given that all data are publicy available in near real time, our results provide a basis for the development of a global probabilistic seasonal drought and burned area forecast product.

References

Turco, M., Jerez, S., Doblas-Reyes, F. J., AghaKouchak, A., Llasat, M. C., & Provenzale, A. (2018). Skilful forecasting of global fire activity using seasonal climate predictions. Nature Communications, 9(1), 1–9.

Turco, M., Jerez, S., Donat, M. G., Toreti, A., Vicente-Serrano, S. M., & Doblas-Reyes, F. J. (2020). A global probabilistic dataset for monitoring meteorological droughts. Bulletin of the American Meteorological Society, 101(10), E1628–E1644.

Acknowledgements

We acknowledge funding through the project ONFIRE, grant PID2021-123193OB-I00,funded by MCIN/AEI/ 10.13039/501100011033.

How to cite: Torres-Vázquez, M. Á., Gincheva-Norcheva, A., Halifa-Marín, A., Montavez, J. P., and Turco, M.: Probabilistic predictions of global fire activity, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7750, https://doi.org/10.5194/egusphere-egu23-7750, 2023.

Teleconnection patterns associated with the Madden–Julian oscillation (MJO) and El Niño–Southern Oscillation (ENSO) impact weather and climate phenomena in the Pacific–North American region and beyond, and therefore accurately simulating these teleconnections is of importance for seasonal and subseasonal forecasts. Systematic biases in boreal midwinter ENSO and MJO teleconnections are found in eight subseasonal to seasonal (S2S) forecast models over the Pacific–North America region. All models simulate an anomalous 500-hPa geopotential height response that is too weak. This overly weak response is associated with overly weak subtropical upper-level convergence and a too-weak Rossby wave source in most models, and in several models there is also a biased subtropical Pacific jet, which affects the propagation of Rossby waves. In addition to this overly weak response, all models also simulate ENSO teleconnections that reach too far poleward toward Alaska and northeastern Russia. The net effect is that these models likely underestimate the impacts associated with the MJO and ENSO over western North America, and suffer from a reduction in skill from what could be achieved.

How to cite: Schwartz, C., Garfinkel, C., Chen, W., Li, Y., Yadav, P., and Domeisen, D. I. V.: The Winter North Pacific Teleconnection in Response to ENSO and the MJO in Operational Subseasonal Forecasting Models Is Too Weak, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8258, https://doi.org/10.5194/egusphere-egu23-8258, 2023.

Forecasts on sub-seasonal to seasonal (S2S) timescales have huge potential to improve early warning and anticipatory action ahead of high impact events. However, fully realising this potential predictability requires reliable forecasts that are communicated effectively so that they can support appropriate preparedness action. This study reflects on the African SWIFT (Science for Weather Information and Forecasting Techniques) S2S forecasting testbed which brought together researchers, forecast producers and forecast users from a range of African and UK institutions. The testbed used a co-production approach to pilot the provision of real-time bespoke S2S forecast products for applications. The S2S testbed supported decision-makers in a range of sectors and contexts. For example, informing food security decisions and hydropower energy planning in East Africa, supporting agricultural decision-making across West Africa, and, in health applications, increasing the lead-time for potential disease outbreaks.

This study critically reflects on the benefits and challenges of the co-production process within the S2S applications context. Specifically, while having direct access to the real-time S2S data allowed user-guided iterations to products to make them more actionable for their specific context. Some key lessons for effective co-production emerged. First, it is critical to ensure there is sufficient resource to support co-production, especially in the early co-exploration of needs. Second, all the groups in the co-production process require capacity building to effectively work in new knowledge systems. Third, evaluation should be ongoing and combine meteorological verification with decision-makers feedback. Ensuring the sustainability of project-initiated services within the testbed hinges on integrating the knowledge-exchanges between individuals in the co-production process into shaping sustainable pathways for improved operational S2S forecasting within African institutions.

How to cite: Hirons, L.: Using a co-production approach to support effective application of S2S forecasts in Africa, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9342, https://doi.org/10.5194/egusphere-egu23-9342, 2023.

Extreme precipitation events (EPE), especially those leading to floods and landslides, are devastating to society. Predicting these events in advance can help disaster managers to carry out plans of action to respond effectively to any oncoming adverse events. Sub-seasonal forecasts, which aim to predict the weather with 2 weeks to 2 months in advance, can help to provide valuable and actionable information to disaster managers. Given the potential usefulness to end users, it is vital to assess the skill of sub-seasonal forecasts in predicting EPEs. However, given that precipitation is known to be a difficult variable to predict, the lead time at which forecasts are skilful may be limited. This study, therefore, aims to assess at which lead time sub-seasonal forecasts of atmospheric drivers of EPEs are skilful.

The study investigates the skill of the European Centre for Medium-Range Weather Forecast (ECMWF) sub-seasonal reforecast in predicting EPE over Italy from 2001 to 2020. A total of 100 EPEs are used as case studies. The variables evaluated are total precipitation, mean sea level pressure, geopotential height at 500 hPa and specific humidity at 850 hPa. Variables are averaged over the 5 days surrounding the date of the EPE. ERA5 is used as the reference dataset. Both deterministic and probabilistic metrics are used to assess the skill of the reforecast.

Results show that the skill for precipitation is limited to the first two weeks. Nevertheless, the ECMWF sub-seasonal product is skilful in predicting the atmospheric fields associated with the selected EPEs, such as MSLP and geopotential height, showing both reliability and discrimination beyond two weeks.

How to cite: Scott, W., Gaetani, M., and Fosser, G.: Skill assessment of sub-seasonal forecasts of different atmospheric variables related to extreme precipitation events over Italy, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10052, https://doi.org/10.5194/egusphere-egu23-10052, 2023.

Predicting the temporal and spatial patterns of South Asian monsoon rainfall within a season is of critical importance due to its impact on agriculture, water availability, and flooding. The monsoon intraseasonal oscillation (MISO) is a robust northward-propagating mode which determines the active and break phases of the monsoon, and much of the regional distribution of rainfall. However, dynamical atmospheric forecast models predict this mode poorly. Data-driven methods for MISO prediction have shown more skill, but only predict the rainfall portion corresponding to MISO.

Here, we combine state-of-the-art ensemble precipitation forecasts from a high-resolution atmospheric model with data-driven forecasts of MISO using a novel method. The ensemble members of the detailed atmospheric model are projected onto a lower-dimensional subspace corresponding to the MISO dynamics, and are then weighted according to their distance from the data-driven MISO forecast in this subspace. We thereby achieve significant improvements in rainfall forecasts over India, as well as the broader monsoon region, at 10–30 day lead times, an interval that is generally considered as a predictability gap. Our results demonstrate the potential of leveraging the predictability of intraseasonal oscillations to improve extended-range forecasts; more generally, they point towards a future of combining dynamical and data-driven forecasts for Earth system prediction.

How to cite: Bach, E., Krishnamurthy, V., Shukla, J., Mote, S., Sharma, A. S., Kalnay, E., and Ghil, M.: Improved subseasonal prediction of South Asian monsoon rainfall using data-driven forecasts of oscillatory modes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10243, https://doi.org/10.5194/egusphere-egu23-10243, 2023.

Extended medium-range prediction targets a period of up to 30 days, which is a longer period than medium-range (up to 15 days) and shorter than seasonal (up to 3 months) forecast. The atmospheric response to the initial condition significantly impacts predictability in medium-range prediction while ocean response which is a slower change compared to the atmosphere is also an important factor in extended medium-range prediction. Thus, it is important to consider not only initial forcing but also air-sea interaction containing ocean response in extended medium-range prediction. The interactions in the earth system model can be considered among the atmosphere, ocean, sea-ice, and ocean wave by coupling of each components. The Korean Integrated Model (KIM) system, which is a global atmospheric forecast model, was developed by the Korea Institute of Atmospheric Prediction Systems. Recently, the ocean and sea-ice model components have been coupled with the KIM atmosphere system, and continuous efforts are being made to improve its performance. The air-sea interaction in an atmosphere-ocean coupled system can be considered by exchanging the variables that require interaction between components with a coupler. The bulk type exchange method basically transfers state variables such as temperature, pressure, and wind, which are used to get flux variables that contain interacting information among the atmosphere, ocean, and sea-ice. The bulk method is simple but the energy budget at the interface among the model components may become inconsistent due to the use of different formulas during calculation of the flux variables. In this study, exchange variables are changed by replacing atmospheric state variables with flux and momentum variables, which are the final form used in the ocean model. It is found that the corrected flux and momentum of the ocean surface resulting from the flux type exchange method change the ocean structure, particularly over the low latitude region. The atmosphere reacts to the changed ocean state, affecting not only the lower atmosphere but also the upper atmosphere. The results show that the flux type variable exchange method has advantages for considering air-sea interaction, which would improve the performance of extended medium-range weather forecast compared to the bulk type exchange method.

Keywords: extended medium-range forecast, coupled model, air-sea interaction, bulk type method

Acknowledgement

This work was funded by the Korea Meteorological Administration Research and Development Program under Grant KMI2022-01210.

How to cite: Yeo, N., Chang, E.-C., Song, H., Park, J., Lee, E., and Koo, M.-S.: Impact of flux type variable exchange method in the atmosphere-ocean coupled version of the Korean Integrated Model (KIM) system for extended medium-range weather forecast, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11267, https://doi.org/10.5194/egusphere-egu23-11267, 2023.

Rain-fed agriculture constitutes more than 95 % of cropland in Germany. It depends heavily on rainfall patterns and the water storage capacities of top soil layers. Intense summer droughts with long-lasting lack of precipitation leads to yield loss in wheat, corn and sugar beet production in the last years 2018, 2019, 2020 and 2022. Hence, these drought events increase the requirement of long-range forecasts for precipitation and soil moisture, which could provide useful predictions for agricultural applications.

Here a coupled modelling attempt is presented, that combines the extended-range ENS-forecasts from the European Centre for Medium-Range Weather Forecasts (ECWMF) with the soil-vegetation-atmosphere-transfer (SVAT) impact model AMBAV to simulate the top soil moisture for subseasonal forecasts on a downscaled 5x5 km grid in Germany. A quality assessment of forecast ensemble means from July 2022 to November 2022 has been done with the corresponding hindcasts for the preceding 20 years. The mean squared error skill score (MSESS) of weekly averages reveals a significant forecast skill up to 4-6 weeks for soil moisture in the upper 60 cm in comparison to an AMBAV analysis run based on gridded weather station data. In contrast, the precipitation forecast skill is much lower and achieve only adequate forecast skill with lead times up to two weeks. Due to the low variability and persistence of soil moisture values, it is proposed, that this storage variable is well suited for climate services like agricultural drought predicting systems on subseasonal time scales. It could offer guidance with sufficient reliability for medium-term management adjustments like irrigation planning or reduced fertilizer usage in case of expected severe drought periods. Overall, the results of this study show the potential of subseasonal soil moisture forecasts for agricultural applications. Further research is needed to verify these findings and to extend the forecast analysis period to the entire year. Then the impact modelling system might contribute to the adaptation of agriculture to climate change in Germany.

How to cite: Leppelt, T.: Quality assessment of subseasonal soil moisture forecasts for agricultural applications in Germany, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11833, https://doi.org/10.5194/egusphere-egu23-11833, 2023.

Current sub-seasonal prediction systems are traditionally based on models developed for numerical weather prediction. We present a different approach wherein we develop a sub-seasonal prediction system using a coupled Earth system model, the Max-Planck-Institute Earth system model (MPI-ESM), developed primarily for the use in climate prediction. We present results from initialized sub-seasonal reforecasts for the time period 1993-2017 from a 1st generation (CMIP5) seasonal-turned-sub-seasonal prediction system based on MPI-ESM including different components of the Earth system: atmosphere, land surface, ocean, and marine ecosystems. With our system we find (1) that atmospheric variables can be predicted with a quality and prediction horizon similar to what is found within the range of current sub-seasonal to seasonal prediction systems, (2) that extreme events as diverse as heatwaves over land, storm severity over Europe, and sudden stratospheric warmings can be skilfully predicted one to a few weeks ahead, (3) that sea surface temperatures can be skilfully predicted in the majority of large marine ecosystems for several weeks ahead, and (4) that sea ice area in the majority of Arctic seas can be skillfully predicted several weeks ahead. Our findings indicate that a coupled Earth system model like MPI-ESM can already be seamlessly used for sub-seasonal to seasonal (to decadal) climate predictions of different domains of the Earth system. Ultimately these results ask for the seamless approach to be embedded into the development of future coupled Earth system models.

How to cite: Koul, V., Brune, S., Febre, C., Domeisen, D. I. V., and Baehr, J.: A sub-seasonal to seasonal prediction system with MPI-ESM, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12537, https://doi.org/10.5194/egusphere-egu23-12537, 2023.

Combining ensemble forecasts from several models has been shown to improve the skill of S2S predictions. One of the most used method for such combination is the “pooled ensemble” method, i.e. the concatenation of the ensemble members from the different models. The members of the new multi-model ensemble can simply have the same weights or be given different weights based on the skills of the models. If one sees the ensemble forecasts as discrete probability distributions, then the “pooled ensemble” is their (weighted-)barycenter with respect to the L2 distance.
Here, we investigate whether a different metric when computing the barycenter may help improve the skill of S2S predictions. We consider in this work a second barycenter with respect to the Wasserstein distance. This distance is defined as the cost of the optimal transport between two distributions and has interesting properties in the distribution space, such as the possibility to preserve the temporal consistency of the ensemble members.
We compare the L2 and Wasserstein barycenters for the combination of two models from the S2S database, namely ECMWF and NCEP. Their performances are evaluated for the weekly 2m-temperature over seven winters in Europe (land) in terms of different scores. The weights of the models in the barycenters are estimated from the data using grid search with cross-validation. We show that the estimation of these weights is critical as it greatly impacts the score of the barycenters. Although the NCEP ensemble generally has poorer skills than the ECMWF one, the barycenter ensembles are able to improve on both single-model ensembles (although not for all scores). At the end, the best ensemble depends on the score and on the location. These results constitute a promising first step before implementing this methodology with more than two ensembles, and ensembles having less contrasting skills.

How to cite: Le Coz, C., Tantet, A., Flamary, R., and Plougonven, R.: Optimal transport for the multi-model combination of sub-seasonal ensemble forecasts, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13445, https://doi.org/10.5194/egusphere-egu23-13445, 2023.

Tropical Cyclone (hereafter, TC), a most destructive weather phenomenon that causes enormous socio-economic damage, occurs around 25 times every year in the western North Pacific, of which Korea is directly or indirectly affected by about 3 to 4 TCs every year. Even if it is affected by a small number of TCs, the damage could be unimaginably large. To preemptively prepare and respond to TCs, predictability on the sub-seasonal to seasonal (S2S) time-scale, over two weeks to two months is being emphasized. In this study, the characteristics of TCs in sub-seasonal forecasting with the Global Seasonal Forecast System 5 (GloSea5) of the Korea Meteorological Administration (KMA) were assessed over the western North Pacific (WNP). The predictability of GloSea5 was examined for its ability to reproduce observed TC climatology as well as changes in TC genesis with the El Niño-Southern Oscillation (ENSO) and a 1998/1999 climate regime shift. GloSea5 showed skilful performance in simulating the frequency and genesis spatial distribution of TCs in climatology and both extreme ENSO phases. Synoptic fields related to TC genesis were also reasonably captured, despite some systematic biases in those. GloSea5 performed well in terms of characteristics of changes in TC genesis due to the climate regime shift. However, there were biases in TC frequency before the regime shift and in changes in TC-related environmental fields. This study implies that GloSea5, which has a good predictive skill for TC genesis over the WNP, can be used as an operational model for sub-seasonal TC forecasting, although it requires continuous improvements to reduce systematic errors

How to cite: Kim, T., Kim, E., Lee, M., Cha, D.-H., Lee, S.-M., Lee, J., and Boo, K.-O.: Characteristics of Tropical cyclones in sub-seasonal forecasting with GloSea5: Predictability in extreme ENSO phases and a climate regime shift, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14682, https://doi.org/10.5194/egusphere-egu23-14682, 2023.

Climate change is causing disruptions in Earth's weather patterns, leading to an increase in the frequency and severity of extreme weather events such as droughts, floods, frost, and heatwaves. These events can impact food production and lead to challenges in meeting the food needs of a growing population. Previous research has documented the role of temperature and precipitation during the growing season in explaining crop yield variability. For example, droughts and extreme heat can reduce cereal production by 9-10%.

Current crop yield models use only a few meteorological variables to represent weather conditions. However, weather patterns or weather regimes, (i.e., persistent, and recurrent flow patterns of the large-scale atmospheric circulation) can provide a more comprehensive view of weather conditions, and can be used to predict and characterise extreme weather events and explain crop yield variation.

In this study, we first conducted a literature review to examine the links between extreme weather events, such as heat waves, and droughts with weather patterns and regimes. One of the main findings of that review was the need to define what extreme weather is in the context of agriculture. The new definition is based on studies that identified optimal and terminal weather conditions for winter wheat at specific phenological stages. Using this definition of extreme weather, we analyse historic yields in East Anglia, UK, forming statistically based relationships between low yield years with a set of classified weather patterns from the UK Met Office. We focused on the weather patterns frequency of occurrence and persistence with additional consideration given to potential microclimates as we compare the effects weather patterns have on a specific farm with a long-term data set to the effects of the larger region. Preliminary analyses shows that a small number of these weather patterns are associated with high impact weather events that cause yield limiting conditions or physical damage to the crop such as wind lodging.

It is hoped that further research will lead to the development of a next-generation crop yield variation model taking into account the weather patterns, which can provide longer-term predictions of regional crop yield variability to help agri-businesses, crop insurers and farmers to facilitate decision making, respond effectively to regional and global crop production shocks and food price spikes, and develop adaptation strategies to reduce the potential impact of extreme weather events.

How to cite: Knight, C., Khouakhi, A., and Waine, T.: Investigating the Role of Weather Patterns in Crop Yield Variability and Predictability, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15043, https://doi.org/10.5194/egusphere-egu23-15043, 2023.

Unprecedented heavy rainfall reaches the warming Earth more frequently, creating the need for effective risk-warning alerts that utilize subseasonal-to-seasonal (S2S) forecasting to integrate information from nowcasting, weather, and seasonal predictions. A record-breaking flooding event occurred in Zhengzhou, Henan Province of China during 17–23 July 2021, causing 398 total of deaths and vast economic losses.

A number of studies have shown this super severe heavy flooding occurred under the background of multiscale circulation interactions and the impacts of remote tropical cyclones. Here, we evaluated the predictability of this extreme rainfall event and the impacts of tropical cyclones (TCs) using subseasonal-to-seasonal (S2S) operational models. Most S2S models can reasonably predict the wet-in-north and dry-in-south monthly rainfall pattern over China in July. Only four models captured the location, probability, and sudden intensification of the Zhengzhou rainfall extremes in advance of one week, largely due to their reasonable prediction of the variability of the western North Pacific subtropical high in mid-latitudes. Although the chance of exceeding the new record daily rainfall is only approx. 0.7% in mid-late July, they provide a high probability of this heavy weekly rainfall one week in advance. However, the S2S models still underestimated the super extremeness of this event. The prediction discrepancies came from the poor predictability of Typhoon IN-FA and its impact on the daily evolution of the extreme rainfall event, even within a few days forecast lead. Compared with the observation, the prediction bias of tropical disturbance changed the environmental monsoon airflow to induce the earlier warning of rainfall extremes prior to the formation of IN-FA. After the formation of IN-FA, the prediction bias of the typhoon’s moving speed distorted the typhoon location, which incorrectly predicted the moisture convergence center and underestimated their remote impacts on this heavy rainfall event. Future research should improve our awareness of the challenges that remain in the S2S forecasts.

How to cite: Yan, Y., Zhu, C., and Liu, B.: Subseasonal prediction of the July 2021 extreme rainfall event over Henan China in S2S forecasting systems, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15077, https://doi.org/10.5194/egusphere-egu23-15077, 2023.

Stakeholders in all socio-economic sectors require reliable forecasts at multiple timescales as part of their decision-making processes. Although basing decisions mostly on a particular timescale (e.g., weather, subseasonal, seasonal) is the present status quo, this approach tends to lead to missing opportunities for more comprehensive risk-management systems (Goddard et al. 2014).

While today a variety of forecasts are produced targeting distinct timescales in a routine way, these products are generally presented to the users in different websites and bulletins, often without an assessment of how consistent the predictions are across timescales. Since different models and strategies are used at different timescales by both national and international seasonal and subseasonal forecasting centers (Kirtman et al. 2014, Kirtman et al. 2017, Vitart et al. 2017), and skill is different at those timescales, it is key to guarantee that a physically consistent “bridging” between the forecasts exists, and that the cross-timescale predictions are overall skilful and actionable, so decision makers can conduct their work.

Here, we propose and explore a new methodology –that we call the X_it (“cross-it”) operator– based on the Liang-Kleeman information flow (e.g., Tawia Hagan et al. 2019) and wavelet spectra and entropy (e.g., Zunino et al. 2007), to “bridge” forecasts at different timescales in a smooth and physically-consistent manner.

In summary, the X_it operator (1) conducts a wavelet spectral analysis (e.g., Ng and Chan 2013, Zunino et al. 2007) and (2) a non-stationary time-frequency causality analysis (e.g., Tawia Hagan et al. 2019, Liang 2015) on forecasts at different timescales to assess cross-timescale coherence and physical consistency in terms of various sources of predictability. In principle, the approach permits to identify which “intrinsic” periods/scales (i) in the timescale continuum (t) are more suitable for the bridging to occur, and/or which ones can produce more skillful forecasts, by pointing to particular target times—i.e., potential windows of opportunity (Mariotti et al. 2020)—in the forecast period where wavelet entropy (uncertainty) is lower.

While the first component of the X_it operator, i.e., the wavelet spectral and entropy analysis (Zunino et al. 2007), is designed to identify the optimal time-frequency bands for cross-timescale bridging, the fact that two forecast systems (e.g., a subseasonal and a seasonal) exhibit significant wavelet coherence does not imply that bridging those systems will provide physically-consistent predictions. The second component of the Xit operator, i.e., the non-stationary causality analysis (Tawia Hagan et al. 2019), is thus designed to assess physical consistency of the bridging by analyzing the causal link between different climate drivers (acting at different timescales) and the forecast variable of interest.

How to cite: Muñoz, Á. G., Doblas-Reyes, F., DiSera, L., Donat, M., González-Reviriego, N., Soret, A., Terrado, M., and Torralba, V.: Hunting for “Windows of Opportunity” in Forecasts Across Timescales? Cross it, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15594, https://doi.org/10.5194/egusphere-egu23-15594, 2023.

Subseasonal forecasting is forecasting of the weather parameters, mainly temperature and precipitation, two weeks to two months in advance. Sub-seasonal variability accounts for a substantial portion of the summer rainfall over India. Prediction of sub-seasonal climate is of immense societal importance in agriculture planning, water management, emergency planning, etc. Using various weather parameters and ECMWF dynamical model forecasts as predictors, this study tries to investigate the weekly forecast of temperature and precipitation at 2-week, 3-week, and 4-week forecast horizon over India using a computationally inexpensive machine learning model-MultiLLR, which prunes out irrelevant predictors and integrates remaining predictors linearly for each target date. The model’s predictions calculated over the years 2019-2022 are as skilful as IMD’s Extended Range Forecasting System (ERFS). The skill of the model is better in the coastal region than in the inland part of India.

How to cite: Jadhav, P., Op, S., and Thelliyil, S.: Subseasonal forecasting of temperature and precipitation over India using a machine learning approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16178, https://doi.org/10.5194/egusphere-egu23-16178, 2023.

Beginning July 2020, the Niño 3.4 index crossed below the threshold to La Niña conditions and remained below a -0.4 sea surface temperature anomaly through the spring of 2023, impacting agriculture, livelihoods, and communities around the world. What caused this prolonged La Niña event and why was it sustained? How did the interaction between the different modes of climate variability influence the event? The internal dynamics of ENSO, the Indian Ocean Dipole, and the Madden-Julian Oscillation are studied here through a non-linear approach utilizing compositing techniques and both linear and non-linear wave superposition to identify what caused and prolonged the 2020-2023 La Niña event.

How to cite: DiSera, L. and Muñoz, Á. G.: The 2020-2023 La Niña: Did Cross-timescale Interference Fuel this Multi-year Event?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16412, https://doi.org/10.5194/egusphere-egu23-16412, 2023.

Changes of tropical wave-modes due to climate change will impact the predictability of the tropical atmosphere, and may impact extratropical weather as well. The simulations of convectively coupled equatorial waves and the Madden-Julian Oscillation (MJO) are considered in 13 state-of-the-art models from phase 6 of the Coupled Model Intercomparison Project (CMIP6).  We look at the wave-modes using frequency-wavenumber power spectra of the models and observations for Outgoing Longwave Radiation and zonal winds at 250 hPa. We analyze the spectra of the historical simulations and end of 21st century projections for the SSP245 and SSP585 scenarios.  The models simulate a spectrum quantitatively resembling that observed, though systematic biases exist. Most models project a future increase in power spectra for the MJO, while nearly all project a robust increase for Kelvin waves (KW) and weaker power values for most other wavenumber-frequency combinations. Models with a more realistic MJO in their control climate tend to simulate a stronger future intensification. In addition to strengthening, KW also shift toward higher phase speeds. The net effect is a more organized tropical circulation on intraseasonal timescales, which may contribute to higher intrinsic predictability in the tropics and to stronger teleconnections in the extratropics. In addition, those projected changes might be due to extratropical forcings, and more specifically due to changes in the North Pacific subtropical jet.

How to cite: Bartana, H., Garfinkel, C., Shamir, O., and Rao, J.: Projected Future Changes in Equatorial Wave Spectrum in CMIP6, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16731, https://doi.org/10.5194/egusphere-egu23-16731, 2023.

The global climatic pattern is governed by the dominant mode of variability in the tropics and the extratropic and their interaction. The extratropical atmosphere is much more vigorous than the tropics owing to sharp meridional temperature gradients in the mid-latitude. Especially on the decadal timescales, large signals are seen over the extratropical region than in the tropics. Here, we propose that during boreal spring, the second leading mode of climate variability in the Southern Hemisphere, has a decadal pattern. This mode is independent of the Southern Annular Mode (SAM), which represents the most dominant mode of climate variability in the Southern Hemisphere. The boreal spring climate of the Southern Hemisphere interacts with the tropics and significantly impacts the global climate, which is reflected in the global Monsoon rainfall. During the positive phase of the decadal mode, the global Monsoon rainfall is coherently suppressed. We propose a new finding highlighting that the Southern Hemisphere's extratropical forcing can significantly impact the tropical Pacific through subtropical pathways on the decadal to multidecadal timescale. The impact of such decadal climate variability is enormous and global and can add a new paradigm to the pursuit of improving decadal predictions of the global climate.

How to cite: Srivastava, S., Chakraborty, A., and Murtugudde, R.: Boreal Spring Southern Hemisphere Climate Mode and Global Monsoon, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-604, https://doi.org/10.5194/egusphere-egu23-604, 2023.

As a dominant pattern of the North Pacific sea surface temperature decadal variability, the Pacific Decadal Oscillation (PDO) has remarkable influences on the marine and terrestrial ecosystems. However, the PDO is highly unpredictable. Here, we assess the performance of the Coupled Model Intercomparison Project Phase 6 (CMIP6) models in simulating the PDO, with an emphasis on the evaluation of CMIP6 models in reproducing a recently detected early warning signal based on climate network analysis for the PDO regime shift. Results show that the skill of CMIP6 historical simulations remains at a low level, with a skill limited in reproducing PDO’s spatial pattern and nearly no skill in reproducing the PDO index. However, if the warning signal for the PDO regime shift by climate network analysis is considered as a test-bed, we find that even in historical simulations, a few models can represent the corresponding relationship between the warning signal and the PDO regime shift, regardless of the chronological accuracy. By further conducting initialization, the performance of the model simulations is improved according to the evaluation of the hindcasts from two ensemble members of the Decadal Climate Prediction Project (NorCPM1 and BCC-CSM2-MR). Particularly, we find that the NorCPM1 model can capture the early warning signals for the late-1970s and late-1990s regime shifts 5–7 years in advance, indicating that the early warning sig- nal somewhat can be captured by some CMIP6 models. A further investigation on the underlying mechanisms of the early warning signal would be crucial for the improvement of model simulations in the North Pacific.

How to cite: Ma, Y.: On the Pacific Decadal Oscillation Simulations in CMIP6 Models: A New Test‐Bed from Climate Network Analysis, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5325, https://doi.org/10.5194/egusphere-egu23-5325, 2023.

Climate extremes can impact societies in various ways: from nuances in daily lives to full humanitarian crises. Droughts  are usually slow onset extremes but can be highly disruptive and affect millions of people every year. Warm temperature extremes (e.g. heat waves) can exacerbate droughts and their impacts and trigger a faster drought evolution. Combined drought and heat waves can lead to devastating consequences. For example, 2022 was a very active year in terms of drought or combined drought and heat waves, affecting particularly hard several regions of the world (e.g. Europe, China, southern South America and East Africa). In a context of risk management and civil protection, the use of operationally available seasonal climate forecasts can provide actionable information to reduce the risks and the impacts of these events on societies with different levels of development and adaptive capacities. 

Within the Copernicus Emergency Management Service (CEMS), the European and Global Drought Observatories (EDO and GDO, respectively) provide real time drought and temperature extreme monitoring products freely available and displayed through two dedicated web services. Recent efforts have been targeting the optimal integration and use of multi-system forecasting products to enhance the early warning component of the service. This contribution provides an overview of first results in terms of  initial multi-model skill assessment of forecasts available through the Copernicus Climate Change Service (C3S). It also discusses future avenues to improve skill in regions with limited predictability, for example by applying physically-based sampling techniques.    

How to cite: Acosta Navarro, J. C. and Toreti, A.: Seasonal forecasting of drought and temperature extremes as part of the Copernicus Emergency Management Service (CEMS), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5602, https://doi.org/10.5194/egusphere-egu23-5602, 2023.

The European North West shelf seas (NWS) support economic and environmental interests of several adjacent populous countries. Forecasts of physical marine variables on the NWS for upcoming months – an important decision-making timescale – would be useful for many industries. However, currently there is no operational seasonal forecasting product deemed sufficient for capturing the high variability associated with shallow, dynamic shelf waters. Here, we identify the dominant sources of seasonal predictability on the shelf and quantify the extent to which empirical persistence relationships can produce skilful seasonal forecasts of the NWS at the lowest level complexity. We find that relatively skilful forecasts of the typically well-mixed Winter and Spring seasons are achievable via persistence methods at a one-month lead time. In addition, incorporating observed climate modes of variability, such as the North Atlantic Oscillation (NAO), can significantly boost persistence for some locations and seasons, but this is dependent on the strength of the climate mode index. However, even where high persistence skill is demonstrated, there are sizeable regions exhibiting poor predictability and skilful persistence forecasts are typically limited to ≈ one-month lead times. Summer and Autumn forecasts are generally less skilful owing largely to the effects of seasonal stratification which emphasises the influence of atmospheric variability on sea surface conditions. As such, we also begin incorporating knowledge of future atmospheric conditions to forecasting strategies. We assess the ability of an existing global coupled ocean-atmosphere seasonal forecasting system to exceed persistence skill and highlight areas where additional downscaling efforts may be needed.

How to cite: Atkins, J., Tinker, J., Graham, J., Scaife, A., and Halloran, P.: Seasonal forecasting of the European North West Shelf: Quantifying the persistence of the physical marine environment, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6000, https://doi.org/10.5194/egusphere-egu23-6000, 2023.

Current global climate models typically fail to fully resolve mesoscale ocean features (with length scales on the order of 10 km), such as the western boundary currents, potentially limiting climate predictability over decadal timescales. This study incorporates high-resolution eddy-resolving ocean (HR: 0.1°) in a suite of CESM model experiments that capture these important mesoscale ocean features with increased fidelity. Compared with eddy-parametrized ocean (LR: 1°) experiments, HR experiments show more realistic climatology and variability of sea surface temperature (SST) over the western boundary currents and eddy-rich regions. In the North Atlantic, the inclusion of mesoscale ocean processes produces a more realistic Gulf Stream and improves both localized rainfall patterns and large-scale teleconnections. We identify enhanced decadal SST predictability in HR over the western North Atlantic, which can be explained by the strong vertical connectivity between SST and sub-surface ocean temperature. SST is better connected with slower processes deep down in HR, making SST more persistent (and predictable). Moreover, we detect a better representation of the air-sea interactions between SST and low-level atmosphere over the Gulf Stream, thus improving low-frequency rainfall variations and extremes over the Southeast US. The results further imply that high-resolution GCMs with increased ocean model resolution may be needed in future climate prediction systems.

How to cite: Zhang, W., Kirtman, B., Siqueira, L., and Clement, A.: Decadal Climate Variability and Predictability with a High-resolution Eddy-resolving Model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7676, https://doi.org/10.5194/egusphere-egu23-7676, 2023.

Earth system predictability on decadal timescales can arise from both low frequency internal variability as well as from anthropogenically forced long-term changes. However, on these timescales, the chaotic nature of the climate system makes skillful predictions difficult to achieve even if we include information from climate change projections. Furthermore, it is difficult to separate the contributions from internal variability and external forcing to predictability. One way to improve skill is through identifying and harnessing initial conditions with more predictable evolution, so-called state-dependent predictability. We explore a neural network approach to identify these opportunistic initial states in the CESM2 large ensemble and subsequently explore how predictability may manifest in a future climate, influenced by both forced warming and internal variability. We use an interpretable neural network to demonstrate that internal variability will continue to play an important role in future climate predictions, especially for states of increased predictability.

How to cite: Gordon, E. and Barnes, E.: An interpretable neural network approach to identifying sources of predictability in the future climate, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8000, https://doi.org/10.5194/egusphere-egu23-8000, 2023.

Decadal prediction represents a source of near-term climate information that has the potential to support climate-related decisions in key socio-economic sectors that are influenced by climate variability and change. While the research to illustrate the ability of decadal predictions in forecasting the varying climate conditions on a multi-annual timescale is rapidly evolving, the development of climate services based on such forecasts is still in its early stages. This study showcases the potential value of decadal predictions in the development of climate services. We summarize the lessons learnt from coproducing a forecast product that provides tailored and user-friendly information about multi-year drought conditions for the coming five years over global wheat harvesting regions. The interaction between the user and climate service provider that was established at an early stage and lasted throughout the forecast product development process proved fundamental to provide useful and ultimately actionable information to the stakeholders concerned with food production and security. This study also provides insights on the potential reasons behind the delayed entry of decadal predictions in the climate services discourse and practice, which were obtained from surveying climate scientists and discussing with decadal prediction experts.

How to cite: Solaraju-Murali, B., Bojovic, D., Gonzalez-Reviriego, N., Nicodemou, A., Terrado, M., Caron, L.-P., and Doblas-Reyes, F. J.: Better late than never: arrival of decadal predictions to the climate services arena, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8296, https://doi.org/10.5194/egusphere-egu23-8296, 2023.

We introduce a simple data assimilation approach applied to the coupled global climate model EC-Earth3.3.1, aiming at producing initial conditions for decadal climate hindcasts and forecasts. We rely on a small selection of assimilated variables, which are available in a consistent manner for a long period, providing good spatial coverage for large parts of the globe, that is sea-surface temperatures (SST) and near-surface winds.

Given that these variables play a role directly at or very close to the ocean-atmosphere interface, we assume a comparably strong cross-component impact of the data assimilation. Starting from five different free-running CMIP6-historical simulations in 1900, we first apply surface restoring in the model’s ocean component towards monthly means of HadISST1. After integrating this five-member ensemble with only assimilating SST for the period 1900-1949, we start additionally assimilating (nudging) 6-hourly near-surface winds (vorticity and divergence) taken from the ERA5 reanalysis from 1950 onwards. To mitigate the risk of model drifts after initializing the decadal predictions and to account for known instationary biases of the model, we assimilate anomalies of all variables that are calculated based on a 30-year running mean.

By assimilating near-surface data over several decades before entering the actual period targeted by the decadal hindcasts/forecasts for CMIP6-DCPP, we expect the coupled model to be able to ingest a significant share of observed climate evolution also in deeper ocean layers. This would then potentially serve as a source of predictive skill on interannual-to-decadal timescales.

We show that the presented assimilation approach is able to force the coupled model’s evolution well in phase with observed climate variability, positively affecting not only near-surface levels of the atmosphere and ocean but also deeper layers of the ocean and higher levels of the atmosphere as well as Arctic sea-ice variability. However, we also present certain problematic features of our approach. Two examples are significantly strengthened low-frequency variability of the AMOC and a wind bias resulting into generally reduced evaporation over ocean areas.

How to cite: Kruschke, T., Karami, M. P., Docquier, D., Schenk, F., Fuentes Franco, R., Willén, U., Wang, S., Wyser, K., Fladrich, U., and Koenigk, T.: A simple coupled assimilation approach for improved initialization of decadal climate predictions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8750, https://doi.org/10.5194/egusphere-egu23-8750, 2023.

The interdisciplinary research project "BayTreeNet" examines the reactions of forest ecosystems to climate dynamics. To establish a relationship between tree growth and climate, it is important to know that in the mid-latitudes, local climate phenomena often show a strong dependence on the large-scale climate weather types (WT), which significantly determine the climate of a region through frequency and intensity. Different WT show various weather conditions at different locations, especially in the topographically diverse region of Bavaria. The meaning of every WT is the physical basis for the climate-growth relationships established in the dendroecology sub-project to investigate the response of forests to individual WT at different forest sites. Complementary steps allow interpretation of results for the past (20th century) and projection into the future (21st century). One hypothesis is that forest sites in Bavaria are affected by a significant influence of climate change in the 21st century and the associated change in WT.

The automated classification of large-scale weather patterns is presented by Self-Organizing-Maps (SOM) developed by Kohonen, which enables visualization and reduction of high-dimensional data. The poster presents the SOM-setting which was used to classify the WT and the results of past environmental conditions (1990-2019) for different WT in Europe based on ERA5 data. Morover, it shows a future projection until 2100 for European WT and their respective environmental conditions. The projections are based on a novel GCM selection technique for two scenarios (ssp1-2.6 and ssp5-8.5) to obtain a range of the most likely conditions.

How to cite: Wehrmann, S. and Mölg, T.: GCM-based future projections of European weather types obtained by Self‑Organizing-Maps and a novel GCM selection technique, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8934, https://doi.org/10.5194/egusphere-egu23-8934, 2023.

A considerable part of the skill in decadal forecasts often come from the forcings which are present in both initialized and un-initialized model experiments. This makes the added value from initialization difficult to assess. We investigate statistical tests to quantify if initialized forecasts provide skill over the un-initialized experiments. We consider three correlation based statistics previous used in the literature. The distributions of these statistics under the null-hypothesis that initialization has no added values are calculated by a surrogate data method. We present some simple examples and study the statistical power of the tests. We find that there can be large differences in both the values and the power for the different statistics. In general the simple statistic defined as the difference between the skill of the initialized and uninitialized experiments behaves best. However, for all statistics the risk of rejecting the true null-hypothesis is too high compared to the nominal value.

We compare the three tests on initialized decadal predictions (hindcasts) of near-surface temperature performed with a climate model and find evidence for a significant effect of initializations for small lead-times. In contrast, we find only little evidence for a significant effect of initializations for lead-times larger than 3 years when the experience from the simple experiments is included in the estimation.

How to cite: Christiansen, B., Yang, S., and Matte, D.: Estimating the significance of the added skill from initializations: The case of decadal predictions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9520, https://doi.org/10.5194/egusphere-egu23-9520, 2023.

The monthly anomaly sea surface temperature field over the global ocean exhibit probabilistic dependencies between remote points and lagged times, which are explained eventually by some oceanic or atmospheric bridge of information transfer. Despite much of the bivariate SST dependencies appear to be linear, others are characterized by robust and statistically significant nonlinear correlations. In order to enhance that, we present a general method of extracting bivariate (X,Y) dependencies, seeking for pairs of polynomials P(X) and Q(Y) which are maximally correlated. The method relies on a Canonical correlation Analysis (CCA) between sets of standardized monomials of X and Y, up to a certain (low) degree (e.g. 4). Polynomial coefficients are obtained from the leading CCA eigenvector. Polynomials are calibrated and validated over independent periods, being afterwards subjected to marginal Gaussian anamorphoses. The bivariate non-Gaussianity in the space of marginally Gaussianized polynomials remains residual because of the correlation concentration and maximization. Consequently, the bivariate Gaussian pdf or in alternative, a copula pdf in the space of maximally correlated polynomials can accurately approximate the bivariate dependency. That probabilistic model is then used to determine conditional pdfs, cdfs and probabilities of extremes.

The method is applied to various (X,Y) pairs. In the first example, X is an optimized polynomial of the El-Niño 3.4 index while Y is that index lagged to the future. For lags between 6 and 18 months, the nonlinear El-Niño forecast clearly surpasses the linear one, contributing to lower the El-Niño seasonal predictability barrier. In the second example, we relate El-Niño (X) with the lagged Atlantic multidecadal oscillation index (Y). Nonlinear, robust correlations appear, both for positive and negative lags up to 5 years putting in evidence Pacific-Atlantic basin oceanic teleconnections.

The above probabilistic (polynomial based) model appears to be a good candidate tool for the statistical (seasonal up to decadal) forecast of regime probabilities (e.g. dry/wet) and extremes, given certain antecedent precursors.

This work was funded by the Portuguese Fundação para a Ciência e a Tecnologia (FCT) I.P./MCTES through national funds (PIDDAC) – UIDB/50019/2020- IDL and the project JPIOCEANS/0001/2019 (ROADMAP: ’The Role of ocean dynamics and Ocean–Atmosphere interactions in Driving cliMAte variations and future Projections of impact–relevant extreme events’). Acknowledgements are also due to the International Meteorological Institute (IMI) at Stockholm University.

How to cite: Pires, C. and Hannachi, A.: Probabilistic nonlinear lagged teleconnections of the sea surface temperature field, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9986, https://doi.org/10.5194/egusphere-egu23-9986, 2023.

How to cite: Drews, A., Schmith, T., Yang, S., Olsen, S., Tian, T., Devilliers, M., Wang, Y., and Keenlyside, N.: Role of the subpolar North Atlantic region in skillful climate predictions for high northern latitudes: A pacemaker experiment, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13375, https://doi.org/10.5194/egusphere-egu23-13375, 2023.

For several years the Met Office has produced a seasonal outlook for the UK every month, which is issued to the UK Government and contingency planners.  The outlook gives predictions of the probability of having average, low, or high seasonal mean UK temperature and precipitation for the coming three-months.  In recent years, there has been increasing demand from sectors such as energy and insurance to include similar probabilistic predictions of UK wind speed: both for the seasonal mean and for measures of extreme winds such as storm numbers.  In this presentation we show the skill of the Met Office’s GloSea system in predicting seasonal (three-month average) UK mean wind and a measure of UK storminess throughout the year, and discuss the drivers of predictability.  Skill in predicting the UK mean wind speed and storminess peaks in winter (December–February), owing to predictability of the North Atlantic oscillation.  In summer (June–August), there is evidence that a significant proportion of variability in UK winds is driven by a Rossby wave train which the model has little skill in predicting. Nevertheless there are signs that the wave is potentially predictable and skill may be improved by reducing model errors.

How to cite: Lockwood, J., Stringer, N., Hodge, K., Bett, P., Knight, J., Smith, D., Scaife, A., Patterson, M., Dunstone, N., and Thornton, H.: Seasonal prediction of UK mean and extreme winds, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13639, https://doi.org/10.5194/egusphere-egu23-13639, 2023.

Accurate predictions of climate variations at the decadal timescale are of great interest for decision-making, planning and adaptation strategies for different socio-economic sectors. Notably, decadal predictions have rapidly evolved during the last 15 years and are now produced operationally worldwide. The majority of the studies assessing the skill of decadal prediction systems focus on time-mean anomalies of standard meteorological variables, such as annual mean near-surface air temperature and precipitation. However, the predictability of extreme events frequency may differ substantially from the predictability of multi-year annual or seasonal means. Predicting the frequency of extreme events at different timescales is of major importance, since they are associated with severe impacts on various natural and human systems. In the current study we evaluate the capability of state-of-the-art decadal prediction systems to predict the frequency of temperature extremes in Europe. More specifically, we assess the skill of a multi-model ensemble from the Decadal Climate Prediction Project (DCPP, 163 ensemble members from 12 models in total) to forecast the number of days belonging to heatwaves episodes during summer (June–August). We find statistically significant predictive skill over Europe, except for the United Kingdom and a large part of the Scandinavian Peninsula, most of which is associated with the long-term warming trend. We are progressing with the evaluation of other statistical aspects of extreme events, including warm and cold episodes during winter, and we are also investigating whether there is predictive skill beyond that stemming from the external forcing.  

How to cite: Tsartsali, E., Athanasiadis, P., Tibaldi, S., and Gualdi, S.: Decadal predictability of European temperature extremes., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13736, https://doi.org/10.5194/egusphere-egu23-13736, 2023.

Earth system models are now regularly being used in inter-annual to decadal climate prediction. Such prediction systems based on CMIP5-generation Earth system models had demonstrated an overall positive impact of initialization, i.e. deriving initial conditions of retrospective forecasts from a separate data assimilation experiment, on decadal prediction skill. This view is now being increasingly challenged in the context of improvements both in CMIP6-generation Earth system models and CMIP6-evaluation of external forcing as well as in the context of ongoing transient climate change. In this study we re-evaluate the impact of atmospheric and oceanic initialization on decadal prediction skill of North Atlantic upper ocean heat content (0-700m) in a CMIP6-generation decadal prediction system based on the Max Planck Institute Earth system model (MPI-ESM). We compare the impact of initial conditions derived through full-field atmospheric nudging with those derived through an additional assimilation of observed oceanic temperature and salinity profiles using an ensemble Kalman filter. Our experiments suggest that assimilation of observed oceanic temperature and salinity profiles into the model reduces the warm bias in the subpolar North Atlantic heat content, and improves the modelled variability of the Atlantic meridional overturning circulation and ocean heat transport. These improvements enable a proper initialization of model variables which leads to an improved decadal prediction of surface temperatures. Our results reveal an important role of subsurface oceanic observations in decadal prediction of surface temperatures in the subpolar North Atlantic even in CMIP6-generation decadal prediction system.

How to cite: Brune, S., Koul, V., and Baehr, J.: Do oceanic observations (still) matter in initializing decadal climate predictions over the North Atlantic ocean?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13789, https://doi.org/10.5194/egusphere-egu23-13789, 2023.

Earth System Models (ESMs) are complex, highly nonlinear, multi-component systems described by large number of differential equations. They are used to study the evolution of climate and its dynamics, and to make projection of future climate at both regional and global levels – which underpins climate change impact assessments such as the IPCC report. These projections are subject to several sorts of uncertainty due to high internal variability in the system dynamics, which are usually quantified via ensembles of simulations.

Due to their multi component nature of such ESMs, the emerging dynamics also contain different temporal scales, meaning that climate ensembles come in a variety of shapes and sizes. However, our ability to run such ensembles is usually constrained by the computational resources available, as they are very expensive to run. Hence, choices on the ensemble design must be made, which conciliate the computational capability with the sort of information one is looking for.

One alternative to gain information is to use low-dimensional climate-like systems, which consists of simplified, coupled versions of atmosphere, ocean, and other components, and hence capture some of the different time scales present in ESMs. This approach allows one to run very large ensembles, and hence to explore all sorts of model uncertainty with only modest computational usage.

In this talk, we discuss this approach in detail, and illustrate its applicability with a few results. Particular attention will be given to the issues of micro and macro initial condition uncertainty, and parametric uncertainty – including external, anthropogenic-like forcing. The ability of large ensembles to constrain decadal to centennial projections will be also explored.

How to cite: de Melo Viríssimo, F. and Stainforth, D.: A low-dimensional dynamical systems approach to climate ensemble design and interpretation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14755, https://doi.org/10.5194/egusphere-egu23-14755, 2023.

Ongoing climate change makes both short- and long-term adaptation and mitigation strategies urgently needed. While many long-term climate models have been developed and investigated in recent years, little attention has been paid to short-term simulations. The first attempts to perform multi-model initialized decadal forecasts were presented in the fifth Coupled Model Intercomparison Project 5 (CMIP5). Near-term climate prediction models are new socially relevant tools to support the decision makers delivering climate adaptation solutions on an annual or decadal scale. Recent improvements in decadal models were coordinated in CMIP6 and the World Climate Research Program (WCRP) Grand Challenge on Near Term Climate Prediction, as part of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (AR6, IPCC). The Decadal Climate Prediction Project (DCPP) provides decadal climate forecasts based on advanced techniques for the reanalysis of climate data, initialization methods, ensemble generation and data analysis. The initialization allows to consider the predictability of the internal climate variability reducing the prediction errors compared to those of the long-term projections, whose simulations do not take into account the phasing between the internal variability of the model and the observations. The aim of this work is to assess the near-future climate change in the Emilia-Romagna region in northern Italy until 2031. The hydrological variables analyzed are the daily precipitation and maximum and minimum temperature. An ensemble of models, with the highest resolution available, is used to handle the uncertainty in the predictions. Initially, to assess the reliability of the selected climate models, the hindcast data of the DCPP are checked against observations. Then, the DCPP predictions are used to investigate the variability of precipitation and temperature in the near future over the investigated area. Some climate features that are referenced to have an important impact on human health and activities are evaluated, such as drought indices and heat waves.

How to cite: Todaro, V., D'Oria, M., Secci, D., Zanini, A., and Tanda, M. G.: Near term climate change in Emilia-Romagna (Italy) using CMIP6 decadal climate predictions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15829, https://doi.org/10.5194/egusphere-egu23-15829, 2023.

The polar vortex in the wintertime Northern Hemisphere can sometimes experience a dramatic breakdown after an associated warming of the stratosphere during so-called Sudden Stratospheric Warmings (SSWs). These events are known to influence the ground weather in Northern Eurasia and large parts of North America. SSWs are primarily generated by enhanced planetary waves propagating from the troposphere to the stratosphere where they decelerate the vortex and lead to its breakdown. According to the Holton-Tan mechanism, the easterly direction of equatorial stratospheric QBO (Quasi-Biennial Oscillation) winds weakens the northern polar vortex by guiding more waves poleward. Recently, we found that during easterly QBO the occurrence rate of SSWs is modulated by the geomagnetic activity. We used the aa-index which is a good proxy for the energetic electron precipitations (EEP) responsible for the indirect effect on ozone. Our model shows that the breaking of the polar vortex is very likely to occur if the geomagnetic activity is weak. On the other hand, during westerly QBO, solar irradiance modulates the SSW occurrence: more SSWs happen under high solar activity.

How to cite: Vokhmyanin, M., Asikainen, T., Salminen, A., and Mursula, K.: Seasonal forecast of the Sudden Stratospheric Warming occurrence, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16034, https://doi.org/10.5194/egusphere-egu23-16034, 2023.

The state-of-the-art climate models suffer from significant sea surface temperature (SST) biases in the tropical Indian Ocean (TIO), greatly damaging the climate prediction and projection. In this study, we investigate the multidecadal atmospheric bias teleconnections caused by the TIO SST biases and their impacts on the simulated atmospheric variability. A set of century long simulations forced with idealized SST perturbations, resembling various persistent TIO SST biases in coupled climate models, are conducted with an intermediate complexity climate model. Bias analysis is performed using the normal-mode function decomposition which can differentiate between balanced and unbalanced flow regimes across spatial scales. The results show that the long-term atmospheric circulation biases caused by the TIO SST biases have the Matsuno-Gill-type pattern in the tropics and Rossby wavetrain distribution in the extratropics, similar to the steady state response to tropical heating. The teleconnection between the tropical and extratropical biases is set up by the Rossby wavetrain emanating from the subtropics. Over 90% of the total bias energy is stored in the zonal modes k≤6, and the Kelvin modes take 50-65% of the total unbalanced bias energy. The spatial and temporal variabilities have different responses to positive SST biases. In the unbalanced regime, variability changes are confined in the tropics, but the spatial variability increases whereas the temporal variability decreases. In the balanced regime, the spatial variability generally increases in the tropics and decreases in the extratropics, whereas the temporal variability decreases globally. Variability responses in the tropics are confined in the Indo-west Pacific region, and those in the extratropics are strong in the Pacific-North America region and the Europe. In the experiment with only negative SST biases, spatial and temporal variabilities increase in both regimes. In addition, the comparison between experiments indicates that the responses of the circulation and its variability are not sensitive to the structure and location of the TIO SST biases.

How to cite: Zhao, Y.-B., Žagar, N., Lunkeit, F., and Blender, R.: Long-term atmospheric bias teleconnection and the associated spatio-temporal variability originating from the tropical Indian Ocean sea surface temperature errors, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16899, https://doi.org/10.5194/egusphere-egu23-16899, 2023.

The large data volumes available in weather forecasting make post-processing an attractive field for applying machine learning. In turn, novel statistical machine learning methods that can be used to generate uncertainty information from a deterministic forecast are of great interest to forecast users, especially given the computational cost of running high resolution ensembles. In this work, we show how one such method, a Bayesian Neural Network (BNN), can be used to post-process a single global high resolution forecast for 2m temperature. This methodology improves both the accuracy of the forecast and adds uncertainty information, by predicting the distribution of the forecast error relative to its own analysis.

Here we assess both model and data uncertainty using two different BNN approaches. In the first approach, the BNN’s parameters are defined to be distributions rather than deterministic parameters, thereby generating an ensemble of models that can be used to quantify model uncertainty. In the second approach, the BNN remains deterministic but predicts a distribution rather than a deterministic output thereby quantifying data uncertainty. Our BNN results are benchmarked against simpler statistical methods, as well as statistics from the ECMWF operational ensemble.

Finally, in order to add trustworthiness to the BNN predictions, we apply an explainable AI technique (Layerwise Relevance Propagation) so as to understand whether the variables on which the BNN bases its prediction are physically reasonable or whether it is instead learning spurious correlations.

How to cite: Clare, M., Ben Bouallegue, Z., Chantry, M., Leutbecher, M., and Haiden, T.: Combining Bayesian Neural Networks with explainable AI techniques for trustworthy probabilistic post-processing, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-946, https://doi.org/10.5194/egusphere-egu23-946, 2023.

Existing weather models are known to have poor skill over Africa, where there are regular threats of drought and floods that present significant risks to people's lives and livelihoods. Improved precipitation forecasts could help mitigate the negative effects of these extreme weather events, as well as providing significant financial benefits to the region. Building on work that successfully applied a state-of-the-art machine learning method (a conditional Generative Adversarial Network, cGAN) to postprocess precipitation forecasts in the UK, we present a novel way to improve precipitation forecasts in East Africa. We address the challenge of realistically representing tropical convective rainfall in this region, which is poorly simulated in conventional forecast models. We use a cGAN to postprocess ECMWF high resolution forecasts at 0.1 degree resolution and 6-18h lead times, using the iMERG dataset as ground truth, and investigate how well this model can correct bias, produce reliable probability distributions and create samples of rainfall with realistic spatial structure. We will also present performance in extreme rainfall events. This has the potential to enable cost effective improvements to early warning systems in the affected areas.

How to cite: Antonio, B., McRae, A., MacLeod, D., Cooper, F., Marsham, J., Aitchison, L., Palmer, T., and Watson, P.: Improving post-processing of East African precipitation forecasts using a generative machine learning model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1365, https://doi.org/10.5194/egusphere-egu23-1365, 2023.

Most Numerical Weather Prediction (NWP) systems use statistical postprocessing methods to correct for bias and underdispersion errors made by ensemble forecasting. This underdispersion leads to an underestimation of extreme events. Thus, many statistical postprocessing methods have been used to take into consideration the extremal behavior of meteorological phenomena such as precipitation. State-of-the-art techniques are based on Machine Learning combined with knowledge from Extreme Value Theory in order to improve forecasts. However, some of the best techniques do not consider the spatial dependency between locations. For example, Taillardat et al. (2019) trains a different Quantile Regression Forest at each location of interest and Rasp & Lerch (2018) uses neural networks with an embedding for the station's information in order to train a global model.
The dataset used corresponds to 3-h precipitation amounts produced by the radar-based observations of ANTILOPE and the 17-members ensemble forecast system called PEAROME. The dataset spans over the south of France with a grid resolution of 0.025 degrees. Our method uses a U-Net-like neural network in order to take into account the spatial structure of the data and the output of our model is a parameterized law at each grid point. Among the choices available in the literature, we focused on the Extended Generalized Pareto Distribution  and the truncated logistic with a point mass in 0. The model is trained by minimizing the scoring rules such as the Continuous Ranked Probability Score, the Log-Score or weighted versions of the aforementioned scoring rules. The method developed here is then compared to the raw ensemble as well as state-of-the-art techniques through scoring rules, skill scores and ROC curves.

References :

• L. Pacchiardi, R. Adewoyin, P. Dueben, and R. Dutta. Probabilistic forecasting with generative networks via scoring rule minimization. Dec. 2021. arXiv:2112.08217
  
• M. Taillardat, A.-L. Fougères, P. Naveau, and O. Mestre. Forest-based and semiparametric methods for the postprocessing of rainfall ensemble forecasting. Weather and Forecasting, 34(3):617–634, jun 2019. doi: 10.1175/waf-d-18-0149.1.

How to cite: Pic, R., Dombry, C., Taillardat, M., and Naveau, P.: U-Net based Methods for the Postprocessing of Precipitation Ensemble Forecasting, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2592, https://doi.org/10.5194/egusphere-egu23-2592, 2023.

Global seasonal weather forecasts have inherent biases compared to observational datasets over mountainous regions. This can be attributed to the model's inaccurate representation of local and global environmental processes on the Earth. In this context, the objective of this study is to assess the variation of seasonal weather forecast biases with respect to static and dynamic environmental variables over the Trentino-South Tyrol region (north-eastern Italian Alps), characterized by complex terrain.

The research employs the latest fifth-generation seasonal weather forecast system (SEAS5) dataset produced by the European Center for Medium-Range Weather Forecast (ECMWF), available at a horizontal grid resolution of 0.125° x 0.125° with 25 ensemble members in a re-forecast period from 1981 to 2016. The reference dataset is a high-resolution gridded observation (250 m x 250 m) over the region of interest. The spatiotemporal variation of monthly weather (i.e., precipitation and temperature) forecast biases over the region is inferred using several statistical indicators at observational dataset grid resolution. The static and dynamic environmental variables (i.e., respectively, terrain characteristics and large-scale atmospheric circulation indices) are used univariately to interpret their relationship with monthly weather forecast biases using the linear regression technique. A statistically significant linear relation between monthly weather forecast biases and terrain characteristics, as well as large-scale atmospheric circulation indices, has been found depending on seasonality and ensemble members.

Given significant univariate linear correlation, a simple linear bias reduction model is developed and assessed by implementing a random subsampling technique in which the regression parameters are simulated by splitting the data into calibration (70%) and validation (30%). The results reveal a reduction in the monthly weather forecast bias over the region.

This study demonstrates that the local and global environmental variables should be explicitly considered in the bias correction and downscaling of the seasonal weather forecasts over complex terrain.

How to cite: Uttarwar, S. B., Napoli, A., Avesani, D., and Majone, B.: Seasonal Weather Forecast Biases Dependence on Static and Dynamic Environmental Variables in the Alpine Region, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2628, https://doi.org/10.5194/egusphere-egu23-2628, 2023.

This work investigates several statistical tests in the context of probabilistic weather forecasting and ensemble postprocessing. The tests are commonly used for comparing predictive performance of e.g. two statistical postprocessing models.  

In the first part of the analysis a case study is conducted on temperature data consisting of observations and ensemble forecasts. The tests are applied to compare the performance of two probabilistic temperature forecasts at different stations, for different lead times, investigating several standard verification metrics to measure prediction performance. The analysis shows that the tests generally behave consistently in the context of temperature forecasts. However, for certain scenarios some tests might be be preferred over the others. In general, the combination of the original Diebold-Mariano test with the continuous ranked probability score (CRPS) to assess forecast accuracy leads to the most consistent and reliable results.

The second part of the analysis uses simulated data to investigate the general behaviour of the tests in different postprocessing scenarios as well as their size and power properties. Again, the original Diebold-Mariano test appears to perform most reliably and shows no noticeable inconsistent behaviour for different simulation settings.

How to cite: Möller, A. and Grupe, F.: Investigating properties of statistical tests for comparing predictive performance with application to probabilistic weather forecasting, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2701, https://doi.org/10.5194/egusphere-egu23-2701, 2023.

Statistical postprocessing of ensemble forecasts has become a common practice in research to correct biases and errors in calibration. Meanwhile, machine learning methods such as quantile regression forests or neural networks are often suggested as promising candidates in literature. However, interpretation of these methods is not always straightforward. 
Therefore, we propose the D-vine (drawable-vine) copula based postprocessing, where for the construction of a multivariate conditional copula the graphical D-vine model serves as building plan. The conditional copula is based on this tracetable model using bivariate copulas, which allow to describe linear as well as non-linear relationships between the response variable and its covariates. Additionally, our highly data-driven model selects the covariates based on their predictive strength and thus provides a natural variable selection mechanism, facilitating interpretability of the model. Finally, (non-crossing) quantiles from the obtained conditional distribution can be utilized as postprocessed ensemble forecasts. 
In a case study for the postprocessing of 10 m surface wind speed ensemble forecasts with 24 hour lead time we compare local and global D-vine copula based models to the zero-truncated ensemble model output statistics (tEMOS) for different sets of predictor variables at 60 surface weather stations in Germany. Furthermore, we investigate different types of training periods for both methods. We observe that the D-vine based postprocessing yields a comparable performance with respect to tEMOS models if wind speed ensemble variables are included only and a significant improvement if additional meteorological and station specific weather variables are integrated. The case study indicates that training periods capturing seasonal patterns are performing best for both models. Additionally, we provide a criterion for calculating the variable importance in D-vine copulas and utilize it to outline which predictor variables are the most important for the correction of 10 m surface wind speed ensemble forecasts.

How to cite: Jobst, D., Möller, A., and Groß, J.: D-Vine Copula based Postprocessing of Wind Speed Ensemble Forecasts, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2902, https://doi.org/10.5194/egusphere-egu23-2902, 2023.

General circulation models (GCMs) are of extreme importance to making future climate projections. Those are used extensively by policymakers to manage responses to anthropogenic global warming and climate change.

To extract a robust global signal and evaluate uncertainties, individual models are often assembled in Multi-Model Ensembles (MMEs). Various approaches to combine individual models have been developed, such as the Multi-Model Mean (MMM) or its weighted variants.

Recently, Thao et al. (2022) proposed a model comparison approach based on graph cuts. Graph cut optimization was developed in the field of computer vision to efficiently approximate a solution for low-level computer vision tasks such as image segmentation (Boykov et al., 2001). Applied to MMEs, it allows selecting for each gridpoint the best-performing model and produces a patchwork of models that maximizes performances while avoiding spatial discontinuities. Thus, it considers the local performance of individual models in contrast with approaches such as MMM or similar methods that use global weights.

Here we propose a new multivariate combination approach of MMEs based on graph cuts. Compared to the existing univariate method, our approach ensures that the relationships between variables, that are present in GCMs, are locally preserved while providing coherent spatial fields. Moreover, we measure the local performance of models using the Hellinger distance between multi-decadal distributions. This allows a combination of models that is not only indicative of the average behavior (e.g. mean temperature or mean precipitation) but of the entire multivariate distribution, including more extreme values that have a high societal and environmental impact.

REFERENCES 

Boykov, Y., Veksler, O., & Zabih, R. (2001). Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(11), 1222–1239. https://doi.org/10.1109/34.969114

Thao, S., Garvik, M., Mariethoz, G., & Vrac, M. (2022). Combining global climate models using graph cuts. Climate Dynamics, February. https://doi.org/10.1007/s00382-022-06213-4

How to cite: Schmutz, L., Thao, S., Vrac, M., and Mariethoz, G.: A multivariate approach to combine general circulation models using graph cuts, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5821, https://doi.org/10.5194/egusphere-egu23-5821, 2023.

This work develops a novel method for generating conditional probabilistic rainfall forecasts with temporal and spatial dependence. A two-step procedure is employed. Firstly, marginal location-specific distributions are modelled independently of one another. Secondly, a spatial dependency structure is learned in order to make these marginal distributions spatially coherent.
To learn marginal distributions over rainfall values, we propose a class of models termed Joint Generalised Neural Models (JGNMs). These models expand the linear part of generalised linear models with a deep neural network allowing them to take into account non-linear trends of the data while learning the parameters for a distribution over the outcome space.
In order to understand the spatial dependency structure of the data, a model based on censored copulas is presented. It is designed for the particularities of rainfall data and incorporates the spatial aspect into our approach. Uniting our two contributions, namely the JGNM and the Censored Spatial Copulas into a single model, we get a probabilistic model capable of generating possible scenarios on short to long-term timescales, able to be evaluated at any given location, seen or unseen. We show an application of it to a precipitation downscaling problem on a large UK rainfall dataset and compare it to existing methods.

How to cite: Huk, D., Adewoyin, R., and Dutta, R.: Joint Generalized Neural Models and Censored Spatial Copulas for Probabilistic Rainfall Forecasting, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8594, https://doi.org/10.5194/egusphere-egu23-8594, 2023.

How to cite: Taillardat, M., Fougères, A.-L., Naveau, P., and De Fondeville, R.: Evaluating probabilistic forecasts of extremes using continuous ranked probability score distributions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8824, https://doi.org/10.5194/egusphere-egu23-8824, 2023.

The ERA5 global reanalysis has been compared against a high-resolution regional reanalysis (COSMO-REA6) by means of scale-separation diagnostics based on 2d Haar discrete wavelet transforms. The presented method builds upon existing methods and enables the assessment of bias, error and skill for individual spatial scales, separately. A new skill score (evaluated against random chance) and the Symmetric Bounded Efficiency are introduced. These are compared to the Nash-Sutcliffe and the Kling-Gupta Efficiencies, evaluated on different scales, and the benefits of symmetric statistics are illustrated. As expected, the wavelet statistics show that the coarser resolution ERA5 products underestimate small-to-medium scale precipitation compared to COSMO-REA6. The newly introduced skill score shows that the ERA5 control member (EA-HRES), despite its higher variability, exhibits better skill in representing small-to-medium scales with respect to the smoother ensemble members. The Symmetric Bounded Efficiency is suitable for the intercomparison of reanalyses, since it is invariant with respect to the order of comparison.

How to cite: Casati, B., Lussana, C., and Crespi, A.: Scale-separation diagnostics and the Symmetric Bounded Efficiency for the inter-comparison of precipitation reanalyses, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9083, https://doi.org/10.5194/egusphere-egu23-9083, 2023.

Statistical postprocessing of forecasts from numerical weather prediction systems is an important component of modern weather forecasting systems. A growing variety of postprocessing methods has been proposed, but a comprehensive, community-driven comparison of their relative performance is yet to be established. Important reasons for this lack include the absence of a fair intercomparison protocol, and, the difficulty of constructing a common comprehensive dataset that can be used to perform such intercomparison. Here we introduce the first version of the EUPPBench, a dataset of time-aligned medium-range forecasts and observations over Central Europe, with the aim to facilitate and standardize the intercomparison of postprocessing methods. This dataset is publicly available [1], includes station and gridded data, ensemble forecasts for training (20 years) and validation (2 years) based on the ECMWF system. The initial dataset is the basis of an ongoing activity to establish a benchmarking platform for postprocessing of medium-range weather forecasts. We showcase a first benchmark of several methods for the adjustment of near-surface temperature forecasts and outline the future plans for the benchmark activity. 

[1] https://github.com/EUPP-benchmark/climetlab-eumetnet-postprocessing-benchmark

How to cite: Bhend, J., Demaeyer, J., Lerch, S., Primo, C., Van Schaeybroeck, B., Atencia, A., Ben Bouallègue, Z., Chen, J., Dabernig, M., Evans, G., Faganeli Pucer, J., Hooper, B., Horat, N., Jobst, D., Merše, J., Mlakar, P., Möller, A., Mestre, O., Taillardat, M., and Vannitsem, S.: The EUPPBench postprocessing benchmark, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9328, https://doi.org/10.5194/egusphere-egu23-9328, 2023.

The established benefits of post-processing the results of multi-model ensembles, even by simple averaging, suggest a more radical approach: The models should be combined more frequently in run-time so as to form a single “supermodel”.  Simple nudging of models to one another, as frequently as the models might assimilate data from observations, combines model fusion with a reasonable degree of model autonomy.

Key to the success of the supermodeling approach is the phenomenon of chaos synchronization, known in the field of nonlinear dynamics, wherein two chaotic systems synchronize when connected through only a few of their variables, despite sensitive dependence on initial conditions. Synchronization gives rise to consensus among models. The nudging coefficients can be trained so that that consensus agrees with observations, because the effective dynamics of the trained supermodel, regarded as a single dynamical system, matches the dynamics of nature. Yet the number of independent nudging coefficients that must be trained is far less than the number of trainable parameters in a typical climate model.

It is expected that supermodeling will be especially useful for improving the representation of localized structures, such as blocking patterns, which will wash out if de-synchronized output fields of different models are combined by averaging.  We confirm a hypothesis that such coherent structures will synchronize even when the underlying fields do not, because the internal synchronization within each structure re-enforces synchronization between models: A configuration of CAM4 and CAM5 models, of different resolution, connected by nudging, exhibits correlated blocking activity even when the flows do not otherwise synchronize.  

We further explore the basis for correlated blocking activity in a pair of coupled quasi-geostrophic channel models. The local synchronization error is lower in a region of the channels where blocks form than elsewhere in the channels. Blocking correlations emerge as a vestige of “chimera synchronization”, the phenomenon in which complete synchronization of two spatially extended systems is intermittent in space as well as time. Such partial synchronization of different models in the regions of blocks - and of other structures such as jets, fronts, and large-scale convection - would be particularly useful for projecting climate-change patterns in extreme events associated with those structures.

How to cite: Duane, G., Schevenhoven, F., and Weiss, J.: Synchronization of Blocking Patterns in Diifferent Models, Connected So As to Form a “Supermodel” of Future Climate, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10153, https://doi.org/10.5194/egusphere-egu23-10153, 2023.

The theoretical advances on the properties of scoring rules over the past decades have broaden the use of scoring rules in probabilistic forecasting. In meteorological forecasting, statistical postprocessing techniques are essential to improve the forecasts made by deterministic physical models. Numerous state-of-the-art statistical postprocessing techniques are based on distributional regression evaluated with the Continuous Ranked Probability Score (CRPS). However, theoretical properties of such minimization of the CRPS have mostly considered the unconditional framework (i.e. without covariables) and infinite sample sizes. We circumvent these limitations and study the rate of convergence in terms of CRPS of distributional regression methods. We find the optimal minimax rate of convergence for a given class of distributions. Moreover, we show that the nearest neighbor method and the kernel method for distributional regression reach the optimal rate of convergence in dimension larger than 2 and in any dimension, respectively.
Associated article: https://doi.org/10.1016/j.ijforecast.2022.11.001

How to cite: Dombry, C., Pic, R., Naveau, P., and Taillardat, M.: Mathematical Properties of Continuous Ranked Probability Score Forecasting, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11230, https://doi.org/10.5194/egusphere-egu23-11230, 2023.

It is often stated that the goal of probabilistic forecasting is to issue predictive distributions that are as sharp as possible, subject to being calibrated. To assess the calibration of ensemble forecasts, it is customary to employ rank histograms. Rank histograms not only assess whether or not an ensemble prediction system is calibrated, but they also reveal what (if any) systematic biases are present in the forecasts. This information can readily be relayed back to forecasters, helping to improve future predictions. Such is the utility of rank histograms, several extensions have been proposed to evaluate the calibration of probabilistic forecasts for multivariate outcomes. These extensions typically introduce a so-called pre-rank function that condenses the multivariate forecasts and observations into univariate objects, from which a standard rank histogram can be constructed. Several different approaches to construct multivariate rank histograms have been proposed, each of which differs in the choice of pre-rank function. Existing pre-rank functions typically aim to preserve as much information as possible when condensing the multivariate forecasts and observations into univariate objects. Although this is sensible when testing for multivariate calibration, the resulting rank histograms are often difficult to interpret, and are therefore rarely used in practice.        
We argue that the principal utility of these histogram-based diagnostic tools is that they provide forecasters with additional information regarding the deficiencies that exist in their forecasts, in turn allowing them to address these shortcomings more readily; interpretation is therefore a key requirement. We demonstrate that there are very few restrictions on the choice of pre-rank function when constructing multivariate rank histograms, meaning forecasters need not restrict themselves to the few proposed already, but can instead choose a pre-rank function on a case-by-case basis, depending on what information they want to extract from their forecasts. We illustrate this by introducing a range of possible pre-rank functions when assessing the calibration of probabilistic spatial field forecasts. The pre-rank functions that we introduce are easy to interpret, easy to implement, and they provide complementary information. Several pre-rank functions can therefore be employed to achieve a more complete understanding of the multivariate forecast performance. Finally, having chosen suitable pre-rank functions, tests for univariate calibration based on rank histograms can readily be applied to the multivariate rank histograms. We illustrate this here using e-values, which provide a theoretically attractive way to sequentially test for the calibration of probabilistic forecasts.

How to cite: Allen, S. and Ziegel, J.: Assessing the calibration of multivariate ensemble forecasts: E-values and the choice of pre-rank function, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11660, https://doi.org/10.5194/egusphere-egu23-11660, 2023.

Climate models are used to generate future hydroclimatic projections for exploring how climate change may affect water resources. Their outputs, however, feature systematic errors due to parametrization and simplification of processes at the spatiotemporal scales required for impact studies. To minimize the adverse effects of such biases, an additional bias adjustment step is typically required.

Over the past decade, adjustment methods with different levels of complexity have been developed that consider one or several variables at a time, consequently adjusting one or multiple features of climate model simulations. Despite attempts in developing such methods and the growing use of some, the selection of methods for accurate simulation of streamflow remains subjective and still highly debated. In this study, we seek to answer whether sophisticated multivariate bias adjustment methods outperform simple univariate methods in the simulation of streamflow signatures.

To this end, we systematically investigated the ability of two simple univariate and two advanced multivariate methods to accurately represent various hydrological signatures relevant for water resources management in high latitudes. We offer practical guidelines for choosing the most suitable bias adjustment methods based on the objective of each study (i.e., hydrologic signatures of interest) and the hydroclimatic regime of the study catchments.

How to cite: Tootoonchi, F., Todorović, A., Grabs, T., and Teutschbein, C.: Impacts of uni- and multivariate bias adjustment methods on simulations of hydrological signatures in high latitude catchments, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12232, https://doi.org/10.5194/egusphere-egu23-12232, 2023.

Spatial sampling remains a conundrum for verification. The observations that are required are rarely on a grid, nor are they homogenously spaced. They are often located where there are people, easy access and do not sample the variable in a representative way. In an aggregate sense, scores derived from such observation locations, will give areas with greater observation density more weight in the aggregate if the variations in network density are not accounted for. Furthermore the performance in some parts of the domain may not be represented at all if there are no observations there. Gridded analyses on the other hand often provide complete coverage, and offer great ease of use, but adjacent grid boxes are not independent. Given this relative wealth of coverage and uniform sampling, we tend to use all available grid points for computing aggregate scores for an area or region, despite knowing that this is likely to produce too-narrow confidence intervals and inflate any statistical significance that may be present. 

In this presentation a variety of approaches, both empirical and statistical, are explored to establish what we ought to include when computing aggregate scores. Three different empirical sampling approaches are compared to selections from statistical coverage or network design algorithms. The empirical options include what is termed “strict” sub-sampling, whereby a sample is taken from the full grid and the reduction in sample size is explored by systematically continually taking a sub-sample from the sub-sample. The second is a systematic reduction in sample size from the original grid whereby each sample is drawn from the original grid, taken every other grid point, then every 3rd grid point, every 4th etc. The third is a mean computed from N random draws of reducing sample size. These empirical options do not respect land or sea locations. They are purely intended at looking at the behaviour and stability of the sample score. The coverage design algorithms provide a methodology for deriving homogeneous samples for irregularly spaced surface networks over land, and regularly spaced sampling of grids over the ocean, to achieve an optimal blend of sampling for regions that cover both land and sea.  These sample sizes and sample scores are compared to a statistically computed effective sample size. 

Some interesting and surprising results emerge. One of which is that as little as 1% of the total number of grid points may be sufficient for measuring the performance of the forecast on a grid, though the proportion of the total will always be dependent on (to varying degrees) the variable, the threshold or event of interest, the metric or score, and the characteristics of the geographical region of interest. 

How to cite: Mittermaier, M. and Gilleland, E.: Exploring empirical and statistical approaches for determining an appropriate sample size for aggregate scores, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12242, https://doi.org/10.5194/egusphere-egu23-12242, 2023.

Reliability is a key attribute of an ensemble forecast. Typically, this means that one expects that the ensemble spread reflects the potential error of the corresponding ensemble mean forecast. In the realistic case of an unperfect forecast, reliability deficiencies can be diagnosed with tools such as the reliability diagram and the rank histogram. Along with the computation of scores, the use of these diagnostic tools is common practice in ensemble forecast verification when assessing univariate forecasts. But what does reliability mean in practical terms when assessing multivariate forecasts? Here the concept of reliability is revisited in the simplest of the multivariate cases: the bivariate forecast. As a result, we propose a set of new diagnostic tools with an emphasis on the cross-variable reliability aspect. Real case examples are used for illustration and discussion.

How to cite: Ben Bouallegue, Z.: On the reliability of bivariate forecasts, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12316, https://doi.org/10.5194/egusphere-egu23-12316, 2023.

Direct model output forecasts by Numerical Weather Prediction models (NWPs) present some limitations caused by errors mostly due to sensitivity to initial conditions, sensitivity to boundary conditions and deficiencies in parametrization schemes (i.e. orography).
These sources of error are unavoidable, and atmospheric chaotic dynamics make prediction errors spread rapidly in time in the course of the forecast, inducing both systematic and random errors.
Nonetheless, in the last 50 years, NWPs had a significant decrease in the impact of these sources of errors, even in the long-term forecast, thanks for instance to an ever-increasing computational capability, but their relevance is still not neglectable.
Moreover, different NWPs present specific different pros and cons which are findable empirically. For instance, in the case of precipitation forecast in north-west Italy, low-resolution models (e.g. ECMWF-IFS) are more reliable in terms of space and time in predicting the average precipitation, while high-resolution models (e.g. COSMO-2I) tend to forecast better the maximum precipitation. Research purposes apart, actual limitations must be seen in an operational context, where weather forecasts’ skillfulness and associated uncertainty are information of the utmost importance to the forecaster and in general to the user of a certain forecasts system.

To tackle these limitations of NWPs and the need for an uncertainty-quantified meteorological forecast, we propose a machine learning-based multimodel post-processing technique for precipitation forecast. We focus on precipitation since it is the most important variable in the issue of spatially localized weather alert notice by the Italian Civil Protection system and at the same time it is one of the most challenging variables to forecast. 
We use a Convolutional Neural Network (CNN) to obtain deterministic and probabilistic forecast grids over 24h up to 48h focusing on North-West Italy, using several high and low-resolution deterministic NWPs as input and using high-resolution rain-gauge corrected radar observations for the training. The effect of the usage of different convolutional parameters (e.g. stride, padding) is taken into account. The deterministic output grid is chosen as the grid with the lowest mean square error obtained during the training, and it is compared with the linear regression of the input NWPs and with every single model. The probabilistic output grid is generated by considering the statistical ensemble of the twenty grids with the lowest mean square error obtained during the training, and it is compared with the logistic regression of the input NWPs and with ECMWF-EPS as a benchmark, both at different precipitation thresholds.

How to cite: Monaco, L., Cremonini, R., and Laio, F.: Towards a machine learning based multimodel for precipitation forecast over the italian peninsula, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13327, https://doi.org/10.5194/egusphere-egu23-13327, 2023.

In recent years neural networks have successfully been applied to probabilistic post-processing of numerical weather prediction forecasts. In the Bernstein Quantile Networks (BQN) method predictive quantile distributions are specified by Bernstein polynomials and their coefficients linked to input features through flexible neural networks. However, precipitation presents an additional challenge due to its mixed distributed nature with a considerable proportion of dry events for short accumulation periods. In this presentation, it is demonstrated how the BQN method can be modified to mixed distributed variables like precipitation by introducing a latent variable and treating zero precipitation cases as censored data. The method is tested on both synthetic and real precipitation forecast data and compared to an approach where a model of the probability of precipitation is combined with a model of precipitation amounts using the laws of probability.

How to cite: Bremnes, J. B.: Censored Bernstein quantile networks for probabilistic precipitation forecasting, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13849, https://doi.org/10.5194/egusphere-egu23-13849, 2023.

Numerical weather prediction (NWP) models usually output their forecasts at a multiplicity of different lead times. For example, the Met Office ensemble prediction system for the UK (MOGREPS-UK) predicts atmospheric variables on a 2.2km grid for up 126h on hourly and sub-hourly timesteps. Even though for applications, information is often required on this range of lead times, many post-processing methods in the literature are either applied at fixed lead time or by fitting individual models for each lead time. This is also the case in systems used in practice such as the Met Office IMPROVER system. However, this is 1) computationally expensive because it requires the training of multiple models if users are interested in information at multiple lead times and 2) prohibitive because it restricts the training data used for training post-processing models and the usability of fitted models.

In this work we investigate lead time dependence of ensemble post-processing methods by looking at ensemble forecasts in an idealized Lorenz96 system as well as temperature forecast data from the Met Office MOGREPS-UK system. First, we investigate the lead time dependence of estimated model parameters in non-homogenous Gaussian regression (NGR -- a standard ensemble post-processing technique) and find substantial smoothness. Secondly, we look at the usability of models fitted for one lead time and employed at another to then thirdly fit models that are “lead time continuous”, meaning they work for multiple lead times simultaneously by including lead time as a covariate using spline regression. We show that these models can achieve similar performance to the classical “lead time separated” models, whilst saving substantial computation time. Fourthly and finally we make first steps towards the development of a cheap computational model including seasonality and working continuously over the lead time, needing to be fit only once.

How to cite: Wessel, J., Ferro, C., and Kwasniok, F.: Lead time continuous statistical post-processing of ensemble weather forecasts, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14425, https://doi.org/10.5194/egusphere-egu23-14425, 2023.

Ensemble forecasts are important due to their ability to characterize forecast uncertainty, which is fundamental when forecasting extreme weather. Ensemble forecasts are however often biased and underdispersed and thus need to be post-processed.

A common approach for this is the use of ensemble model output statistics (EMOS), where a parametric distribution is fitted with a limited number of predictors. With recent advances in computer science and increased amounts of data available, machine learning techniques, like random forests, are becoming more popular for high dimensional regression problems. In this research, we explore the use of the quantile regression forest (QRF), a random forest adapted for conditional quantile estimation, applied to medium range gridded probabilistic precipitation forecasts. QRFs are non-parametric and allow for a larger number of predictors, which means they can possibly consider more dependencies that might otherwise not be captured with a simple EMOS.

A QRF takes several hyperparameters that influence the way the decision trees in the forest are constructed. We explore the minimum number of samples needed in a leaf to split it (minimum node size) and the number of predictors explored in each split (mtry). A hyperparameter space is constructed by setting ranges for both minimum node size and mtry, and the optimal hyperparameter set is determined by performing a cross validated grid search. Here, each model is assessed based on the continuous ranked probability skill score (CRPSS). For comparison, EMOS is applied with a zero-adjusted gamma (ZAGA) distribution, using a limited number of predictors that are physically correlated to precipitation. Both methods are verified on a separate testing data set and evaluated using several scores, including CRPSS and Brier skills score (BSS).

We consider 4 years (November 2018 – October 2022) of archived operational ECMWF-IFS ensemble forecasts for the Netherlands. The data is split into November 2018 – October 2021 for training and cross-validation, and October 2021 – October 2022 for testing, separating data for season, initialization time and lead-time. Forecasts are post-processed up to +10 days. Ensemble statistics on 60+ forecast variables are used as predictors. Spatially and temporally aggregated, gauge-adjusted radar observations are used as predictand. The raw ensemble is considered as the benchmark.

The results of this research will determine what method will be used to post-process the ensemble precipitation forecasts in the context of the early warning center (EWC) of the Royal Netherlands Meteorological Institute. The most suitable method could differ between shorter and longer lead times.

How to cite: van der Kooij, E., Squintu, A., Whan, K., and Schmeits, M.: Quantile regression forests for post-processing ECWMF ensemble precipitation forecasts: hyperparameter optimization and comparison to EMOS, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14560, https://doi.org/10.5194/egusphere-egu23-14560, 2023.

In the framework of KNMI’s Early Warning Center (EWC), ECMWF ensemble (ENS) predictions are used to issue medium-range forecasts of severe weather. Timely forecasts of wind gusts extremes are important to prevent potential damage. However, ensemble forecasts are affected by biases and under- or over-dispersion. These errors lead to a reduction in the skill of the forecasts, especially for long lead-times and for extreme cases, such as windstorms and deep convective episodes. Hence, statistical post-processing is a fundamental step in the establishment of a skillful weather alert system for extreme wind gust events.     

However, weather models like ECMWF-IFS are subject to frequent updates, which include changes in the calculation of certain diagnostic variables and by consequence in statistical features of their ensemble distribution. This is the case for ECMWF wind gusts forecasts, whose bias has been reduced with the last update in October 2021. Therefore, the use of pre-update wind gusts forecasts in the training of the post-processing model must be considered with care.

In the context of the development of an Ensemble Model Output Statistics (EMOS) model, this limitation has been tackled by reconstructing wind-gusts forecasts with a preliminary EMOS model. This step has been performed by including in the regression those variables that are used by ECMWF for the calculation of wind gusts, which were less affected by the update.

The reconstructed wind gusts forecasts have been added to a set of summary statistics of the ensemble distribution of variables physically related to wind gusts. A process of forward selection has been applied to identify the most relevant contributions to the general EMOS model, highlighting reconstructed wind gusts as the most important predictor for all lead-times.

The post-processed forecasts obtained with this experimental EMOS model have been verified and compared to those calculated with a conventional EMOS model (performed ignoring the above caveats) and with the results of a non-parametric Quantile Regression Forest. These models have been trained on the same period (2018-2021) and tested on the period that has followed the update (2021-2022), including only grid-points and stations that cover the territory of the Netherlands and distinguishing between summer and winter half-years. The method showing the best performance will be employed operationally for the post-processing of ECMWF-ENS wind gust forecasts over the Netherlands and will be used in the EWC weather alert system.

How to cite: Squintu, A. A., van der Kooij, E., Whan, K., and Schmeits, M.: NWP model updates and post-processing: a strategy for an EMOS model on ECMWF wind gusts forecasts, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14712, https://doi.org/10.5194/egusphere-egu23-14712, 2023.

How to cite: Zanetta, F., Nerini, D., Buzzi, M., and Liniger, M. A.: Towards sub-kilometer resolution probabilistic analysis of surface wind in complex terrain, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15152, https://doi.org/10.5194/egusphere-egu23-15152, 2023.

Accurate predictions of heavy precipitation in India are vital for impact-orientated forecasting, and an essential requirement for mitigating the impact of damaging flood events. Operational forecasts from non-convection-permitting models can have large biases in the intensities of heavy precipitation, and while convection-permitting models can perform better, their operational use over large areas is not yet feasible. Statistical postprocessing can reduce these biases for relatively little computational cost, but few studies have focused on postprocessing forecasts of monsoonal rainfall.

We present a postprocessing method for operational precipitation forecasts based on local precipitation distributions for 30 Indian weather types. It is applied to ensemble forecasts for daily precipitation with 12km spatial resolution and lead times of up to 10 days from the Indian National Centre for Medium Range Weather Forecasting (NCMRWF) Ensemble Prediction System (NEPS). The method yields local probabilistic forecasts that are the weighted mean of the observed local precipitation distributions for each weather type, with weights given by the relative frequency of the weather types in the forecast ensemble.

The general forecast skill is determined through the Continuous Ranked Probability Skill Score (CRPSS) and the skill for predicting the exceedance of the local 90^th percentile is quantified through the Brier Skill Score (BSS). The CRPSS shows moderate improvement over most of India for forecasts with one day lead time, and substantial improvements almost everywhere for longer lead times. The BSS for one day forecasts indicates a spatially complex pattern of higher and lower performance, while for longer lead times the forecasts for heavy precipitation are improved almost everywhere. The improvements with respect to both measures are particularly high over mountainous or wet regions. We will also present reliability diagrams for the raw and postprocessed forecasts of threshold exceedances.

How to cite: Widmann, M., Gonczol, N., Angus, M., and Neal, R.: Postprocessing of ensemble precipitation forecasts over India using weather types, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17348, https://doi.org/10.5194/egusphere-egu23-17348, 2023.

In this study, a global variable-resolution modeling framework of atmospheric dust and its radiative feedback is introduced and evaluated. In this model, atmospheric dust is simulated simultaneously with the meteorological fields, and dust-radiation interaction is included. Five configurations of global mesh with the refinement at different resolutions and over different regions of interest are used to explore the impacts of regional refinement on modeling dust lifecycle at regional and global scales. The model produces reasonably the overall magnitudes and spatial variabilities of global dust metrics such as surface mass concentration, total deposition, AOD, and radiative forcing compared to observations and previous modeling results. Two global variable-resolution simulations with mesh refinement over major deserts of North Africa (V16km-NA) and East Asia (V16km-EA) simulates less dust emissions and smaller dry deposition rate inside the refined regions due to the weakend near-surface wind speed caused by better resolved topographic complexity at higher resolution. Dust mass loading over North Africa is close to each other between V16km-NA and U120km, while over East Asia, V16km-EA simulates higher dust mass loading. Over the non-refined areas with the same resolution, the difference between global variable-resolution and uniform-resolution experiments also exist, which is partly related to their difference in dynamic time-step and the coefficient for horizontal diffusion. Refinement at convection-permitting resolution around the Tibet Plateau (TP) leads to significantly different dust and precipitation around the TP against coarse resolution, which implies that dust-precipitation interaction over this area deserves further investigation with this  global variable-resolution modeling framework in future. 

How to cite: Zhao, C., Feng, J., Du, Q., Xu, M., Gu, J., and Hu, Z.: Simulating atmospheric dust and its radiative impact with a global variable-resolution model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3730, https://doi.org/10.5194/egusphere-egu23-3730, 2023.

Aerosols play a significant role in the radiation and atmospheric precipitation physics of microphysics and convection, and have a significant impact on air quality, visibility, public health, aviation, and climate. A physics suite, which includes the aerosol-aware double momentum Thompson-Eidhammer microphysics scheme (TH-E MP), the scale-aware and aerosol-aware Grell-Freitas (GF) convection scheme, and the MYNN-EDMF boundary layer and shallow cloud scheme, was developed at NOAA Global System Laboratory (GSL). The GSL physics suite is applied in the FV3GFS global model and the Rapid Refresh Forecast System (RRFS) regional model. We also developed the RRFS – Smoke and Dust model (RRFS-SD) at NOAA GSL with the Common Community Physics Package (CCPP), which is designed to facilitate a host-model agnostic implementation of physics parameterizations. Because of the interactive and strongly coupled nature of chemistry and physics, it is natural to allow for the smoke, dust and other chemical modules to be called directly from the physics suite. Here we embedded the plume rise modules for wildfire, sea-salt, dust, and anthropogenic emission modules into the regional model of RRFS and global UFS model using CCPP as subroutines of physics. The prognostic emissions of sea-salt, and organic carbon are combined to represent the “water friendly” aerosol emission, while the prognostic emission of dust is used to represent “ice friendly” aerosol emission for TH-E MP. With this implementation, we examined the aerosol indirect feedback when using the TH-E scheme in the global FV3GFS forecast with C768 (~13km) horizontal resolution and 127 vertical levels. There are significant cloud-radiation responses to the aerosol differences, and the severely positive precipitation bias over Europe and North America is significantly alleviated when applying this aerosol emission method for indirect feedback. We also examined the smoke direct feedback to the radiation in the RRFS-SD with 3km horizontal resolution and 64 vertical layers for September, 2020 during which the western US experienced extreme wildfires. The aerosol direct feedback run significantly improves the forecast of aerosol optical depth, surface 2m air temperature, 10m wind speed, and radiation fluxes.

How to cite: Li, H., Grell, G., Ahmadov, R., Romero-Alvarez, J., Zhang, L., James, E., Baker, B., Olson, J., Sun, S., Schnell, J., and Wang, N.: Indirect and direct aerosol feedback in the global and regional scale NOAA UFS Weather Model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3983, https://doi.org/10.5194/egusphere-egu23-3983, 2023.

The Chinese Meteorology Administration chemistry model CUACE is online integrated into the mesoscale operational weather prediction (NWP) model (GRAPES_Meso5.1) and aerosol-cloud-radiation interaction is achieved to establish the first version (V1) of chemistry-weather (CW) interacted model GRAPES-Meso5.1/CUACE CW V1. The most polluted winter 2016-2017 is selected to study the meteorology impacts on haze/fog prediction, the impact of aerosol-radiation, aerosol-cloud and CW interaction (ARI, ACI, CWI) on haze/fog prediction and NWP. Single way model without CWI displays reasonable PM _2.5 and visibility prediction in general. However, modeled PM_2.5 peaks are underestimated and visibility valleys are overestimated during haze/fog pollution, the underestimation of relative humidity (RH) contributes major to this misestimation; CWI model cut the negative bias of PM _2.5 peaks and the positive bias of visibility valleys. The improvement of 5km and 3km low visibility by CWI during severe haze/fog period is more obvious than that of 10 km, which just compensates for the largest deficiency in low visibility prediction related with severe haze/fog by single way model; The NWP including sea level pressures, relative humidity(RH), temperature, wind speed are also improved by CWI from surface to upper troposphere; ARI contributes larger to the predicted PM_2.5 ,visibility and NWP improvement than that of ACI, their relative contributions varies with model vertical height and the overlapping condition of cloud and aerosols. Due to the joint contribution of RH and PM_2.5, CWI’s improving on visibility is larger than PM_2.5. This study illustrates the importance of including CWI in air quality prediction model.

How to cite: Wang, H.: Chemistry-Weather Interacted Model System GRAPES_Meso5.1/CUACE CW V1.0: Development, Evaluation and Application in Better Haze/fog Prediction in China, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4025, https://doi.org/10.5194/egusphere-egu23-4025, 2023.

How to cite: Zhang, W., Wang, H., and Zhang, X.: Aerosol–cloud interaction in the atmospheric chemistry model GRAPES_Meso5.1/CUACE and its impacts on mesoscale numerical weather prediction under haze pollution conditions in Jing–Jin–Ji in China, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4146, https://doi.org/10.5194/egusphere-egu23-4146, 2023.

We investigate the aerosol direct and semi-direct effect on subseasonal prediction using NOAA’s fully coupled Unified Forecast System (UFS) model, which includes the atmospheric model FV3 with the Global Forecast System (GFS) physics package V17, MOM6 ocean model, WW3 wave model and CICE6 sea ice model. A systematic twin experiment is carried out: (i) UFS with prescribed aerosol climatology and (ii) UFS coupled to interactive aerosols from the GOCART aerosol module. Both experiments are deterministic 32-day hindcasts with monthly initialization over multiple years.

The modeled aerosol optical depth (AOD) in both experiments is in good agreement with the MODIS satellite observations. The AOD from the experiments with interactive aerosols captured the interannual variability seen in the observations. The estimated radiative forcing from the aerosol radiation interaction in these two sets of experiments is similar in the multi-year average. However, the advantage in the experiments with interactive aerosols can be seen clearly when simulating radiative forcing in the extreme dust storm and biomass burning events. Changes in cloud and precipitation are small between these two sets of experiments.

How to cite: Sun, S., Frost, G., Grell, G., Zhang, L., Baker, B., Meixner, J., and Cheng, A.: Simulating direct and semi-direct effect of aerosols on subseasonal prediction: climatology versus interactive aerosols in the UFS model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11392, https://doi.org/10.5194/egusphere-egu23-11392, 2023.

While overall global warming with the causes and global processes connected to well-mixed CO2, and its impacts on global to continental scales are well understood with a high level of confidence, there are knowledge gaps concerning the impact of many other non-CO2radiative forcers leading to low confidence in the conclusions. This relates mainly to specific anthropogenic and natural precursor emissions of short-lived GHGs and aerosols and their precursors. These gaps and uncertainties also exist in their subsequent effects on atmospheric chemistry and climate, through direct emissions dependent on changes in e.g., agriculture production and technologies based on scenarios for future development as well as feedback of global warming on emissions, e.g., permafrost thaw. In addition to the atmospheric radiative forcing (gaseous or aerosols), albedo changes connected to land use and land cover can play a role, depending on the adaptation or mitigation measures included in different scenarios.

The main goal of the EC Horizon Europe project FOCI (from the call HORIZON-CL5-2021-D1-01-0 Improved understanding of greenhouse gas fluxes and radiative forcers, including carbon dioxide removal technologies), is to assess the impact of key radiative forcers, where and how they arise, the processes of their impact on the climate system, to find and test an efficient implementation of these processes into global Earth System Models and into Regional Climate Models, eventually coupled with CTMs, and finally to use the tools developed to investigate mitigation and/or adaptation policies incorporated in selected scenarios of future development targeted at Europe and other regions of the world. We will develop new regionally tuned scenarios based on improved emissions to assess the effects of non-CO2 forcers. Mutual interactions of the results and climate services producers and other end-users will provide feedback for the specific scenarios preparation and potential application to support the decision-making, including climate policy.

How to cite: Halenka, T., Sokhi, R., and Baklanov, A.: Project FOCI - Non-CO2 Forcers and Their Climate, Weather, Air Quality and Health Impacts, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13739, https://doi.org/10.5194/egusphere-egu23-13739, 2023.

The Integrated Forecasting System (IFS) of ECMWF is core of the Copernicus Atmosphere Monitoring Service (CAMS) to provide global analyses and forecasts of atmospheric composition, including reactive gases, as well as aerosol and greenhouse gases. The CAMS global model consists of the aerosol model of the IFS, IFS-AER, which is a sectional-bulk scheme, while the chemistry scheme is based on a CB05-based carbon-bond mechanism, with the option to couple this to BASCOE-based stratospheric chemistry. The composition model is updated regularly, aligned with updates of ECMWF’s operational meteorological model. Here we report on updates planned for the operational version after next, referred to as CY49R1. This concerns revisions on a large range of topics, as developed over the recent years, and therefore impacting many aspects of chemistry and aerosol composition in troposphere and stratosphere. Main aspects concern:

• A review of the representation of polar stratospheric clouds and of their impact on stratospheric ozone,
• An extension of IFS-AER to represent stratospheric sulfate aerosols, coupled with CB05 and BASCOE precursor gases,
• An upgrade of gas-particle partitioning through the implementation of EQSAM4Clim in the IFS,
• Computation of aerosol, cloud and rain pH, and use of the update pH values in aqueous chemistry,
• A combined representation of aerosols and chemistry deposition processes (wet and dry),
• Update of aerosol optics, including a simple representation of dust asphericity and hygroscopic growth,
• Update of PM diagnostic output

In this contribution we provide an overview of expected changes with emphasis on changes in composition modeling aspects. We will present their expected impact on key atmospheric composition aspects, including air quality performance across major pollution regions across the world, aerosol optical depth, dust, and stratospheric composition products.

How to cite: Remy, S., Huijnen, V., Simon, C., Metzger, S., Williams, J., Minganti, D., Bingen, C., Alexe, M., and Flemming, J.: Changes to the IFS atmospheric composition model in support to the CAMS update for CY49R1., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15355, https://doi.org/10.5194/egusphere-egu23-15355, 2023.

AQ-WATCH (Air Quality: Worldwide Analysis and Forecasting of Atmospheric Composition for Health) is an international consortium, which co-develops and co-produces tailored products and services derived from space and in situ observational data for improving air quality forecasts and attribution. For this purpose, AQ-WATCH develops a supply chain leading to innovative downstream products and services for providing air quality information tailored to the identified needs of international users. This presentation will focus on one of the AQ-WATCH products, the AQ-WATCH air quality forecast system. Air quality forecast models provided by the AQ-WATCH consortium are set up for the focus regions in Asia and the Americas, based on the templates of Copernicus European and MarcoPolo-Panda Asian ensembles, but with much higher resolution and reliance on regional emission and observational information. The models are established over the focus regions using the meteorological and emission data taken from Copernicus repositories and other national archives and refined with local information wherever available. Each forecast model is then evaluated using local observational datasets and with the needs of the stakeholders. Machine learning workflows are being incorporated into the forecast system to improve both results from individual models and the model ensembles based on bias correction from observation data. Lessons learnt from model comparison in the focus regions will be presented. At last, the potential application of the system prototype, as well as the other AQ-WATCH products, namely the global and regional air quality atlas, the air quality attribution & mitigation, the dust and fire forecasts, and the fracking analysis tool, to other regions of the world will be discussed.

How to cite: Li, C. W. Y., Sofiev, M., Timmermans, R., Kranenburg, R., Pfister, G., Kumar, R., Deroubaix, A., Huneeus, N., Opazo, M., Caballero, T., Mo, D., Zhang, X., Leufen, L. H., Kleinert, F., Schultz, M., Granier, C., Basart, S., Salvi, O., Caillard, B., and Brasseur, G.: Introduction to the AQ-WATCH multi-model air quality forecast system, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15547, https://doi.org/10.5194/egusphere-egu23-15547, 2023.

The ECMWF’s Integrated Forecast System (IFS) is the global atmospheric model used by the Copernicus Atmospheric Monitoring Service (CAMS) to provide analyses and forecasts on atmospheric composition. Currently, the CAMS global model includes the aerosol model of the IFS, the aerosol module IFS-AER making use of a sectional-bulk scheme, and the chemistry scheme based on a CB05-based carbon-bond mechanism, with the option to couple this to stratospheric chemistry module BASCOE. The combined BASCOE will be used operationally in the CAMS global system starting from the upgrade to cycle 48R1 planned in June 2023. This abstract focuses on further developments related to stratospheric chemistry and aerosols that are to be implemented in the future operational cycle 49R1, as well as on a first evaluation of IFS’ performances in representing stratospheric aerosols and chemistry against different datasets.

Initially focussing on the troposphere, IFS-AER has been extended to include and represent stratospheric sulfate aerosol processes, keeping the existing tracers. The extended IFS-AER(strato) has been coupled to IFS(BASCOE) through the gaseous sulphuric acid tracer, to the IFS radiation scheme, and to the 4Dvar assimilation scheme. The evaluation of aerosol aspects makes use of aerosol datasets (aerosol extinction, AOD, …) from the Global Ozone Monitoring by Occultation of Stars (GOMOS, onboard Envisat), and the Global Space-based Stratospheric Aerosol Climatology (GloSSAC), based on different cases studies including quiescent and (highly) volcanic periods. It has also been tested against reference simulations from WACCM-CARMA. These intercomparisons show a reasonable agreement against retrieval datasets such as GloSSAC and reference simulations from WACCM-CARMA. In quiescent conditions, the new system showed a decreasing trend with respect to the reference datasets.

BASCOE includes a simple PSC parameterization, which has been updated and tuned in cycle 49R1. In order to assess the impact of this upgrade, we evaluate the composition of the polar lower stratosphere during the winter-spring seasons ("ozone hole" events) of 2008, 2009 and 2020 above the Antarctic and 2009, 2011, 2012 and 2020 above the Arctic, with a focus on 5 key species observed by Aura-MLS. This evaluation demonstrates the capacity of IFS(BASCOE) to forecast the chemical composition of the polar lower stratosphere above both the Arctic and the Antarctic for several years with very different evolution of the polar vortex. While further improvements are desirable and will require an overhaul of the PSC parameterization, the current performance allows us to study the interannual variability of ozone hole episodes.

How to cite: Bingen, C., Chabrillat, S., Errera, Q., Huijnen, V., Metzger, S., Minganti, D., Rémy, S., Williams, J., and Flemming, J.: Extension and evaluation of the Integrated Forecast System (IFS) cycle 49R1 to stratospheric aerosols and chemistry for the global Copernicus Atmospheric Monitoring Service (CAMS), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15869, https://doi.org/10.5194/egusphere-egu23-15869, 2023.

Southeastern Brazil is the most developed and populous region with 89.5 million of inhabitants, according to the Instituto Brasileiro de Geografia e Estatística (IBGE) for 2021. The main metropolitan and industrialized areas are concentrated in its four states (São Paulo, Minas Gerais, Rio de Janeiro and Espírito Santo). One of them comprises the Metropolitan Area of São Paulo (MASP) with 7.3 million vehicles that releases air pollutant -gas and ultrafine particles- to the atmosphere due to the use of different fuel types; light-duty vehicles consume ethanol, gasohol (85% gasoline and 25% hydrous ethanol) or natural gas, and heavy vehicles (i.e., buses and trucks) run on diesel. So, frequently high concentrations of air pollutants (ozone and fine particles) in urban areas are above the recommended limits suggested by the World Health Organization (WHO) with high health risk mainly for children and elderly. The biggest concern is the high health risk of exposing the population to ultrafine particles, also called nanoparticles. Consequently, it is important to understand the formation of ultrafine particles, whether they are emitted directly or formed in the atmosphere. 

We study the formation processes of ultrafine particles in the scenario of fuel change in the road transport sector, including a greater use of biofuels. The air quality modeling system will analyze the impact of different scenarios in urban areas in southeastern Brazil. We begin with the air quality simulation for the current conditions as the base case scenario using the WRF-Chem model. As the main data input, we use emission data with two temporal profile distributions (monthly and hourly time average). First, we use available monthly anthropogenic emission's data processed by the European Copernicus Atmosphere Service (CAMS). Secondly, we added hourly road transport emission calculated with the LAPAT model, which use emission factors derived from measurements in experimental campaigns in tunnels where light and heavy vehicles circulate within the MASP. This simulation test with the WRF-Chem model considers the MOZART-MOSAIC mechanism and additional emissions from other sources such as biomass burning and chemical initial and boundary conditions from the CAM-Chem model. Experimental data and measurements of meteorological and air quality parameters will support the work to evaluate the performance of the model’s results.

How to cite: Delgado Peralta, A. H. and Andrade, M. D. F.: Impact of the use of biofuels on the formation of ultrafine particles in southeastern Brazil, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16081, https://doi.org/10.5194/egusphere-egu23-16081, 2023.

The Integrated Forecasting System (IFS) of ECMWF is used within the Copernicus Atmosphere Monitoring Service (CAMS) to provide global analyses and forecasts of atmospheric composition, including aerosols as well as reactive trace gases and greenhouse gases. Inorganic gas/aerosol equilibrium involving the major sulphate and nitrate anions, i.e., H₂SO₄/HSO₄^-/SO₄^2- and HNO₃/NO₃^-, largely determines the aerosol acidity, while the gas/liquid/solid phase partitioning of semi-volatile cations, NH₃/NH₄⁺, and the liquid/solid partitioning of non-volatile mineral cations, particularly Ca²⁺, Mg²⁺, and K⁺, overall control the gas-liquid-solid aerosol equilibrium partitioning of reactive nitrogen compounds. For the NO₃^- and NH₄⁺ equilibrium, our recent developments have focused on EQSAM4Clim, which has been recently integrated into the IFS as a computationally efficient means of describing aerosol pH in a global modeling system.

EQSAM4Clim is used in the IFS to estimate gas-liquid-solid partitioning and the aerosol associated liquid water content, which is subsequently used to estimate the associated aerosol, cloud and rain acidity. The aerosol, cloud and rain pH is computed by considering the liquid water (H₂O) content in the respective liquid water phase using either the aerosol associated water computed by EQSAM4clim, and if present, the cloud and/or rain water of the IFS. The pH is coupled to the aqueous phase chemistry in IFS(CB05) and in the wet deposition of SO₂ and NH₃, which in-turn affects the aerosol composition through changes in the SO₂/SO₄^2-, NH₃/NH₄⁺and HNO₃/NO₃^- partitioning, the aerosol associated liquid water content and solution pH.

Here we present first results of the of the impact of improved aerosol acidity in the solution (aerosol/cloud/rain water) on PM2.5 forecasts simulated in the IFS which are subject to gas-particle partitioning. In particular, the NH₄⁺, NO₃^- and SO₄^2- concentrations have been compared against observational datasets at the surface, showing promising improvements as a direct result of the new pH computations. When coupling the EQSAMClim pH into the aqueous phase chemistry routine, the surface concentrations of SO₄^2- and the SO₂ + SO₄^2- wet deposition fluxes are improved over most of Europe, but degraded over parts of US. The relative impact of the improved pH appears generally small as compared to other related changes such as updates in aqueous chemistry rates. In the pH coupling, the aqueous chemistry component dominates the impact on the wet deposition of SO₂/NH₃. All of these results are highly dependent on the emissions input (SO₂/NO_x/NH₃). 

How to cite: Metzger, S., Rémy, S., Huijnen, V., Williams, J., Chabrillat, S., Bingen, C., and Flemming, J.: Impact of pH computation from EQSAM4Clim on inorganic aerosols in the CAMS system, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16085, https://doi.org/10.5194/egusphere-egu23-16085, 2023.

A global aerosol data assimilation (DA) system based on the ensemble-variational (EnVar) application in the Joint Efforts for Data assimilation Integration (JEDI) was recently developed for the Global Ensemble Forecast System - Aerosols (GEFS-Aerosols) in operations at NOAA/NWS/NCEP. The aerosol optical depth (AOD) retrievals at 550 nm are assimilated to improve the GEFS-Aerosols initial conditions and its subsequent forecasts. To account for aerosol emission uncertainty in the ensemble forecasts and thus enhance AOD assimilation, a stochastically-perturbed emission (SPE) approach was implemented in the Common Community Physics Package (CCPP)-based GEFS-Aerosols. The performance of this JEDI-based EnVar aerosol DA system has been evaluated using the CCPP-based GEFS-Aerosols in the near-real time (NRT) experiments at NOAA/OAR/GSL and the global aerosol reanalysis products that  assimilate 550 nm AOD retrievals from the the Visible Infrared Imaging Radiometer Suite (VIIRS) instruments and the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments, respectively. The NRT experiment results are displayed on the GSL website (https://ruc.noaa.gov/projects/nrt/Aerosol-DA/). Both the NRT experiment results and global aerosol reanalyses demonstrate that compared to the six-hour forecasts without AOD assimilation, the analyses and subsequent six-hour forecasts resulting from AOD assimilation show significantly improved agreement with AOD retrievals from VIIRS, MODIS and the Aerosol Robotic NETwork (AERONET), and AOD analyses/reanalyses from NASA and ECMWF. Although AOD retrievals, due to their column-integral nature, provide limited information regarding aerosol compositions and vertical profiles, AOD assimilation in our experiments generally contributes to improved aerosol analyses and forecasts verified against those from NASA and ECMWF.  

One of the ongoing Unified Forecast System (UFS)-Research to Operations (R2O) efforts aims to integrate and improve aerosol prediction within UFS (hereafter referred to as UFS-Aerosols). UFS-Aerosols will eventually replace the standalone GEFS-Aerosols for operations at NOAA/NWS/NCEP. Compared to GEFS-Aerosols, UFS-Aerosols is coupled with NASA's second-generation Goddard Chemistry Aerosol Radiation and Transport (GOCART) model including additional nitrate aerosol species, adopts improved biomass burning and dust emissions, and allows for aerosol-radiation interactions. Motivated by the promising results of assimilating AOD for GEFS-Aerosols and to advance aerosol assimilation and prediction in UFS, we are extending and improving this JEDI-based EnVar aerosol DA system for UFS-Aerosols. It requires further development of AOD forward operator, its tangent linear and adjoint models in JEDI’s Unified Forward Operator (UFO) to accommodate additional nitrate aerosol species in UFS-Aerosols. Enhancements to this DA system for UFS-Aerosols include implementing SPE within UFS-Aerosols to improve background ensemble and implementing assimilation of log-transformed AOD within JEDI to better satisfy the Gaussian assumptions in the DA update. To evaluate these new developments for UFS-Aerosols, cycled DA experiments will be performed to assimilate 550 nm AOD retrievals from VIIRS instruments on board NOAA’s satellites, and verified against various aerosol observations and reanalyses. Results will be presented.

How to cite: Huang, B., Pagowski, M., Martin, C., Tangborn, A., Abdi-Oskouei, M., E. Barré, J., Kondragunta, S., Grell, G., and Frost, G.: Extending and Improving JEDI-based Global Aerosol Data Assimilation System for UFS-Aerosols, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17426, https://doi.org/10.5194/egusphere-egu23-17426, 2023.

Including prognostic atmospheric composition (AC) simulations in numerical weather predication or climate modelling application to exploit AC – weather feedbacks is often prohibited by the high computational cost of adding complex AC simulation to the weather or climate model.  There are in principle two approaches to solve this problem: (i) drastically reduce the complexity of the aerosol and chemistry simulations in the weather model, or (ii)  safe cost by reducing the spatial resolution of the model components simulating the AC processes and implement a coupling mechanism.  

At ECMWF a dual configuration forecast (dcfc) approach for the of the Integrated Forecasting System (IFS) has been developed based on the infrastructure for Object-Oriented Prediction System (OOPS). It enables the coupled simulation of a high-resolution application and a low-resolution application of the IFS and a coupling mechanism. The low-resolution model instance simulates the aerosol and chemistry-processes as activated for the operational AC forecasts by the Copernicus Atmosphere Monitoring Service (CAMS)  

We show first scientific results of this dual configuration forecast (dcfc) for a sever dust storm event in Europe in March 2022. We will discuss to what extent and at what computational cost the dcfc application can forecast the meteorological impact of the dust on radiation and 2m temperatures. We will compare the dcfc result to the more expensive integrated IFS-CAMS configuration as well as to NWP forecast using the aerosol climatology as currently applied in the ECMWF operational weather forecasts.  

How to cite: Flemming, J. and Hamrud, M.: A multiple gird approach for atmospheric composition - aware NWP forecasts with the ECMWF forecast system, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17501, https://doi.org/10.5194/egusphere-egu23-17501, 2023.

Through a NERC/MOES funded project, PROMOTE, analysis based on WRF and CMAQ models has been conducted to understand the impact of road transport emissions on air quality over Delhi. NCEP/FNL ¹ data was used to drive WRF with four nested domains over India with resolutions of 45km, 15 km, 5 km, and 1.6 km for 2018. EDGAR v5.0 emission inventory (for 2015) ² and Cam-Chem initial and boundary condition data ³ were used to drive the CMAQ model. In the baseline runs, all domains were considered without any change in emissions, while in Scenario 1, road transport sector was removed in the third domain (5km) covering Delhi region. Model performance for NO_x, NO₂, PM₁₀, PM_2.5 and O₃ was evaluated with available observations, recognising that air quality and meteorological datasets were limited for the period analysed. In the later part of the study, OSCAR model ⁴ was used to predict the high-resolution air quality over Delhi and estimate the contributions from road transport emissions. Relative contributions to Delhi's air quality from local and regional long range transport sources are discussed. 

As part of a NERC funded COP26 project on Climate Adaptation for India, an overarching goal of this study is to quantify how air quality changes in South Asia in a changing climate under SSP245 (middle-of-the-road scenario) ⁵. A dynamical downscaling process was implemented and bias-corrected Coupled Model Intercomparison Project Phase 6 (CMIP6) data ⁶ was used to drive Weather Research and Forecasting (WRF) model simulations. Simulations have been conducted for the South Asia-Cordex domain for 2015 (representative of 2011-2020 years) and 2050 (representative of 2046-2055 years) with a 27 km grid resolution. The Community Multiscale Air Quality (CMAQ) model was driven with an average of ten years meteorology with future land use land cover, initial and boundary conditions, and future emissions ⁷ for India. To assess the impact of averaged meteorology on the CMAQ performance, the CMAQ model was run with both ten years' averaged meteorology around 2015 (2011-2020) and only 2015 meteorology.

Although averaging ten years around the desired year suppresses the diurnal variations, it provides an indication of monthly changes in climate and air quality variables. Under the selected SSP245 scenario, the CMAQ model predicted monthly means of PM_2.5 anomalies (2050-2015) range over India between 8 to 41 μg/m³. Significant change is PM_2.5, PM₁₀, NO_x, and O₃ anomalies, especially in the urban regions of India, such as Delhi, before and after the Monsoon months (June to October) have been observed.

Financial Support: We acknowledge funding from NERC/MOES (Reference: NE/P016391/1) for the PROMOTE project and NERC funding (Reference: 2021COPA&R48Sokhi) for the COP26 Improving adaptation strategies for climate extremes and air pollution affecting India project.

References

¹ NCEP/NOAA/U.S. 2015,  https://doi.org/10.5065/D65Q4T4Z.

² Crippa M. et al. (2019): http://data.europa.eu/89h/377801af-b094-4943-8fdc-f79a7c0c2d19

³ Buchholz, R. R. et al, (2019). https://doi.org/10.5065/NMP7-EP60

⁴  Singh V, et al. (2020) Environmental Pollution, 257, 113623

⁵ van Vuuren, D.P. et al. (2011). Climatic Change 109, 5. https://doi.org/10.1007/s10584-011-0148-z

⁶ Xu, Z. et al.  (2021). Sci Data 8, 293. https://doi.org/10.1038/s41597-021-01079-3

⁷ SMoG-India v1 2015 and 2050 emissions dataset, NERC project.

How to cite: Alyuz, U., Sokhi, R., Momoh, K., Singh, V., Venkataraman, C., Sharma, A., Gupta, G., Tibrewal, K., Khaiwal, R., Mor, S., and Beig, G. and the PROMOTE Team: High-resolution dynamical downscaling of present and future air quality over the South Asia-Cordex domain with a focus on the megacity Delhi, India, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17561, https://doi.org/10.5194/egusphere-egu23-17561, 2023.

Convectively induced turbulence (CIT) is a serious aviation hazard and it is challenging to forecast the CIT in the region near convection. Previous studies used reginal model with high resolution or global model with low resolution and selected empirical indices to diagnose the turbulence. In this study, we used The Model for Prediction Across Scales (MPAS) to simulate some cases of CIT reported near Hong Kong. MPAS allows us to use convection-permitting resolution in the interested area while including the global-large scale circulation with coarser resolutions in other regions. The eddy dissipation rate (EDR) is computed to diagnose the potential occurrence of CIT. We compared three methods for calculating EDR from the resolved flow in the MPAS, the first one based on second order structure function, the second one based on Scale-Similarity in Large Eddy Simulation (LES) and the third is Near Cloud Turbulence (NCT) diagnostics by using Convective Gravity Wave Drag. Comparing with the NOAA Graphical Turbulence Guidance (GTG) product and flight data suggests that computing EDR with Scale-Similarity is more effective and accurate than second order structure function and NCT diagnostics. Resolution is also an important factor in forecast, we tested the method in mesh with different resolutions but similar distributions, the results from low resolution simulations can generate a useful turbulence pattern forecast, but the intensity is weak, highlighting the value of high resolution simulations that can resolve convection. We evaluated the sensitivity to several model physics and numeric options in simulations. Those variations can change the EDR prediction by influencing the intensity and the life cycle of the convection. No particular scheme produces systematically more intense turbulence than others, suggesting varying model physics captures some stochasticity of convection. Compared with flight records of EDR along the flight routes, MPAS could produce in three out of five cases showing maximum EDR is close to the observed intensity of turbulence (EDR>0.4). However, in the other two cases, the results are not satisfactory mainly because of significant location biases of the predicted convection. We also add initial condition perturbation-based large ensemble in one case and find it possible to improve the prediction of the failed cases by influencing the position of the convection. Further work should be conducted to prioritize the ensemble members since only a few members can capture the turbulence and doing the average will erase them easily.

How to cite: Chen, H., Shi, X., Leung, C. Y., Cheung, P., and Chan, S.: Using MPAS model to forecast the Convectively Induced Turbulence, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1439, https://doi.org/10.5194/egusphere-egu23-1439, 2023.

The interactions of ice particles with radiative fluxes in tropical high clouds substantially alter the heating structure within the atmosphere, also known as cloud radiative heating (CRH). CRH influences the upper-tropospheric temperature structure and thus modulates the strength and position of tropical and extratropical circulations. Moreover, it influences the life cycle of tropical high clouds through longwave destabilization of the cloud layer and lifting of clouds by absorption of both shortwave and longwave radiation by ice crystals. A possible change of CRH, for example, due to global warming, can substantially alter the tropical climate.Despite a large body of work that has explored interactions between clouds and radiation, responses of CRH to global warming remain largely unknown. We therefore use idealized SAM cloud-resolving model simulations, the RCEMIP multimodel dataset, and a 15-year-long satellite-derived CRH dataset to explore changes in CRH under different sea surface temperatures.

To a first approximation, the upper tropospheric CRH shifts nearly isothermally to a higher altitude level following a surface warming. In addition, upper-tropospheric CRH in 27 of the 32 analyzed models increase by 0.5 to 10%/K, with a mean value of about 3%/K. Interestingly, the CRH increases despite decreases in upper tropospheric ice water content and cloud fraction. The increase in CRH can be to a large extent explained by an increase in atmospheric transmissivity due to a 2-3 km vertical shift of high clouds, in an environment with decreased air density. Similarly, all models simulate an increase in the upper tropospheric clear-sky radiative cooling in warmer conditions.

Additionally, the CRH response to surface warming can be largely predicted by assuming a nearly isothermal vertical shift of upper tropospheric CRH profiles (as per the fixed anvil temperature hypothesis) following a warmer moist adiabat and by considering the increase in CRH magnitude due to changes in atmospheric density. Therefore, if we know the CRH of a reference climate state, we can, to a good approximation, estimate its response to surface warming.

The modeled CRH vertical shift and increase are confirmed by a 15-year-long satellite-derived tropical CRH dataset. The years with the highest SSTs lead to the most positive CRH that is shifted to higher levels, similarly to what is simulated by RCEMIP models.

How to cite: Gasparini, B., Voigt, A., Mandorli, G., and Stubenrauch, C.: SST-driven changes in cloud radiative heating in RCEMIP models and observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2556, https://doi.org/10.5194/egusphere-egu23-2556, 2023.

The vertical structure of large-scale tropical circulations is determined by their complex coupling with both deep and shallow convection. This is reflected in our theoretical frameworks for understanding the tropical precipitation distribution, which consist of “deep” theories that focus on overturning circulations that extend throughout the depth of the troposphere and “shallow” theories that focus on low-level convergence driven by boundary-layer pressure gradients. While both types of theories suggest links between low-level thermodynamic fields and the precipitation distribution, shallow theories highlight the importance of the distribution of surface temperature, while deep theories additionally highlight the importance of the low-level humidity. 

Here we use idealised cloud-permitting simulations to elucidate the physical factors that control the vertical structure of tropical circulations. We first demonstrate how the influence of convective entrainment on the lapse rate can act to change the vertical structure of deep tropical circulations, with implications for the behaviour of precipitation in the current and future climate. We further investigate the interaction between deep and shallow tropical circulations  by simulating an idealised overturning circulation over varying surface conditions. By independently varying the sea-surface temperature and moisture availability, the low-level temperature and moisture distributions are manipulated such that the predictions of “deep" and “shallow" theories of the circulation may be distinguished. The results provide insight into the relative roles of oceanic SST gradients and land-ocean contrasts in determining the climatological precipitation distribution in the tropics.

How to cite: Singh, M.: Shallow and Deep Circulations in the Tropical Atmosphere, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3078, https://doi.org/10.5194/egusphere-egu23-3078, 2023.

Radiative-convective equilibrium is the simplest possible way to phrase many questions about a deep-convecting atmosphere and is accessible by a wide range of model types. The radiative-convective equilibrium project (RCEMIP) provides a common configuration, but reveals a large spread in the simulated climate across models, including profiles of temperature and relative humidity. Here we use simple models and theory to understand the intermodel spread in CAPE, relative humidity, and their responses to warming.

Across the RCEMIP ensemble, temperature profiles are systematically cooler than a moist adiabat, consistent with theory that they are set by dilute ascent. As horizontal grid spacing is reduced in models with explicit convection from 1 km to 200 m, CAPE and relative humidity increase. Across all models, CAPE increases with warming at a rate (14-19%/K) greater than that expected from the Clausius-Clapeyron relation. We find that there is higher CAPE (greater instability) in models that are on average moister in the mid-troposphere, which is consistent with the simple plume model of Romps (2016) in which both instability and relative humidity depend on entrainment and precipitation efficiency. The sign of the relationship suggests that differences in entrainment drive the intermodel spread. This relationship is true across both models with explicit and parameterized convection.

To more explicitly evaluate the drivers of the intermodel spread, we use the Romps (2016) model to diagnose theory-implied values of entrainment and precipitation efficiency given the simulated values of CAPE and relative humidity. We then decompose the the variability across models in CAPE and relative humidity (and their responses to warming) into contributions from entrainment, precipitation efficiency, and the depth of the convecting layer. Targeted microphysics parameter perturbation experiments with an individual cloud-resolving model in which precipitation efficiency is varied and explicitly diagnosed provide proof of concept for this decomposition technique. 

How to cite: Wing, A. and Singh, M.: Control of Tropical Stability and Relative Humidity in Radiative-Convective Equilibrium Simulations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3532, https://doi.org/10.5194/egusphere-egu23-3532, 2023.

The initiation of deep convection in a diurnal cycle is still one of the most important uncertainties in a climate numerical model. This is partially due to our poor understanding of the physical mechanisms leading to the transition from shallow to deep convection. In this work, we discuss the role of shallow cumulus clouds in the initiation of deep convection. By using a simple entraining plume model, we show that the interaction between an active and a passive shallow cumulus helps the former to reach higher altitudes and, in the right conditions, may initiate deep convection. It is also shown that the organization of passive and active clouds due to the formation of cold pools may act as a positive feedback. Furthermore, based on the proposed mechanism, a stochastic triggering function is derived, which can be implemented in climate models. As an important feature, the stochastic function is scale-aware, which makes it suitable for simulations at the gray-zone.

How to cite: Vraciu, C.-V.: The role of passive shallow cumuli in the transition from shallow to deep convection, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3561, https://doi.org/10.5194/egusphere-egu23-3561, 2023.

This study focuses on the deep-inflow mixing features of the orographically locked diurnal convection, involving interactions between local circulation and the thermodynamic environment of the convection. Under the weak synoptic weather regime, orographically locked diurnal convection is a typical summertime phenomenon in Taiwan, a tropical island in the Asian monsoon region. Numerical simulations are carried out using the vector vorticity equation model with high-resolution Taiwan topography (TaiwanVVM), which can appropriately simulate the characteristics of diurnal convection and the evolution of boundary layer and local circulation. The semi-realistic approach, simplified by observed soundings as the uniform initial condition over the entire domain, emphasizes the decisive environmental factors that modulate the development of convection, representing the variability of the background environment by the ensembles. The analyses by the deep-inflow mixing framework, including the locally-derived convective structures and the upstream moist static energy (MSE) transport, improve the understanding of the interactive physical processes in the boundary layer development and local circulation evolution of orographically locked diurnal convection over complex topography. The convective structures of the deep-inflow mixing, increasing vertical velocity and convective mass flux with height through a deep lower-tropospheric inflow layer, are found in strong convective updraft columns within heavily-precipitating systems over precipitation hotspots. While the topography constrains the location of the convection, enhanced convective development is associated with higher upstream MSE transport through this deep-inflow layer via local circulation, augmenting the rain rate by 35% in precipitation hotspots. The results highlight the importance of non-local dynamical entrainment of the deep-inflow, transporting MSE via local circulation to supply the growth of orographically locked diurnal convection. Thus, the deep-inflow mixing framework can serve as the theoretical basis for describing the orographic locking feature of diurnal convection over complex topography. Guided by the simulations, the Storm Tracker mini-radiosondes are released upstream of the precipitation hotspot, targeting observations within the most common deep-inflow path. Initial field measurements support the presence of high MSE transport within the deep-inflow layer when organized convection occurs at the precipitation hotspot.

How to cite: Chang, Y.-H., Chen, W.-T., Wu, C.-M., Kuo, Y.-H., and Neelin, J. D.: Identifying the Deep-inflow Mixing Features in Orographically Locked Diurnal Convection, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4079, https://doi.org/10.5194/egusphere-egu23-4079, 2023.

Change of the intertropical convergence zone (ITCZ) with global warming has important consequences for the regulation of the tropical climate and for future precipitation projections. Most of the volume of the ITCZ is filled with ice associated with convective anvils, which opens the perspective of using the response of ice clouds to study changes of the tropical convergence zones with global warming. Past studies have shown a decrease of tropical high-cloud fraction with surface warming, whereby the response of anvil clouds is used to interpret the response of ice clouds as a whole. However, tropical clouds organize over a very wide range of scales, meaning that the response of ice clouds is more complex. In particular, precipitating deep convection may represent a small volume of total cloudiness, but it concentrates most of the ascending motion in the tropics and is therefore of crucial importance for the dynamics. Here we show how the high resolution in next generation convection-resolving climate models and in observations can be leveraged to directly measure the response of precipitating deep convection with surface warming in the ice signal. For this purpose, we use the first year-long simulations of global warming ever performed with a Global Storm Resolving Model (GSRM) at 3 km resolution. These simulations use the eXperimental System for High-resolution prediction on Earth-to-Local Domains (X-SHiELD), developed at the Geophysical Fluid Dynamics Laboratory (GFDL). By tracking the response of tropical clouds to surface warming from the response of ice water path (IWP), the vertical integral of ice mixing ratio, we show that the response of precipitating deep convection can be identified at high resolution and that this response, marked by an increase in frequency of very deep convective cores and a decrease in frequency of more moderate convection, is robust in model and active sensor observations. We discuss this result and show how it promotes a simple view of the changes of tropical convergence zones in ice-based coordinates.

How to cite: Bolot, M., Harris, L., Cheng, K.-Y., Blossey, P., Bretherton, C., Clark, S., Kaltenbaugh, A., Merlis, T., Zhou, L., and Fueglistaler, S.: New GSRM global warming simulations and active sensors reveal robust changes of tropical convergence zones in cloud ice space, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4150, https://doi.org/10.5194/egusphere-egu23-4150, 2023.

While a poleward shift of the jet stream and storm track in response to increased greenhouse gases appears to be robust, the magnitude of this change
is uncertain and differs across models, and the mechanisms for this change are poorly constrained. An intermediate complexity GCM is used to explore
the factors governing the magnitude of the poleward shift and the mechanisms involved. The degree to which parameterized subgrid-scale convection is inhibited has a leading-order effect on the poleward shift, with a simulation with more convection (and less large-scale precipitation) simulating a significantly weaker shift, and eventually no shift at all if convection is strongly preferred over large-scale precipitation. Many of the mechanisms that have been proposed to lead to the poleward shift are present in all simulations (even those with no poleward shift), and hence we can conclude that these mechanisms are not of leading-order significance for the poleward shift in any of the simulations. In contrast, the thermodynamic budget is able to diagnose the reason the jet and storm track shift differs among the simulations, and helps identify midlatitude latent heat release as the crucial differentiator. These results have implications for intermodel spread in the jet, hydrological cycle, and storm track response to increased greenhouse gases in intermodel comparison projects.

How to cite: White, I., Garfinkel, C., Keller, B., Lachmy, O., Gerber, E., and Jucker, M.: Sensitivity of projected storm track and jet latitude changes to the parameterization of convection: implications for mechanisms of the future poleward shift, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4711, https://doi.org/10.5194/egusphere-egu23-4711, 2023.

Two structural assumptions are frequently employed in convective parameterisation: the diagnostic and quasi-equilibrium assumptions. The former assumes an instantaneous relationship between the large-scale environment (“macrostate”) and subgrid-scale convective activity, while the latter postulates that convective processes are almost in equilibrium with slowly evolving large-scale forcing at all times. Both assumptions do not take into account the role of convective memory (“microstate” memory), which is defined as the dependence of convection on its own history. Here, we present the memory behaviour of three convection schemes by comparing their responses in two idealised RCE experiments in single-column models (SCMs) to those of a cloud-resolving model (CRM). Three main findings from these tests will be discussed. First, when the large-scale environment is held constant (“FixMacro”), precipitation remains invariant in time with the Zhang-McFarlane scheme, confirming that the scheme does not parameterise convective memory and is fully diagnostic. The org scheme (Mapes & Neale, 2011) displays similar behaviour to the CRM in that precipitation increases in the first moments after FixMacro starts, with larger entrainment rates associated with slower growth. However, its logarithmic growth shape differs from that of the CRM, which displays exponential growth, and can be explained using the scheme’s governing equations. Second, when the prognostic convective memory variable is set to zero at one time step (essentially wiping out microstate memory), the org scheme displays remarkably similar behaviour to the CRM, with precipitation dropping to zero and then recovering to its RCE value over a recovery time scale t_mem. In comparison, precipitation in the LMDZ cold pool scheme (Grandpeix & Lafore, 2010) responds in the opposite direction: it grows and then falls back to its RCE value. Finally, the mean and temporal variance of the org variable were found to correlate strongly with memory strength (t_mem), indicating that org has captured important aspects of convective memory. Overall, our results indicate that the org and LMDZ cold pool schemes partially, but do not fully capture CRM memory behaviour and are limited by their structural assumptions. They also demonstrate the usefulness of our simple idealised experiments to probe the memory behaviour of convection schemes. 

How to cite: Hwong, Y. L., Colin, M., Aglas, P., Muller, C., and Sherwood, S.: Evaluating Memory Properties in Convection Schemes Using Idealised Tests, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4968, https://doi.org/10.5194/egusphere-egu23-4968, 2023.

The shallow cumulus clouds are ubiquitous in the atmosphere, populating a large part of the subtropical oceans. They may play a strong climate feedback due to their cooling effect on the Earth atmosphere. As a result, a large number of studies investigated the organization of cumulus clouds and their interaction with the climate. However, the organization of passive shallow clouds and their impact on the atmospheric convection and climate change received very limited attention. In this work, we perform a series of large eddy simulations in order to investigate how the organization and the total cloud cover depends on the relative humidity of the environment. We show that although the active cumulus clouds only show a weak correlation with the relative humidity, the passive clouds are very sensitive to it. We show thus that the cloud cover of the shallow cumuli is very sensitive to the relative humidity which could be very important in the context of the climate change. Furthermore, we formulate a conceptual picture to explain the organization of passive shallow cumulus clouds.

How to cite: Marin, A. and Vraciu, C.-V.: On the organization of passive shallow cumulus clouds, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5898, https://doi.org/10.5194/egusphere-egu23-5898, 2023.

Within the atmospheric modelling community, a large focus in recent years has been on the concept of Convective Self-Aggregation (CSA): In an environment of radiative convective equilibrium, with homogeneous initial conditions and a constant-temperature tropical sea surface, convection can spontaneously aggregate into domain-wide patterns of persistent dry areas and constrained rainy areas over a temporal timescale of weeks to months. CSA, albeit still a modeling paradigm, could reveal the mechanisms behind some of the convective organization observed in the tropics.

This process of forming domain-wide structure can be accelerated to the order of days by imposing oscillating surface temperatures with a large enough amplitude [1]. The ‘diurnally aggregated’ cloud field is similar to CSA as it also constrains the surface rain field to certain parts of the domain. Further, pattern formation was found to initiate first as persistent dry patches in the uppermost layers of the simulated atmosphere. The dry patches subsequently penetrate through to the subcloud layer [2].

In this work we investigate how diurnal surface temperature amplitudes, typical of tropical land, affect the formation of persistent dry patches and the spatio-temporal extent of the emergent mesoscale convective systems. We run a set of cloud resolving simulations initialized with typical profiles of temperature and humidity. We impose a large-amplitude diurnally oscillating surface temperature, which we then set to constant at different times, to see the effect on the diurnally aggregated cloud field. We present the results of this study, which show a strong dependence on the degree of aggregation over ‘land’, in determining the aggregation over ‘sea’, and a form of hysteresis arises.

1. Haerter, Jan O., Bettina Meyer, and Silas Boye Nissen. ‘Diurnal Self-Aggregation’. Npj Climate and Atmospheric Science 3, no. 1 (30 July 2020): 1–11. https://doi.org/10.1038/s41612-020-00132-z.
2. Jensen, Gorm G., Romain Fiévet, and Jan O. Haerter. ‘The Diurnal Path to Persistent Convective Self-Aggregation’. Journal of Advances in Modeling Earth Systems 14, no. 5 (2022): e2021MS002923. https://doi.org/10.1029/2021MS002923.

How to cite: Kruse, I. L. and Haerter, J. O.: Investigating Convective Self-Aggregation in the Transition from Land to Sea, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6933, https://doi.org/10.5194/egusphere-egu23-6933, 2023.

The difficulty to accurately represent atmospheric convection in numerical weather forecasts contributes to persistent biases in weather and climate simulations – particularly tropical precipitation. Convection-permitting global forecasts are an improvement on global models with parametrized convection schemes, however it is not yet clear whether they improve forecast skill to match or improve upon the current approach of nesting a convection-permitting high-resolution regional model inside a global model with parameterized convection. This is far less computationally expensive than running a global convection-permitting model.

To test this, the Met Office is coordinating a UK K-scale project nesting high resolution (2.2 km) limited area models (LAMs) within global models that have between 5 and 10 km grid resolution. We compare these nested regional models with two different global simulations, run with parameterised and explicit convection science configurations. The 2.2 km resolution LAMs encompass a variety of domains focussing on both tropical land and ocean regions.

Our current work seeks to investigate if and where we see differences in model evolution between the high-resolution nested LAM approach and the explicit convection global driving model.  We focus on an active MJO event in January 2018 where enhanced convection propagated across the Indian Ocean and impacted the Maritime continent. For high-impact events such as this, do we see a marked change in the model forecast when explicitly simulating convection globally rather than in a regional limited area model (as currently used in operational forecasts)? Further, are differences between the global convection permitting and LAM forecasts more pronounced over ocean-dominated regions where the amplitude of the diurnal cycle of convection is smaller?

This talk will summarise our findings in the context of the wider K-scale project, evaluating how our recent work contributes to the development of more accurate weather forecasts.

How to cite: Macholl, J., Jones, R., and Lewis, H.: Globally modelling explicit convection: how does it compare with the nested limited area model approach at high horizontal resolutions?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7082, https://doi.org/10.5194/egusphere-egu23-7082, 2023.

The cold pools, created below cumulonimbus from the evaporation of precipitation, generate the strong winds responsible for the large dust storms called “haboobs” which appear in the Sahel in summer. Most global climate models do not take into account these types of dust emissions due to lack of parameterization of cold pools and associated gusts (Marsham et al. 2011 ; Pantillon et al. 2015).

The introduction of a parameterization of cold pools in the LMDZ climate model has improved the representation of convection, and in particular of the diurnal cycle of continental precipitation in the tropics (Rio et al. 2009). The aim of this work is to develop a parameterization of gusts related to cold pools in order to take into account ‘‘hoobobs’’ in LMDZ model. To do this, we use Large Eddy Simulations (LES) performed on an oceanic domain and in Radiative-Convective Equilibrium (RCE) mode. We use a LES of an oceanic RCE case, easier to analyze because the temperatures are uniform on the surface and therefore the cold pools easier to detect. Before developing a gust parameterization, we evaluate the cold pools parameterization in LMDZ on this RCE case, which has never been done so far. If the comparison confirms the relevance of the scheme and its qualitative match to the LES behavior, is also led to substantial improvements and adjustments to this scheme. Next, we analyze the wind distributions in the LES in order to construct a parametrization based on a probability distibution function of the subgrid scale distribution of the wind which will allow us to take into account the effect of gusts on dust storms. The parametrization relates the moments of the distribution to large-scale wind speed, the spreading speed of the cold pools and the surface fraction covered by the latter. In the following, we will test the parametrization on the LMDZ model by focusing on dust storms in the Sahel during the rainy season.

How to cite: Thiam, M. L., Hourdin, F., Grandpeix, J.-Y., Rio, C., and Gaye, A. T.: Parametrization of dust storms in the Sahel by cold pools, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7224, https://doi.org/10.5194/egusphere-egu23-7224, 2023.

The representation of cumulus convection is a known source of uncertainty within current weather and climate models. Where model resolution is too coarse to accurately resolve convection, parameterisations are required to estimate the impact of small-scale convective processes. High resolution large eddy simulations (LES) can be used to diagnose many aspects of convective processes, such as heat and momentum budgets and rates of entrainment. However, LES is computationally expensive, making it impossible to use within operational models. This study aims to bridge the gap between current coarser models and LES by developing a stochastic Lagrangian model to represent an ensemble of air parcels. Vertical velocity, liquid water potential temperature, and total specific humidity are predicted following the ensemble of parcels. The random motions associated with turbulence are represented by a stochastic term within the w-tendency equation. The mean fields which the parcels interact with are defined by an ensemble average of nearby parcels. Several fixers have been developed to ensure that conservation properties are respected. At the current stage of development, the model can represent dry convective boundary layer and shallow convection cases. A theoretical study of the stochastic differential equations is useful to verify the self-consistency of the model and also as a tool for calibrating various parameters within the model. A key question for this project is how well the stochastic parcel model can replicate the statistics of LES results. This will act as a measure of the model’s success, allowing for a deeper understanding on accurately modelling convective processes. Due to the Lagrangian nature of the model, analysis can be conducted upon how the parcels’ characteristics change over time as the parcels experience smaller-scale convective processes such as entrainment. Ultimately, results from this model may yield better understandings of small-scale convective processes. This can create potential for improvements to parameterisations in operational models, reducing model uncertainty generated by convective processes.

How to cite: Hutton, T., Thuburn, J., and Beare, R.: A Lagrangian View to the Evolution of Convective Updrafts, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9002, https://doi.org/10.5194/egusphere-egu23-9002, 2023.

The intrinsic failure of eddy-diffusion parameterizations in representing upward transport of heat in the convective boundary layer, recognized since the 70s, has lead to various propositions of parameterizations like counter-gradient terms and third order closures to account for the asymmetry of the vertical transport. An approach that is now well recognized consists in combining a mass flux parameterization of the organized structures of the convective boundary layer with a local TKE closure for small scale turbulence. The idea traces back to a proposition by Chatfield and Brost (1987) and is since often referred to as the Eddy Diffusion Mass Flux (EDMF) approach. The “thermal plume model” developed for LMDZ was the first EDMF scheme published and tested in a climate model (Hourdin et al., 2002). It was first introduced in the LMDZ5B atmospheric component of the IPSL model for CMIP5. However, this first version suffered from youth problems. It is only for CMIP6A, about 20 years after the development of the parameterization, that a first satisfactory version of the model was delivered. Through years, and more often with this last version, the key role of the representation of shallow convection on many component of the system has been realized: 1) the ventilation of air by the subsiding air around thermal plumes dries the surface, reinforcing the near surface evaporation. Representing this convection correctly both over trade winds and subsiding regions in the tropics, together with the associated cumulus and stratocumulus clouds, is one of the key for the reduction of the East Tropical Ocean warm bias; 2) the preconditioning of the deep convection by a phase of shallow convection is a key for a correct representation of the phasing of the diurnal cycle of convective rainfall over continents; 3) the strong diurnal cycle of the convective boundary layer in desert areas is essential to well represent the maximum of near surface wind in the morning, responsible for a maximum of dust emission, when the momentum of the nocturnal low level jet is brought suddenly back toward the surface when reached by the developing dry convection. 4) the thermal plume model being active about on half of the globe all the time, it controls the transport of all trace elements, with some non linear effects when the emissions themselves show a diurnal cycle. In this presentation, we review these lessons learned with LMDZ, identify the issues which should require further developments, and expose how new machine assisted techniques allow to reconcile improvement of parameterizations at process scale and climate model improvement.

Chatfield, R. B., & Brost, R. A. (1987). A two-stream model of the vertical transport of trace species in the convective boundary layer. Journal of Geophysical Research, 92, 13,263–13,276

Hourdin, F., Couvreux, F., & Menut, L. (2002). Parameterisation of the dry convective boundary layer based on a mass flux representation of thermals. Journal of the Atmospheric Sciences, 59, 1105–1123

How to cite: Hourdin, F. and Rio, C.: On the importance of dry and cloudy boundary layer convection and of its parameterization in climate models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9438, https://doi.org/10.5194/egusphere-egu23-9438, 2023.

Understanding how convective storms respond to changes in their environment on a local scale is critical to begin to elucidate how Earth’s changing climate will affect storms globally. There is now a vast amount of storm-scale observational data, including from geostationary and low-earth orbiting satellites and ground-based observing systems. However, employing these datasets to build comprehensive databases of convective storms and the local environments that form them requires new analysis methodologies. Here, in preparation for the NASA INCUS satellite mission, we have used the tobac tracking package to identify, track and analyze storms and their environments with these big datasets. Using tobac to track storms with geostationary satellite and ground-based radar data, we have built a comprehensive, months-long database of convective storms over their entire lifetime. For each individual convective storm, the database contains their formation environments (including convective available potential energy, wind shear, etc.), evolution over time, and, where applicable, additional data, such as those from low earth orbiting satellites. In this presentation, we will employ this vast database of clouds and storms to quantify the relationship, on a storm scale, between thermodynamic and dynamic environments and storm properties, including lifetime, growth rate, and ice and liquid water paths. 

How to cite: Freeman, S., Schulte, R., Leung, G., and van den Heever, S.: Tracking Convective Storms and their Environments with the tobac Tracking Package, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9855, https://doi.org/10.5194/egusphere-egu23-9855, 2023.

The convective mass flux within tropical convection influences the large-scale circulation, drives cloud radiative forcing, has integral links to the production of fresh water, and impacts extreme weather. CMF forms the focus of the recently selected Investigation of Convective Updrafts (INCUS) mission to be launched in 2026. This NASA mission is comprised of 3 spacecraft, all of which will carry a Ka-band cloud radar. One spacecraft will also carry a passive microwave radiometer. The 3 smallsats are to be separated by time intervals of 30, 90 and 120 seconds, thus allowing for the rapid and systematic sampling of the same storm with all three spacecraft. These time intervals (delta-ts) also facilitate the investigation of the magnitude and evolution of CMF, which will be examined as a function of storm type, storm lifecycle and environmental properties. INCUS will therefore provide the first global systematic investigation into CMF and its evolution within deep tropical convection.

A wide range of research tasks have been conducted in preparation for the INCUS mission and the development of the INCUS algorithms including: (1) running and analyzing extensive suites of large-domain, high-resolution model simulations; (2) examining ground-based Doppler radar observations obtained using adaptive scanning techniques during several recent field campaigns; and (3) evaluating anvil characteristics using passive microwave radiometer and geoIR data. This talk will focus on three specific highlights arising from these modeling and observational analyses. First, we will examine the temporal scales of updraft variability. Second, we will analyze the relationship between ice water path cores and convective updrafts. Finally, we will demonstrate proof of the INCUS delta-t concept linking changes in reflectivity to CMF through the use of ground-based radar analyses.

How to cite: van den Heever, S., Haddad, Z., Dolan, B., Freeman, S., Grant, L., Kollias, P., Leung, G., Luo, J., Marinescu, P., Posselt, D., Rasmussen, K., Sai, P., Schulte, R., Stephens, G., Storer, R., and Takahashi, H.: Tropical Convection through the Lens of the INCUS Mission, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11285, https://doi.org/10.5194/egusphere-egu23-11285, 2023.

Idealized high-resolution models show spontaneous aggregation of tropical convection on the beta-mesoscale driven by radiative feedbacks, and the resulting drying implies a potentially important impact on climate sensitivity missing in classic convective parameterization schemes. Here, we combine multiple state-of-the-art observations and reanalysis of the tropical atmosphere and ocean in a 1000 x 700 km region in the tropical Western Pacific warm pool region, along with numerical models and machine learning techniques to demonstrate that in boreal summer, while radiative and surface fluxes act to cluster convection, the convection remains in a random configuration as evidenced by very limited spatial variability in total column humidity. Instead, in the winter/spring period, when the warm pool is displaced southwards, the region lies on the warm pool boundary with stronger north-south surface temperature gradients. Convection usually remains strongly organized in these periods but is interspersed with occasional random episodes. This entails a sudden flipping into the random state associated with the southerly flow anomalies that advect convection and humidity over the cooler sea surface temperature (SST) regions. Observations and models suggest that this advection of humidity is the principal driver of organization and disorganization of convection and that diabatic feedbacks instead always act to try and cluster convection. Results also indicate that when convection is organized, the atmosphere is significantly drier than when convection is random and that the Longwave (LW) clear-sky top of atmosphere flux is significantly larger in the organized state, principally due to the moisture differences between both configurations. The LW all-sky flux difference between both states is less significant compared to the LW clear-sky because it is largely driven by the cloud cover, which, although smaller for the organized state, does not differ significantly. These differences between organized and random convective states, and the role of the diabatic processes in providing forcing for aggregation, mostly reproduce the findings of idealized models. However, this study indicates that in the real tropical atmosphere diabatic forcing is inadequate to lead to aggregation on its own over homogeneous SSTs, and instead, spatial SST gradients and large-scale dynamics are key to driving aggregation and determining its breakup over the warm pool region.

How to cite: Casallas, A., Tompkins, A., and De Vera, M.: No evidence of spatial feedbacks causing convective clustering in the Tropical Western Pacific, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12581, https://doi.org/10.5194/egusphere-egu23-12581, 2023.

Mesoscale Convective Systems (MCSs) represent some of the most intense and destructive thunderstorms in the world. Understanding the physical processes that drive these storms and influence their characteristics is vital for hazard prediction and mitigation. 

A significant amount of research in the “natural laboratory” of West Africa has shown that soil moisture heterogeneity on different spatial scales can influence the location of convective initiation (10s of km) and the intensification of remotely triggered storms (100s of km).

Previous studies have demonstrated that the control of soil moisture state on convective initiation identified in West Africa is also important elsewhere in the world whereas very little is known about the influence of surface conditions on travelling storms in other regions.

In the current work we combine satellite observations and reanalysis data to characterise the impact of pre-storm soil moisture conditions on the atmospheric environment and characteristics of mature storms in seven MCS hotspot regions, West Africa, South Africa, South America, Great Plains, India, China and Australia. 

We observe a clear latitudinal dependence of the coupling signal with distinct differences between regions where convection is predominately driven by monsoonal or frontal dynamics. However our results suggest that in all regions, large-scale (100s of km) soil moisture gradients are having an impact on convection within mature MCSs through moderation of the climatological temperature gradient in the lower atmosphere, which influences factors that favour convection such as shear and convergence.

How to cite: Barton, E., Klein, C., Taylor, C., Marsham, J., and Parker, D.: Response of Mature Storms to Soil Moisture State in Global Hotspot Regions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13504, https://doi.org/10.5194/egusphere-egu23-13504, 2023.

Tropical deep connective clouds (DCCs) have large top of atmosphere (ToA) cloud radiative effects (CREs) in both the shortwave (SW) and longwave (LW), which both have average magnitudes of greater than 100 Wm^-2. Due to the opposite sign of the two components, the overall ToA CRE is generally assumed to average to approximately 0 Wm^-2. Although there are a number of mechanisms that contribute to this balance, the fact that the daytime only SW CRE balances with the LW CRE indicates that the diurnal lifecycle of DCCs is a key component of this balance. Understanding how the diurnal cycle of DCCs influences their CRE is vital for understanding how any changes in their diurnal cycle of these clouds may influence the climate.

A year-long dataset of retrieved cloud properties and derived broadband radiative fluxes has been produced by the ESA Cloud CCI project using temporally highly resolved satellite observations. Using a novel method, we are able to detect and track both isolated DCCs and large, mesoscale convective systems (MCSs) over their entire lifecycle. We explicitly retrieve the cloud properties and CREs of DCCs over Africa, and how these properties change over the lifecycle of approximately 100,000 observed clouds. We find that the mean anvil SW CRE greatly varies depending on the initiation time of day and the lifetime of the DCC, whereas the LW CRE is consistent throughout the diurnal cycle and varies primarily with cloud top temperature.

As a result of our study we can confirm that the mean observed ToA CRE of all DCCs (integrated over area and lifetime) is indeed approximately 0 Wm^-2, but very few DCCs individually have mean CREs near this value. Instead, we find that DCCs occurring during the daytime have a large cooling effect, and those at nighttime have a warming effect, resulting in a bimodal distribution. While MCSs make the largest contribution to the overall effect due to their large areas and lifetimes, because they tend to exist during both nighttime and daytime the overall magnitude of their ToA CREs tend to be smaller than those of isolated DCCs. As a result, factors which influence the diurnal cycle of deep convection – such as changes in CAPE generation or convective inhibition – may have a more important influence on the properties of isolated DCCs rather than larger MCSs.

How to cite: Jones, W., Stengel, M., and Stier, P.: The Diurnal Cycle of the Cloud Radiative Effect of Deep Convective Clouds over Africa from a Lagrangian Perspective, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13744, https://doi.org/10.5194/egusphere-egu23-13744, 2023.

The spatial and temporal variability of air temperature represents the imprint of various meteorological processes, ranging from microscale turbulence to synoptic-scale weather systems. Convective cold pools, formed by evaporatively cooled downdrafts of precipitating clouds, are known to be an important source of mesoscale variability over mid-latitude land. Cold pools both directly perturb the near-surface temperature field and influence variability by controlling larger-scale convective organization. However, their impact on the sub-mesoscale (100 m to 10 km) temperature variability is unclear due to insufficient observational data. Consequently, the validation of sub-mesoscale variability in numerical weather prediction (NWP) and Large-Eddy Simulation (LES) models is also impeded.

In this study, we apply the variogram framework to determine sub-mesoscale temperature variability in observations as well as in idealized and realistic simulations of cold pool events. The basis of the analyses are actual and virtual observations of a dense network of 99 surface measurement stations as part of the Field Experiment on Submesoscale Spatio-Temporal Variability in Lindenberg (FESSTVaL) conducted in eastern Germany during summer 2021. The observed variogram averaged over the lifetime of a cold pool shows enhanced temperature variance at scales between about 1 km and 15 km compared to well-mixed boundary layer conditions, although the magnitude of the perturbation strongly varies for single time steps. Except for the intensification phase, the cold pool generally reduces the temperature variability at sub-km scales compared to pre-cold pool conditions. This suggests smoothing of sub-km temperature gradients by enhanced mixing near the surface as well as damped turbulent surface fluxes.

Idealized cold pool simulations at LES grid spacings capture the overall variogram shape and evolution well but show the largest uncertainty for sub-km scales as compared to the observed variograms. The results are sensitive to the sampled lifetime stage of the cold pool, its environmental conditions, and the model representation of dissipation time scales and turbulent surface fluxes. These findings can help to identify the spatial and temporal scales of variability that are relevant to correctly simulate convective processes in the atmosphere and their interaction with the land surface.

How to cite: Kirsch, B., Grant, L. D., Falk, N. M., Neumaier, C. A., Bukowski, J., Ament, F., and van den Heever, S. C.: Sub-mesoscale temperature variability in observed and simulated convective cold pools, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13748, https://doi.org/10.5194/egusphere-egu23-13748, 2023.

The characteristics of isolated deep convection initiation (DCI) and its relation to topography in the North China area are studies statistically and numerically. The infrared brightness temperature data from satellite Himawari-8 are utilized to identify DCI events in three summers. A total of 2534 DCI events are obtained and their locations show clustering over mountains and hills, suggesting the significance of local topography. Topography is described with elevation and relief amplitude. DCI events and grid boxes are counted. DCI events per grid box increases with elevation and relief amplitude. Among different types of topography, DCI is favored in mountains and hilly areas. Moreover, the morning cloud cover condition also shows notable impact on the relation of DCI and topography. For the regime characterized with less morning clouds (regime one), DCI strongly depends on elevation and relief amplitude, while for the regime with more morning clouds (regime two), topography shows a moderate impact on DCI. The time of DCI events are also recorded, and regime one shows a stronger diurnal variation and a peak occurring 2 hours earlier than that of regime two. The synoptic patterns show the difference of large-scale environment between the two regimes, which can explain their differences in DCI to some extent. To clarify the mechanism of topographic effect in DCI process, quasi-idealized numerical simulation in North China is conducted with WRF. The averaged 6-hourly ERA-Interim reanalysis data, which can maintain the major patterns of large-scale circulations, are inputted into the model as initial and boundary conditions. The elevation and relief amplitude of the study domain is varied in the model. The preliminary result shows that the speed of upscale convection growth changes with elevation and relief amplitude, which indicates that mechanisms involving topography-induced variation of solar heating may exist and need further numerical study. We suggest that special attention should be paid to elevation and relief amplitude (or topography type), as well as morning cloud cover condition when forecasting DCI in the North China area and mountainous areas around the world.

How to cite: Lu, G., Ren, Y., Fu, S., and Xue, H.: The Relationship Between Isolated Deep Convection Initiation and Topography in the North China Area, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13971, https://doi.org/10.5194/egusphere-egu23-13971, 2023.

We investigate microphysical uncertainties in hailstorms using statistical emulation in a single model framework with the objective to disentangle the relative contributions from aerosols, microphysical parameters and environmental conditions to the uncertainty in cloud-, precipitation- and hail-related parameters.

Our selected case study is the Andreas hailstorm on 28 July 2013 in the Neckar Valley and over the Swabian Jura in Southwest Germany. We perform model simulations on cloud-resolving scale with the numerical weather prediction model ICON coupled with the aerosol module ART (ICON-ART). We use a two-moment cloud microphysics scheme with a representation of ice nucleation by dust aerosols.
We generated a perturbed parameter ensemble (PPE) to sample uncertainties in cloud-, precipitation- and hail related parameters. Six parameters from the categories aerosols, microphysics and environmental conditions were jointly perturbed, namely the cloud condensation nuclei (CCN) and ice nuclei (IN) concentrations, the riming efficiency of graupel and hail, the convective available potential energy (CAPE) and vertical wind shear. The defined parameter ranges are based on forecast analysis and literature. We used the maximin Latin hypercube algorithm to distribute the parameters well-spaced in the six-dimensional parameter uncertainty space. For these six parameters, an ensemble of 90 members was generated and in addition a smaller independent ensemble of 45 members serves for validation.

We used the Gaussian process emulation and developed emulators for hail- and precipitation related output variables. To quantify contributions to the uncertainty in the output variables from the perturbed parameters individually as well as interactions between them, a variance-based sensitivity analysis was performed. We will present first results, which reveal the importance of the CCN concentration for controlling the number concentration of hail particles as well as the CCN concentration and environmental conditions for controlling the amount of hail and precipitation in the model. The geographical distribution of hail and precipitation shows a large variety among the ensemble members, with storm tracks shifted further to the north or south compared to the reference simulation. The path of the storm track is thereby mainly controlled by CAPE and the vertical wind shear, however, aerosol parameters seem to be important for the development of multiple storm tracks. 

How to cite: Frey, L., Hoose, C., Kunz, M., Miltenberger, A., and Kuntze, P.: Using statistical emulation to quantify microphysical uncertainties for the Andreas hailstorm in 2013, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14317, https://doi.org/10.5194/egusphere-egu23-14317, 2023.

Convection over the Maritime Continent has large impacts for local extreme weather, as well as for global weather and climate. However, this convection and its impacts are poorly represented in current weather and climate models. This is largely due to complex multi-scale interactions between convection and the ocean, intricate island coastlines and topography, equatorial waves, and larger-scale dynamics such as the Madden-Julian oscillation (MJO). In order to better understand the modulation of convection by the MJO, and its representation in current models, we developed a new modelling suite that couples the Met Office Unified Model to a thermodynamic mixed-layer ocean model with additional corrections to account for ocean dynamics. This allows two-way interactions between the atmosphere and ocean on convection-relevant timescales without the expense of a full dynamical ocean model. We present results from simulations of 10 DJF seasons over the Maritime Continent at grid spacings of 12km (with a mass flux convection scheme) and 2km (without).  

We investigated the modulation of large-scale convective heating and moistening by MJO phase in both models. We show that: 

• The 2km suite has more variability by MJO phase, and this variability is more realistic than that in the 12km suite when compared to observational data; 
• There is more variability in the type of convection (defined by the shape of heating/moistening profiles) between MJO phases in the 2km suite; 
• The dominant variation is between different types of convection in the two suites. 

We also present preliminary results on the modulation by the MJO of basic properties of the cloud field like feature size, and feature isotropy.  

How to cite: Shipley, D., Howard, E., and Woolnough, S.: Modulation of Maritime Continent convection by the MJO: differences between parametrized and explicit convection, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14672, https://doi.org/10.5194/egusphere-egu23-14672, 2023.

Squall lines are the consequence of the interaction of low-level shear with cold pools associated with convective downdrafts. Beyond a critical shear amplitude, squall lines tend to orient themselves at an angle with respect to the low-level shear. While the mechanisms behind squall line orientation seem to be increasingly well understood, uncertainties remain on the implications of this orientation. Roca & Fiolleau 2020 show that long lived mesoscale convective systems, including squall lines, are disproportionately involved in rainfall extremes in the tropics. One may then question whether the orientation of squall lines has an impact on rainfall extremes, and if so, why.

Using a cloud-resolving model, we perform idealized simulations of tropical squall lines by imposing a vertical wind shear in radiative-convective equilibrium. Our results show that precipitation extremes in squall lines are 40% more intense in the critical case and remain 30% superior in the supercritical regime. With a theoretical scaling of precipitation extremes (Muller & Takayabu 2019), we show that the condensation rates control the amplification of precipitation extremes in tropical squall lines, mainly due to its dynamic component. The critical case is not only optimal for squall line orientation, but also for the cloud base velocity intensity of new convective cells.

How to cite: Abramian, S., Muller, C., and Risi, C.: Extreme Precipitation in Tropical Squall Lines, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15870, https://doi.org/10.5194/egusphere-egu23-15870, 2023.

How to cite: Holloway, C., Pope, K., Stein, T., and Jones, T.: Radiation, Clouds, and Self-Aggregation in RCEMIP Simulations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1071, https://doi.org/10.5194/egusphere-egu23-1071, 2023.