The choice of cloud fraction parametrization scheme in weather and climate models significantly influences model performance. Currently in the Met Office’s Unified Model (UM), two different approaches are used to represent sub-grid clouds: a prognostic scheme in the global atmosphere and land (GAL) configuration, and a diagnostic scheme in the regional atmosphere and land (RAL) configuration. Historically, prognostic schemes have performed better at climate resolutions where memory is important, whilst diagnostic schemes have been sufficient for higher resolution numerical weather prediction (NWP). Due to recent increases in computational power, both climate simulations and NWP are being run at higher resolutions. This blurs the boundary between the two configurations, and it would therefore be beneficial to unify a single large-scale cloud fraction scheme which works seamlessly across all resolutions.
A framework for testing candidate cloud fraction schemes has been developed, using high resolution (300m grid spacing) simulations. This grid spacing was chosen as previous comparisons of the UM with observational data show a cloud fraction scheme is required, however most deep convection will be resolved at this resolution and so there is no need for a convection scheme.
We investigate four different cloud fraction schemes: Smith (diagnostic), Bi-Modal (diagnostic), PC2 (prognostic), and a new hybrid cloud scheme combining PC2 for ice and Bi-Modal for liquid. We also look at two cloud microphysics schemes: Wilson & Ballard (single moment), and Cloud AeroSol Interacting Microphysics (CASIM; double moment).
Simulations of shallow cumulus and stratocumulus cloud regimes have been performed over a south UK domain for several case study dates. Through comparisons of rainfall rates and storm cell sizes against 1 km radar observations, it’s been demonstrated that all model configurations overpredict the number of small cells even at this high resolution, particularly GAL9 which also hugely overpredicts rainfall rates. Further comparisons against 3D radar composites provide information on timing and morphology errors. In addition, comparisons against the observations from the Wessex UK Summertime Convection Experiment (WesCon) provide further constraints for single-site model output for parameters including liquid water path and cloud-base height. Together, these comparisons will help to identify the configuration that best represents observed cloud at high resolutions, thereby informing the development of a unified physics configuration.
How to cite: Cottrell, F., Barrett, P., Abel, S., Whitall, M., Williams, K., and Field, P.: Informing the Unification of a Single Cloud Fraction Scheme in the Met Office’s Unified Model , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5755, https://doi.org/10.5194/egusphere-egu25-5755, 2025.
Short-term weather forecasting, especially for extreme events, remains challenging due to the need to effectively combine recent observations with numerical weather predictions. To tackle this challenge, we present RUSH (Rapid Update Short-term High-resolution forecast), an innovative framework designed to provide high-resolution (1 km) precipitation forecasts on a national scale with lead times up to 24 hours. RUSH follows the recent attempts to create fully AI-driven kilometer-scale forecasting systems that completely replace traditional numerical modeling with a combination of machine learning and observational data. Our system employs a Latent Diffusion Model architecture to seamlessly blend information from multiple data sources, including radar composites, satellite observations (SEVIRI bands), and ECMWF's AI-based global forecasting system (AIFS).
The model is conceptually designed to transition from observation-driven predictions in the first few hours to a sophisticated spatial and temporal downscaling of AIFS forecasts at longer lead times. This approach aims to leverage the strengths of both data sources: the high spatial and temporal resolution of observational data for immediate forecasts, and the physically consistent evolution provided by AIFS for longer horizons. By utilizing an end-to-end AI architecture from global to local scale, RUSH not only addresses the computational constraints typically associated with traditional numerical weather predictions but also explores the potential for a new generation of fully data-driven weather forecasting systems.
Our framework processes multi-source input data at different spatial and temporal scales, including radar-derived 30-minute precipitation accumulations, key SEVIRI channels, and selected AIFS forecast fields at 25km resolution. The model's sequence-to-sequence architecture allows for flexible spatial domain handling and probabilistic precipitation forecasting through multiple realizations.
We will present preliminary results from two experimental implementations over different European domains (Italy and Belgium), demonstrating the model's capability to generate rapid-update forecasts and discussing its potential for operational implementation in weather services. The evaluation will focus on precipitation prediction skills across different intensity thresholds and temporal scales, with particular attention to extreme event forecasting. A preliminary comparison with operational limited area models (COSMO-2I and ALARO-AROME) over selected case studies will assess the competitiveness of this fully AI-driven approach against high-resolution numerical weather prediction systems.
How to cite: Franch, G., Tomasi, E., de Kock, S., Angelinelli, M., and Cristoforetti, M.: RUSH: A Novel Fully AI-driven Framework for Seamless Integration of Observations and Global AI Forecasts in Short-term Weather Prediction, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16651, https://doi.org/10.5194/egusphere-egu25-16651, 2025.
Anemoi is an open-source framework co-developed by ECMWF and several European national meteorological services to build, train, and run data-driven weather forecasts. Its primary goal is to empower meteorological organisations to train machine learning (ML) models using their data, simplifying the process with shared tools and workflows.
Designed for modularity and flexibility, Anemoi offers key components for efficient data-driven forecasting. The framework is organised into distinct Python packages covering the entire machine learning lifecycle—from the creation of customised datasets from diverse meteorological sources to the development and training of advanced deep learning graph models. Once a model is trained, Anemoi enables users to run it for inference, using the outputs of physics-based NWP analyses or ensembles as initial conditions, while maintaining comprehensive lineage tracking.
Anemoi has already been applied in experimental operational forecasting, including ECMWF’s Artificial Intelligence Forecasting System (AIFS). It has supported models utilising stretched grid and limited-area configurations. These applications demonstrate Anemoi’s potential to enhance forecasting accuracy by integrating ML techniques into existing systems.
More than just a technical framework, Anemoi represents a collaborative effort among meteorological services, researchers, and technologists, fostering knowledge exchange and innovation.
How to cite: Prieto Nemesio, A., Nerini, D., Wijnands, J., Nipen, T., and Chantry, M.: Anemoi: A New Collaborative Framework for Data-driven Weather Forecasting, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19431, https://doi.org/10.5194/egusphere-egu25-19431, 2025.
Precipitation nowcasting, which entails high-resolution forecasting of precipitation events within 1–2 hours, is significant to daily life and professional activities. Nevertheless, accurate short-term precipitation forecasting remains a considerable challenge at present. Traditional numerical weather prediction, which relies on intricate physical equations to simulate the Earth's atmospheric state, necessitates substantial computational resources and frequently yields lower accuracy for small-scale forecasts, thereby failing to meet the demands of precipitation prediction in complex regions. Most deep learning methodologies concentrate exclusively on the spatiotemporal prediction of a singular precipitation variable, thereby neglecting the dynamic spatiotemporal relationships between precipitation and other meteorological data within the meteorological system. Moreover, due to the rapid pace of climate change, long-term time series data is often inadequate for accurately addressing precipitation forecasting for extreme weather events, since past meteorological time series data may not accurately reflect the current atmospheric conditions. There is an urgent need to rely on short-term time series for prediction tasks. However, most current methods that rely on short-term time series for prediction perform poorly in forecasting moderate to heavy precipitation events. Inspired by spatiotemporal information transformation schemes, we introduce a spatiotemporal information(STI) transformation equation from chaotic dynamics into the field of computer vision and develop a neural network model framework based on spatiotemporal information transformation. This framework maps high-dimensional spatial information to the temporal information of future precipitation information, thereby facilitating the integration of dynamic spatiotemporal relationships between various meteorological data and precipitation, and enabling the mutual transformation of spatiotemporal information for enhanced forecasting accuracy. Furthermore, we propose an adaptive gradient loss function designed to improve the model's sensitivity to learning moderate-intensity precipitation. This research utilizes the US SEVIR dataset for training and testing, which encompasses data such as satellite visible light, infrared temperature, humidity, and cloud precipitation while employing multiple meteorological data for precipitation forecasting over the subsequent hour. We selected the Structural Similarity Index, Peak Signal-to-Noise Ratio, False Alarm Rate, Critical Success Index, and Heidke Skill Score as both quantitative and qualitative evaluation metrics. Experimental results demonstrate that the STI framework reduces the model's error in moderate to heavy precipitation events, making the model more sensitive to severe rainfall events. Furthermore, when the STI framework is integrated into other deep learning models and retrained, it further enhances their precipitation prediction accuracy. This finding indicates that the STI framework effectively captures the dynamic spatiotemporal relationships between various meteorological and precipitation data.
How to cite: Hu, J., Liu, D., Huang, X., and Wu, X.: Spatiotemporal Information Transformation for Precipitation Nowcasting Using Multi-Meteorological Factors, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14517, https://doi.org/10.5194/egusphere-egu25-14517, 2025.
Lightning, hail, severe turbulence and severe icing associated with cumulonimbus clouds (Cb) present a significant safety hazard to air traffic and can impact the comfort and timeliness of a flight. The World Area Forecast System (WAFS) facilitates safe and efficient flight planning by providing global forecasts of key meteorological hazards. The next generation of WAFS will provide probabilistic forecasts of these hazards, including cumulonimbus clouds.
At the Met Office, these forecasts are currently made using three simple threshold tests applied to parameters from MOGREPS-G, a global NWP ensemble. These thresholds are used as a proxy for the occurrence of cumulonimbus clouds in the NWP data.
In this work, a series of deep learning models have been trained to predict the occurrence of cumulonimbus in global satellite observations using a wider set of parameters from the control member of MOGREPS-G. The purpose of the training is for the deep learning model to learn the representation of a cumulonimbus in the NWP data in a supervised manner. The model predictions are then applied to the whole ensemble to produce a probability forecast of cumulonimbus occurrence.
A range of loss functions were used during model training and verification to account for spatial information at a range of scales. Different loss functions were also used to enhance the reward for correct forecasts of the relatively rare cumulonimbus clouds.
Some of the trained models are shown to have greater skill than a baseline using the threshold test method. The model characteristics change depending on the choice of loss function used during training.
Further work is needed to explore how to make predictions at a range of lead times and how to use inputs from the whole ensemble.
How to cite: Creswick, A.: A deep learning approach for probabilistic forecasts of cumulonimbus clouds from NWP data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9783, https://doi.org/10.5194/egusphere-egu25-9783, 2025.
Radar nowcasting methodologies have evolved from traditional optical flow and extrapolation techniques to advanced deep learning algorithms. However, accurately modeling growth and decay processes remains a significant challenge. This study explores spatio-temporal statistical models inspired by physics-based stochastic partial differential equations (SPDEs). Specifically, the solution to the advection-diffusion PDE is framed as a vector autoregressive process with coloured noise, characterized by non-uniform spectral properties.
We investigate the stochastic component using Gaussian Processes (GPs) and Gauss Markov Random Fields (GMRFs), evaluating covariance structures such as exponential, squared exponential, and dynamically weighted covariance and precision matrices. Nowcasts employing state-dependent GPs and GMRFs are assessed over lead times ranging from 15 minutes to 2 hours. The approach is tested on simulated data and UK precipitation events from the Met Office Nimrod system, focusing on a 200 km × 200 km region. Training data spans January 2014 to December 2020, with observational dimensions on the order of 10^4. To enable computationally efficient Bayesian inference, we utilize sparse matrix methods and Laplace approximations.
How to cite: Atureta, V., Siegert, S., and Challenor, P.: Exploring spatiotemporal vector autoregressive models for radar nowcasting, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17194, https://doi.org/10.5194/egusphere-egu25-17194, 2025.
Precipitation nowcasting plays an essential role in operational weather forecasting services. Sudden precipitation events have significant socio-economic impacts, including natural disasters like flash floods. This challenge is becoming increasingly critical as climate change alters weather patterns and the frequency of extreme weather events continues to increase.
Over the last decade, radar observations, offering high temporal and spatial resolution, have facilitated the development of machine learning methods for precipitation nowcasting. Once trained, these methods are well suited to processing large datasets with low latency, especially in a real-time context. Recent advances in the field of nowcasting have focused on optimizing model architectures, improving loss functions for imbalanced data, and integrating multivariate inputs, including radar and satellite observations.
This study explores some critical hyperparameters, such as temporal context length, edge effect during training, influence of the output horizons prediction, and convolution kernel size. To do this, we investigate the performance of several models, including both machine learning approaches from different families, in particular SmaAt-Unet, ConvLSTM , and DGMR (trained on UK rains) , as well as non-machine learning methods such as STEPS. An eleven years consistent radar precipitation dataset covering the Paris region was set up from Météo-France mosaic. Nine years were used for training machine learning models, and two years were reserved to evaluate the models’ performances. To assess the model in different weather conditions, the data set is divided into four groups with distinct characteristics corresponding to various meteorological phenomena. To ensure consistent evaluation, we evaluated the models on the same two-year test dataset, focusing on three criteria, namely: spatial consistency (Pearson correlation coefficient), location accuracy (CSI), and precipitation intensity (MSE).
Our analysis reveals that machine learning models consistently outperform traditional optical flow methods, with notable variations in performance across timescales and rainfall intensities. We also highlight that performance is nearly identical for all models in the presence of stratiform rain, while there are substantial differences in the convective rain group. Additionally, we show that for deep learning models, considering edge effects during training prevents the propagation of inevitable errors and helps avoid the appearance of ghost rain cells at the edges of the map. Furthermore, we show that the size of the kernels of the first layers plays an important role and must be large enough to allow correlation between distant pixels.
Finally, our study provides guidelines for the development of precipitation nowcasting models.
How to cite: Guigal, B., Chazottes, A., Barthès, L., Viltard, N., Le Bouar, E., Moreau, E., and Mallet, C.: Methodological Focus on Hyperparameters for Different Rain Nowcasting Models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4138, https://doi.org/10.5194/egusphere-egu25-4138, 2025.
Rainfall nowcasting algorithms rely primarily on extrapolation, where recent radar rainfall observations are projected forward in time based on a motion field that is determined with past data. While additional (stochastic) processes may be incorporated, as is for example done in the pySTEPS models, extrapolation remains the fundamental mechanism. Although the motion field estimates are robust, they assume a steady state in the motion field for the future. This assumption can face significant challenges in maintaining accuracy over time, especially during convective weather events characterized by rapid changes in precipitation patterns and their movement.
In this study, we focus on three objectives: 1) identifying the current errors and uncertainties in the steady-state motion field derivation using pySTEPS, 2) the construction of a dynamic motion field derivation approach using a new deep-learning model, MotioNNet, and 3) the development of ensemble motion fields for MotioNNet. MotioNNet is a U-Net based deep-learning architecture, which uses the past radar images (five in this study) in combination with the estimated static motion field from pySTEPS to estimate the deviation from the provided static motion field per grid cell with increasing lead time. For the ensemble generation in MotioNNet, we tested probabilistic techniques such as SpatialDropout and Monte Carlo dropout.
We trained and tested our model on C-band weather radar data from the Royal Netherlands Meteorological Institute (KNMI), using 10,000 rainfall events. These events were selected to include cases with both intense precipitation and significant motion errors. Our results show that the static motion field approach results in average motion field errors of 1 – 3 km h-1 at the start of the forecast and increases to 4 – 8 km h-1 (on average, and locally sometimes much higher) at a lead time of 90 minutes. The dynamic motion field estimates of MotioNNet improve the motion prediction accuracy by approximately 13%. The improvement is much higher for structured and stable events (up to 45%), but almost negligible for localized thunderstorm events. The results of the ensemble construction in MotioNNet indicate that MotioNNet is capable of adding perturbations in space where most uncertainty takes place, especially for structured and stable events. This is an advantage compared to the spatially uniform approach of pySTEPS. However, the spread of the ensembles is still underestimated, even more so than with pySTEPS, indicating that the uncertainty in the forecast is not yet well represented.
We conclude that the hybrid MotioNNet approach can substitute and enhance parts of the motion field module in pySTEPS. MotioNNet refines initial motion field estimates, rather than replacing them, which leads to a modular approach that fits well in the overall pySTEPS framework. We expect that the dynamic motion field approach from MotioNNet will aid in further enhancing the predictability of (high-intensity) rainfall events for short lead times, especially for structured events where motion errors currently play a role in the forecast error.
How to cite: Imhoff, R., Blázquez Martín, D. A., Taormina, R., and Schleiss, M.: Data-driven dynamic motion field generation for rainfall nowcasting, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6014, https://doi.org/10.5194/egusphere-egu25-6014, 2025.
Machine learning-based models are rapidly transforming medium-range weather forecasting. The European Centre for Medium-Range Weather Forecasts (ECMWF) has developed the Artificial Intelligence Forecasting System (AIFS), a state-of-the-art data-driven model combining a graph neural network encoder-decoder with a sliding window transformer processor. Trained on ECMWF's ERA5 re-analysis and operational numerical weather prediction analyses, AIFS demonstrates exceptional deterministic forecast skill across upper-air variables, surface weather parameters, and tropical cyclone tracks.
Building on this foundation, ECMWF has introduced AIFS-CRPS, a probabilistic extension of AIFS designed for ensemble forecasting. AIFS-CRPS is obtained by training a stochastic model with the Continuous Ranked Probability Score (CRPS) as its loss function. It addresses uncertainties and generates highly skilful probabilistic forecasts. For medium-range timescales, AIFS-CRPS matches or outperforms ECMWF’s physics-based Integrated Forecasting System ensemble across key variables and lead times.
This presentation will highlight recent advancements in deterministic and probabilistic forecasting with AIFS, showcasing its operational readiness and its potential to redefine medium-range forecasting at ECMWF.
How to cite: Hahner, S. and the AIFS-Team: The AIFS: ECMWF’s data-driven weather forecasting system, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12077, https://doi.org/10.5194/egusphere-egu25-12077, 2025.
In this talk, we consider a mathematical model of cloud physics that consists of the Navier-Stokes equations coupled with the cloud evolution equations for water vapor, cloud water, and rain. In this model, the Navier-Stokes equations describe weakly compressible flows with viscous and heat conductivity effects, while microscale cloud physics is modeled by the system of advection-diffusion-reaction equations. We aim to explicitly describe the evolution of uncertainties arising from unknown input data, such as model parameters and initial or boundary conditions. The developed stochastic Galerkin method combines the space-time approximation obtained by a suitable finite volume method with a spectral-type approximation based on the generalized polynomial chaos expansion in the stochastic space. The resulting numerical scheme yields a second-order accurate approximation in both space and time and exponential convergence in the stochastic space. Our numerical results demonstrate the reliability and robustness of the stochastic Galerkin method. We also use the proposed method to study the behavior of clouds in certain perturbed scenarios, for example, the ones leading to changes in macroscopic cloud patterns as a shift from hexagonal to rectangular structures.
How to cite: Chertock, A.: Stochastic Galerkin method for cloud simulation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1939, https://doi.org/10.5194/egusphere-egu25-1939, 2025.
I will introduce a flux globalization-based well-balanced path-conservative central-upwind scheme on Cartesian meshes for the two-dimensional (2-D) two-layer thermal rotating shallow water equations. The scheme is well-balanced in the sense that it can exactly preserve a variety of physically relevant steady states. In the 2-D case, preserving general "moving-water" steady states is difficult, and to the best of our knowledge, none of existing schemes can achieve this ultimate goal. The proposed scheme can exactly preserve the 𝑥- and 𝑦-directional jets in the rotational frame as well as certain genuinely 2-D equilibria. Numerical experiments demonstrate the performance of the proposed scheme in computationally non-trivial situations: in the presence of shocks, dry areas, non-trivial topographies, including discontinuous ones, and in the case of hyperbolicity loss. The scheme works equally well in both the 𝑓-plane and beta-plane frameworks.
How to cite: Kurganov, A., Cao, Y., Liu, Y., and Zeitlin, V.: Flux Globalization-Based Well-Balanced Path-Conservative Central-Upwind Scheme for Two-Dimensional Two-Layer Thermal Rotating Shallow Water Equations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2053, https://doi.org/10.5194/egusphere-egu25-2053, 2025.
The Asian Summer Monsoon Anticyclone (ASMA) plays a critical role in trapping, transporting, and redistributing water vapour in the upper troposphere and lower stratosphere, particularly into the extratropical lower stratosphere. Comparison of ERA5 reanalysis data with remote sensing data and simulations with the model ICON-CLM in convection-parameterized (12 km grid spacing) and convection-permitting (3.3 km) setups indicate that the transport into the ASMA is overestimated in ERA5 over the Tibetan plateau (Singh & Ahrens 2023). This presentation critically discusses the water vapour transport into the upper-troposphere/lower-stratosphere by deep convective events over the Tibetan plateau and the Himalayas – an area identified as hotspot for troposphere-stratosphere exchange (Škerlak et al. 2014) using convection-parameterized reanalysis data. Our investigations use a decade-long ICON-CLM climate-like simulation (Collier et al. 2024) performed as a contribution to the CORDEX flagship pilot study Convection-Permitting Third Pole (CPTP).
References
Collier, E., N. Ban, N. Richter, B. Ahrens, D. Chen, X. Chen, H-W. Lai, R. Leung, L. Li, T. Ou, P.K. Pothapakula, E. Potter, A. F. Prein, K. Sakaguchi, M. Schroeder, P. Singh, S. Sobolowski, S. Sugimoto, J. Tang, H. Yu, C. Ziska: The First Ensemble of Kilometre-Scale Simulations of a Hydrological Year over the Third Pole. Clim Dyn. https://doi.org/10.1007/s00382-024-07291-2, 2024
Singh, P., B. Ahrens: Modeling Lightning Activity in the Third Pole Region: Performance of a km-Scale ICON-CLM Simulation. Atmosphere, 14(11), 1655, DOI: 10.3390/atmos14111655, 2023
Škerlak, B., M. Sprenger, and H. Wernli: A global climatology of stratosphere–troposphere exchange using the ERA-Interim data set from 1979 to 2011. English. Atmospheric Chemistry and Physics 14 (2), 913–937. doi: 10.5194/acp-14-913-2014, 2014
How to cite: Ahrens, B. and Singh, P.: Moist convection and tracer transport in and out of the Asian Summer Monsoon Anticyclone, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20360, https://doi.org/10.5194/egusphere-egu25-20360, 2025.
The middle and lower reaches of the Yangtze River (MLYR) suffers from extreme precipitation (EP) during summer, which has a huge impact on human society and ecosystem. However, the large spreads among climate models hinder their application in future risk assessment. In this work, four typical synoptic patterns (SPs) triggering EP over MLYR are identified based on the clustering algorithm. And we found a significant linear correlation between the CMIP6 (sixth phase of Coupled Model Intercomparison Project) models’ ability to reproduce the observed typical SPs in present-day climate and the projected future changes of EP over MLYR. Then we proposed an emergent constraint method for EP projections based on this linear correlation and the observed SPs. Using this method, the model spread is evidently narrowed, which increases the credibility of projected future EP changes.
How to cite: Hu, Y., Lin, Y., Bao, J., and Deng, Y.: Constraining Future Changes in Extreme Precipitation Using Typical Synoptic Patterns, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2650, https://doi.org/10.5194/egusphere-egu25-2650, 2025.
This article introduces the five-year research plan of the project and the preliminary progress made over the past two years: 1. Implemented tracking observation experiments on the Mei-Yu frontal extreme precipitation associated in the middle and lower reaches of the Yangtze River for the years 2023 and 2024; 2. Investigated the triggering and maintenance mechanisms of extreme precipitation related to multi-scale interactions and associated thermodynamic conditions; 3. Conducted studies on the microphysical structure and evolution simulation of extreme precipitation. To be specific, the mechanism of low-level jet formation is analyzed during the rainy season in the Yangtze River Basin in 2024.
How to cite: Cui, C. and Wang, B.: Preliminary results on the Mei-Yu Frontal Heavy Rainfall Tracking Observation Experiment and Related Studies, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3219, https://doi.org/10.5194/egusphere-egu25-3219, 2025.
The present study assesses the simulated precipitation and cloud properties using three microphysics schemes (Morrison, Thompson, and MY) implemented in the Weather Research and Forecasting model. The precipitation, differential reflectivity (ZDR), specific differential phase (KDP) and mass-weighted mean diameter of raindrops (Dm) are compared with measurements from a heavy rainfall event that occurred on 27 June 2020 during the Integrative Monsoon Frontal Rainfall Experiment (IMFRE). The results indicate that all three microphysics schemes generally capture the characteristics of rainfall, ZDR, KDP, and Dm, but tend to overestimate their intensity. To enhance the model performance, adjustments are made based on the MY scheme, which exhibited the best performance. Specifically, the overall coalescence and collision parameter (Ec) are reduced, which effectively decreases Dm and makes it more consistent with observations. Generally, reducing Ec leads to an increase in the simulated content (Qr) and number concentration (Nr) of raindrops across most time steps and altitudes. With a smaller Ec, the impact of microphysical processes on Nr and Qr varies with time and altitude. Generally, the autoconversion of droplets to raindrops primarily contributes to Nr, while the accretion of cloud droplets by raindrops plays a more significant role in increasing Qr. In this study, it is emphasized that even the precipitation characteristics could be adequately reproduced, accurately simulating microphysical characteristics remains challenging and it still needs adjustments in the most physically based parameterizations to achieve more accurate simulation.
How to cite: Zhou, Z.: An evaluation and improvement of microphysical parameterization for a heavy rainfall process in Meiyu season, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1816, https://doi.org/10.5194/egusphere-egu25-1816, 2025.
In this study, the microphysical characteristics of summer and winter liquid rainfall are analyzed by 4 Parsivel sites in Hubei Province in the middle reaches of the Yangtze River during 2015-2018. The possible reasons for summer and winter DSD differences are also discussed. The main conclusions are summarized as follows:
(1) Hubei Province is dominated by stratified rainfall in winter, while summer includes convective, stratified, and mixed rainfall. Compared with winter, the average rain rate and Dm in summer are larger, the number concentration Nw is relatively smaller, while difference between δM is very small. The PDF distribution of Dm peak value are about 1.0 mm both in summer and winter, and the Dm data is skewed to the right while the Nw show the opposite.
(2) With increasing rain rate, the Dm increases in both summer and winter. For rain rate R < 2 mm h-1, there are larger Dm and smaller Nw in summer than that in winter, while for the rain rete R > 2 mm h-1 shows the opposite.
(3) There are differences in the μ-λ and Z-R relationships between summer and winter in the middle reaches of the Yangtze River. The relationships also different from those in the lower reaches of the Yangtze River.
(4) The middle reaches of the Yangtze River are mainly influenced by the warm and humid air transport originates in the subtropical South Indian Ocean. In summer, the convective rainfall raindrops grow by collision–coalescence mechanism, and the break-up mechanism also plays an important role which makes smaller diameter. The ice particles could grow sufficiently and fall to the ground with enough time by the accretion mechanism in winter.
In summary, this study gives an insight into the seasonal characteristics of rainfall microphysics in summer and winter, which are very useful for radar QPE and numerical forecasting models modify in the middle reaches of the Yangtze River. However, due to the limitation of observation data, more types of observation data and numerical models simulation should be included to understand the mechanism of the microphysical processes for future reach.
How to cite: Wang, B. and Fu, Z.: The seasonal characteristics of summer and winter raindrops size distribution in Central China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3232, https://doi.org/10.5194/egusphere-egu25-3232, 2025.
Convective clouds during the Mei-yu season contribute significantly to the total rainfall and related disasters over the middle and lower reaches of the Yangtze River in China. Studying the effects of aerosols on convective clouds is of great importance to weather and climate research. However, there are still many open questions to address. This study investigated the effects of aerosol on convections with different cloud geometrical thickness (CGT) bins during the 2018 Mei-yu season, which lasted for 17 days from 18 June to 5 July. Contrasting aerosol effects on shallow and deep convective clouds were revealed by means of anthropogenic aerosol experiments in the Weather Research and Forecasting model with Chemistry (WRF-Chem). Specifically, increased anthropogenic aerosols lead to a 9% reduction in total rainfall and a 7.17% decrease in convection occurrences during the Mei-yu season. After adopting a methodology that stratifies the convective clouds by fixing the CGT, we found that increasing aerosols suppress shallow convections with CGT less than 4 km and invigorate deep convections with CGT greater than 4 km. Increased aerosols enhance the scattering of shortwave radiation, resulting in cooling of the surface air and increasing the stability of the regional lower atmosphere, potentially suppressing shallow convection. Meanwhile, in deep convection, with its stronger updraft and more latent heat, convective invigoration occurs under polluted conditions due to the aerosol-related microphysical and dynamical responses. Considering the high-humidity environment during the Mei-yu season, additional relative humidity tests show that the competing aerosol effects come from convective core invigoration and convective periphery processes which enhance evaporation and dissipation, demonstrating relative humidity is a critical factor in maintaining the net aerosol effects on convections. These results contribute to a better understanding of the effects of anthropogenic aerosols on convections during the Mei-yu season and the competing effects of aerosols depending on the ambient environmental conditions.
How to cite: liu, L.: Contrasting aerosol effects on shallow and deep convections during the Mei-yu season in China , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1775, https://doi.org/10.5194/egusphere-egu25-1775, 2025.
Gridscale forecasts of surface weather delivered by operational global NWP suffer from biases which depend strongly on the weather situation and on geographical factors. Such biases also plague re-analyses, such as ECMWF’s ERA5, as operational models are the engines of those re-analyses. This presentation will itemise a number of different gridscale biases identified through a conditional verification exercise in which millions of station measurements were compared with short range Control run forecasts of the ECMWF operational ensemble. We will postulate what physical reasons might underpin these biases. There is for example a strong dependence of rainfall forecast bias on model near surface relative humidity, which seems to relate to the handling of droplet evaporation and other cloud physics processes. All such errors can in principle be addressed via ECMWF’s “ecPoint” post-processing approach; indeed the conditional verification activity here was managed via ecPoint calibration software. The resulting corrections will be illustrated.
Whilst data-driven AI models are currently delivering better predictions of the synoptic pattern than classical physics-based global NWP, the fact remains that those AI models are generally using unadjusted re-analyses for training, and so the situation-dependant biases will clearly put a cap on skill attainable by them for surface weather parameters, even when the forecast synoptic pattern is ‘perfect’. Some ECMWF views on how to overcome this barrier, to deliver even better predictions, will be very briefly presented.
How to cite: Hewson, T.: Using Conditional Verification to describe Situation-dependant Model Biases for Surface Weather – Applications and Implications, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18177, https://doi.org/10.5194/egusphere-egu25-18177, 2025.
Cloudburst is a new post-processing system at the Met Office, leveraging Amazon Web Services (AWS) to provide a route for easy deployment of post processing pipelines allowing for the generation of replacement data as legacy sources are retired. The focus is primarily on generating diagnostics where consistency across multiple variables is required to provide a coherent weather narrative. Thus far all provided parameters have utilised the Met Office’s global and UK deterministic models but the system is made to be versatile so ensemble forecasts could be used in future.
The diagnostics generated in Cloudburst use code from the open-source IMPROVER (Integrated Model post-PROcessing and VERification) repository, which offers a versatile toolbox of post-processing plugins. By enhancing this toolbox with new plugins and functionalities, we promote the reusability of post-processing components, fostering collaboration between the Blended Probabilistic Forecast team and the Cloudburst team. Any code added to the IMPROVER repository by Cloudburst is made as adaptable as possible so that it could be applied to deterministic forecasts or ensemble members.
In this presentation we will describe the first diagnostic generated within Cloudburst: precipitation type. This diagnostic was required to be consistent with the rain and snow rate so these were also rederived from the precipitation rate. Precipitation type, along with rain and snow rates, have now been operationalised and the data sent downstream for customers.
How to cite: Spelman, M.: Cloudburst: A Platform for Running Post-Processing Workflows at the Met Office, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5870, https://doi.org/10.5194/egusphere-egu25-5870, 2025.
Title: Probabilistic Postprocessing of Hourly Precipitation Ensemble Forecasts Using UNet
Authors: Marcos Esquivel-González, Albano González, Juan Carlos Pérez, Juan Pedro Díaz, Pierre Simon Tondreau
Affiliation of authors: Grupo de Observación de la Tierra y la Atmósfera (GOTA), Avenida Astrofísico Francisco Sánchez, s/n, La Laguna, 38200, Canary Islands, Spain
Abstract: Reliable precipitation forecasting is crucial in sectors like public safety, agriculture and water management. Numerical Weather Prediction (NWP) models, which form the backbone of modern forecasting, are prone to errors due to their limitations and the chaotic behavior of equations, requiring postprocessing to improve accuracy and quantify uncertainties. Thus, this study evaluates probabilistic postprocessing models tailored for the Canary Islands, with the aim of enhancing Weather Research and Forecasting (WRF) ensemble forecasting accuracy in hourly precipitation forecast. UNet-based models were explored using two approaches, one incorporating the full set of km-scale convection-permitting ensemble forecast simulations (25) and another applying dimensionality reduction via Principal Component Analysis (PCA) and feature selection methods. These models were compared to traditional benchmarks like the Censored Shifted Gamma Distribution (CSGD) with Ensemble Model Output Statistics (EMOS) and the Analog Ensemble method. In the analysis of the results, not only the reliability of the predictions for the set of available meteorological stations was considered, but also the generalization capacity of the UNet models to obtain precipitation predictions for the whole region.
In general, UNet models outperformed traditional approaches. The UNet with PCA excelled in probabilistic and deterministic metrics but struggled in regions without weather station data. Conversely, the UNet with feature selection, while slightly less accurate overall station locations, showed better generalization to unseen locations, maintaining consistent performance across the region and reducing computational demand. Additionally, the Integrated Gradients technique, an interpretability method that quantifies the contribution of each input feature to a model’s predictions by analyzing gradients, was employed to evaluate the impact of input variables on model performance. This analysis revealed that the integration of digital terrain elevation data significantly contributed to the UNet's outputs, underscoring the importance of topographic data in rainfall prediction.
How to cite: Esquivel González, M., González, A., Pérez, J. C., Díaz, J. P., and Tondreau, P. S.: Probabilistic Postprocessing of Hourly Precipitation Ensemble Forecasts Using UNet, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9837, https://doi.org/10.5194/egusphere-egu25-9837, 2025.
EGU abstract 2025
NP5.2 EDI: Advances in statistical post-processing, blending, and verification of deterministic and probabilistic forecasts
The challenge of uncertain observations: Probabilistic verification of decadal predictions with high temporal resolution
Verification plays an important role in the evaluation and the development of climate predictions. With new developments in the field and ever larger availability of computational resources, temporal high resolutions become an option. But we often do not make use of the full temporal distribution and much too often we still rely on temporal averages to reduce the dimensionality of the data to make a verification with common metrics manageable. One of the reasons is the challenge how to verify in an understandable manner probabilistic model predictions with probabilistic, uncertain observations.
Tools for probabilistic verification are available, like the Continuous Rank Probability Score (CRPS), but are often defined for perfect observations. Furthermore, many tools are for the wider community hard to comprehend and are as such often not applied. This poses the question on how to verify predictions on the basis of current imperfect usage of metrics within the field and how to communicate prediction skill in general.
This contribution will address two main approaches and apply it to the comparison between a decadal prediction and the associated projection (historical simulation), with an assimilation simulation as an observational reference. In the first we will ask how to communicate verification results for a wider community. For this we will look at framing the skill as yearly matchups between the two model results. Basing on the Integrated Quadratic Distance each year determines which model result is closer to the observations and the years how often one result was better than the other leads to our verification result. In a second approach it will be discussed to find modifications of some of the most applied metrics in our field, Anomaly Correlation (ACC) and Root-Mean Square (RMS), towards uncertain observations. While these metrics are imperfect, they allow an easy communication for people already applying them. Differences in their interpretation will be discussed, giving us insights about how uncertain observations change our understanding of a good prediction. We address also significance estimation and it will be highlighted why we need to find easy comprehendible approaches to handle uncertain observations in the future.
How to cite: Düsterhus, A.: The challenge of uncertain observations: Probabilistic verification of decadal predictions with high temporal resolution, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8034, https://doi.org/10.5194/egusphere-egu25-8034, 2025.
Raw forecasts, be they weather or hydrological, suffer from the inevitable errors stemming from either model structures or initial conditions estimation. With forecasting being a critical component in addressing challenges in flood control, reservoir and hydropower operation, and other fields related to the environment, energy and public safety, improving forecasting skill is increasingly necessary. Post-processing methods can help in this regard and can help improve forecast accuracy and reliability. Non-Homogeneous Gaussian Regression (NGR) and Bayesian Model Averaging (BMA) are the two most commonly used methods when it comes to post-processing probabilistic forecasts, and they have shown to be similarly efficient in many studies. For case studies where there are several distinct forecasts for one single observation, NGR risks losing information on uncertainty by aggregating the forecasts even though it accounts for heteroscedasticity. BMA, on the other hand, evaluates distinct model components and utilizes them accordingly, while assuming all the forecasts are alike in their under/overdispersion. This work introduces a mixed NGR-BMA approach for calibrating air temperature forecasts with lead-times of 1-10 days where the forecasts are first processed with NGR and then corrected once more by BMA according to a priori information on the skill of model components. This way, the upsides of each method is maintained through post-processing. The results generally show that the higher the lead-time, the more the proposed method outperforms either BMA or NGR taken individually.
How to cite: Oghbaei, B. and Arsenault, R.: Using Non-Homogeneous Gaussian Regression to incorporate heteroscedasticity when post-processing air temperature forecasts by Bayesian Model Averaging, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10245, https://doi.org/10.5194/egusphere-egu25-10245, 2025.
In evaluating multivariate probabilistic forecasts predicting vector quantities such as a weather variable at multiple locations or a wind vector, an important step is the assessment of their calibration and reliability. Here, we focus on the Gaussian Box ordinate transform (BOT), which is appropriate if the forecasts and observations are multivariate normal. The BOT is based on the Mahalanobis distance of the observation vector and the estimated Gaussian mean and asymptotically standard uniform if the forecasts and the observation are drawn from the same multivariate Gaussian law. However, for small ensemble sizes combined with high dimensionality, deviation from uniformity is substantial even for reliable forecasts, resulting in hump-shaped or triangular BOT histograms. To circumvent this problem, we derive an ensemble size and dimension-dependent fair version of the Gaussian BOT, where the uniformity holds for any combination of these parameters. With the help of a simulation study, first, we assess the behaviour of the fair BOT for various dimensions, ensemble sizes, and types of calibration misspecification. Then, using ensemble forecasts of vectors consisting of multiple combinations of upper-air weather variables, we demonstrate the usefulness of the fair BOT when multivariate normality is only an approximation.
*Research was supported by the Hungarian National Research, Development and Innovation Office under Grant No. K142849.
How to cite: Baran, S. and Leutbecher, M.: Fair Box ordinate transform for multivariate Gaussian forecasts, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15060, https://doi.org/10.5194/egusphere-egu25-15060, 2025.
Bias correction techniques are often used as effective and reliable approaches to improve the representation of current and past conditions in climate models. This study aims to evaluate the performance of Quantile Delta Mapping (QDM) as a bias correction method for daily precipitation simulations from climate models: the Icosahedral Nonhydrostatic Model (ICON), the Regional Climate Model COSMO-CLM (CCLM), and the Regional Climate Model (REMO) at a spatial resolution of 3 km over Germany. The dataset consists of historical observations from HYRAS and climate model simulations between 1961 and 1990, split into a calibration period (1961–1980) and an independent validation period (1981–1990). To assess performance, we considered four aspects: 1) sequence of events, 2) distribution of values, 3) spatial structure, and 4) visual inspection of distance metrics, ultimately providing an integrative qualitative ranking across these aspects. Performance metrics included correlation, Nash-Sutcliffe efficiency (NSE), Kling-Gupta efficiency (KGE), and error metrics such as BIAS, mean square error (MSE), and root mean squared error (RMSE). Additional metrics considered were the Kolmogorov-Smirnov (KS) statistic, Perkins Skill Score (Sscore), probability density function (PDF), 80th, 90th, and 95th percentiles, and spatial autocorrelation. As a preliminary assessment of the simulated precipitation from ICON, results show only slight improvements in the time and spatial distribution of precipitation metrics. For example, the KS statistic improved from 0.0314 to 0.0190, while the Sscore improved from 0.0314 to 0.0195 when comparing HYRAS vs. ICON raw and HYRAS vs. ICON bias-corrected using QDM, respectively. Therefore, limited improvement is expected from bias correction when the climate model already performs well, whereas significant improvements can be achieved when the climate models perform only acceptably.
How to cite: Espitia, E., Díaz Esteban, Y., Haupt, M., Adakudlu, M., Vlachopoulos, O., and Xoplaki, E.: A multi-criteria evaluation of the performance of bias correction using Delta Quantile Mapping for simulated precipitation over Germany, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8427, https://doi.org/10.5194/egusphere-egu25-8427, 2025.
Accurate forecasting of solar power generation is crucial for grid operators, as location-dependent photovoltaic (PV) installations exhibit diverse production patterns. The need for high temporal and spatial resolution, combined with the inherent variability of PV outputs, presents significant challenges for forecasting and post-processing across different time horizons. This study addresses these challenges in post-processing optimal point forecasts for PV sites across multiple forecasting ranges, with the aim of providing seamless output for end-users in the energy sector. Specifically, we focus on two-day-ahead PV site forecasts, with an emphasis on a highly resolved nowcasting range (from minutes to hours ahead) and a smooth transition to short-range forecasts. Advanced machine learning techniques, gridded meteorological models, and a variety of location-specific data sources are employed to enhance our post-processing approach for optimal site forecasts.
Focusing on an Austrian case study, we develop a post-processing framework based on machine learning approaches for time-series forecasting, with particular emphasis on Long Short-Term Memory (LSTM) models compared to more classical methods such as Random Forest (RF) and Multiple Linear Regression (MLR). Our primary objective is to smoothly post-process and identify transitions among a set of range-specific, mostly gridded background models spanning various spatial and temporal resolutions. The post-processed models used as input primarily represent irradiance and related parameters. Our work integrates IrradPhyD-Net, a high-resolution AI-based nowcasting model, with AROME, a limited-area Numerical Weather Prediction (NWP) model for the alpine region, providing valuable physical information extending into the short- and medium-range. To exploit the location-specific characteristics of the site, we incorporate additional time-series models that capture the climatology and trends of PV, irradiance, and strongly correlated parameters identified during pre-processing. Given the substantial and growing input data needs of AI and machine learning, we build on our previous contributions by integrating semi-synthetic data to address challenges posed by limited or inconsistent historical PV data, thereby improving model stability. In this context, additional data sources, such as satellite-based CAMS radiation time-series and ERA-5 reanalysis, are essential.
By leveraging skillful input models, supported by synthetic data, our post-processing framework demonstrates strong forecast skill across the studied ranges. Thus, sourcing and transforming data from multiple inputs proves to be an effective way to achieve seamless, high-skill forecasts while maintaining high temporal resolution for nowcasting.
How to cite: Papazek, P., Gfäller, P., and Schicker, I.: Hybrid Post-Processing for Solar Power: Bridging Nowcasting to Short-Range , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17282, https://doi.org/10.5194/egusphere-egu25-17282, 2025.
Climate forecasts at seasonal timescales are critical for various sectors, and play a key role in decision-making processes, helping to mitigate risks associated with climate variability and extreme events. However, model outputs are typically insufficient for many practical applications due to coarse resolution and systematic biases, requiring the employment of post-processing techniques to enhance their usability and target stakeholders’ interest such as early warning systems. Post-processing techniques such as downscaling and bias correction can translate model outputs into higher-resolution, bias-corrected forecasts that are more relevant and best appropriate for local applications. We present a physics-informed CNN-based framework for downscaling and bias correction of ECMWF SEAS5.1 seasonal temperature and precipitation forecasts over Europe from 1° to ~1.2km, which represents a downscaling factor of ~60. The approach considers several climate drivers of atmospheric surface variables from SEAS5.1 as input and takes European Meteorological Observations at 1.2 km as ground truth data. We use an analog-based approach to account for the mismatch between long-range model outputs and observations due to model drifting, which is a problem for supervised neural networks algorithms running on climate datasets. Finally, we present a detailed evaluation of the performance for the period 2017-2022, by comparing our results to the raw output. In most cases, the post-processed forecasts outperform the raw predictions in terms of bias reduction, spatial representation and capturing the extremes. This work has potential implications for reducing uncertainties, improving spatial representation, and addressing systematic biases present in raw ECMWF seasonal products.
How to cite: Díaz Esteban, Y., Lin, Q., Heidari, F., Espitia Sarmiento, E. F., and Xoplaki, E.: Improving seasonal forecasts for early warning systems in Germany, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15861, https://doi.org/10.5194/egusphere-egu25-15861, 2025.
Operational weather forecasting has traditionally relied on physics-based numerical weather prediction (NWP) models, but the rise of AI-based weather emulators is reshaping this paradigm. However, most data-driven models for medium-range forecasting still face limitations, such as a narrow range of predicted variables and low effective spatiotemporal resolution. This presentation will compare the strengths and weaknesses of these two approaches, using Environment and Climate Change Canada’s Global Environmental Multiscale (GEM) model and Google DeepMind’s GraphCast model. It will demonstrate that GraphCast outperforms GEM in predicting large-scale features, particularly for longer lead times.
Building on these findings, we propose a new hybrid NWP-AI system, in which GEM’s large-scale state variables are spectrally nudged towards GraphCast’s inferences, while GEM continues to generate fine-scale details critical for weather extremes. Results show that this hybrid system improves GEM’s forecast accuracy, reducing RMSE for the 500-hPa geopotential height by 5-10% and extending predictability by 6-12 hours in the extratropics, peaking at day 7 of the forecast. It also yields significant improvements in tropical cyclone trajectory prediction without degrading intensity forecasts. Unlike state-of-the-art AI-based models, the hybrid system ensures meteorologists retain access to all forecast variables, including those critical for high-impact weather. Preparations are currently well underway for the operationalization of this hybrid system at the Canadian Meteorological Centre.
How to cite: Separovic, L., Husain, S., Caron, J.-F., Aider, R., Buehner, M., Chamberland, S., Creese, C., Lapalme, E., McTaggart-Cowan, R., Subich, C., Vaillancourt, P., Yang, J., and Zadra, A.: Leveraging Data-Driven Weather Forecasting for Improving Numerical Weather Prediction Skill Through Large-Scale Spectral Nudging, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10701, https://doi.org/10.5194/egusphere-egu25-10701, 2025.
Fog, rain, snow, and icing are the high-impact weather events often lead to the highway blockings, which in turn causes serious economic and human losses. At present, there is no clear calculation method for the severity of highway blocking which is related to highway load degree and economic losses. Therefore, there is an urgent need to propose a method for assessing the economic losses caused by high-impact weather events that lead to highway blockages, in order to facilitate the management and control of highways and the evaluation of economic losses. The goal of this work is to develop a method to be used to assess the high impact weather (HIW) effects on the highway blocking. Based on the K-means cluster analysis and the CRITIC (Criteria Importance through Intercriteria Correlation) weight assignment method, we analysed the highway blocking events occurred in Chinese provinces in 2020. Through cluster analysis, a new method of severity levels of highway blocking is developed to distinguish the severity into five levels. The severity levels of highway blocking due to high-impact weather are evaluated for all weather types. As a part of calculating the degree of highway blocking, the highway load in each province is evaluated. The economic losses caused by dense fog are specifically assessed for the entire country.
How to cite: Liu, D., Jing, T., Yan, M., and Gultepe, I.: A New Method for Calculating Highway Blocking due to High Impact Weather Conditions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2044, https://doi.org/10.5194/egusphere-egu25-2044, 2025.
The spatiotemporal variation of fog reflects the complex interactions among fog, boundary layer thermodynamics and synoptic systems. Previous studies revealed that fog can present fast spatial propagation feature and attribute it to boundary layer low-level jet (BLLJ), but the effect of BLLJ on fog propagation is not quantitatively understood. Here we analyze a large-scale fog event in Jiangsu, China from 20 to 21 January 2020. Satellite retrievals show that fog propagates from southeast coastal area to northwest inland with the speed of 9.6 m/s, which is three times larger than the ground wind speeds. The ground meteorologies are insufficient to explain the fog fast propagation, which is further investigated by WRF simulations. The fog fast propagation could be attributed to the BLLJ occurring between 50 and 500 m, because the wind speeds (10 m/s) and directions (southeast) of BLLJ core are consistent with fog propagation. Through sensitive experiments and process analysis, three possible mechanisms of BLLJ are revealed: 1) The abundant oceanic moisture is transported inland, increasing the humidity of boundary layer and promoting condensation; 2) The oceanic warm air is transported inland, enhancing the inversion layer and favouring moisture accumulation; 3) The moisture advection probably promotes low stratus formation, and later it subsides to be ground fog by turbulent mixing of fog droplets. The fog propagation speed would decrease notably by 6.4m/s (66%) in the model if the BLLJ-related moisture and warm advections are turned off.
How to cite: Yan, S., Wang, H., Liu, X., Zu, F., and Liu, D.: Effect of boundary layer low-level jet on fog fast spatial propagation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1679, https://doi.org/10.5194/egusphere-egu25-1679, 2025.
Climate model output at 30 European airports (including 25 of the busiest) is used to investigate summer take-off distance required – TODR – and maximum take-off mass – MTOM – and how they may change in the future. We compare data from 2035–2064 to a historical baseline of 1985–2014 using three future forcing scenarios which represent low (SSP1-2.7), medium (SSP3-7.0), and high (SSP5-8.5) future emissions trajectories defined by the widely used Shared Socioeconomic Pathways, SSPs.
This work presents data for the A320 aircraft manufactured by Airbus but the calculation framework is widely applicable to any similar fixed-wing aircraft and uses entirely open-access input data.
We use 10 models from the 6th Coupled Model Intercomparison Project (CMIP6) which have a range of equilibrium climate sensitivity values; a measure of the amount of global warming they give for a doubling of carbon dioxide concentrations.
We use a numerical scheme which considers the resultant forces on an aircraft in the runway acceleration phase of its take-off and show that 30-year average values of TODR could increase by up to 100 m by mid-century. There is, however, significant variability since daily data is used throughout.
We quantify the changing probability distribution of TODR using kernel density estimation and illustrate this using an example showing how increases in extreme daily maximum temperature could alter distributions of TODR.
Additionally, we project that the 99th percentile (a one in a hundred day event) of the TODR from 1985-2014 may by exceeded on as many as half the summer days for some sites in the future.
Four of the airports studied (Chios, Pantelleria, San Sebastian and Rome Ciampino) have runway lengths which are shorter than the TODR when the aircraft is carrying its maximum payload. This means that the weight they carry must be reduced to fulfil safety constraints, which will only become more stringent as temperatures increase further. Relative to the mean weight-restriction amount for the historical period, we find that the number of passengers may have to be reduced by up to 10-12 passengers per flight, again accompanied by a significantly increased chance of exceeding extreme historical values.
How to cite: Williams, J., Williams, P., Guerrini, F., and Venturini, M.: Climate change will increase aircraft take-off distances and reduce payloads, but by how much?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3272, https://doi.org/10.5194/egusphere-egu25-3272, 2025.
Moist convection in the Maritime Continent (MC) is typically driven by synoptic disturbances: Northerly Cold Surge (NCS), Borneo Vortex, and Madden-Julian Oscillation (MJO). One or more of these tropical disturbances can control the convective behaviour in the MC, resulting in changes in the diurnally forced convection, cloud populations and diurnal precipitation. This investigation analyses a record extreme rainfall event on Java Island around New Year's Eve 2020, the highest amount of rainfall recorded in the capital city of Indonesia, Jakarta. We use reanalysis data from ECMWF Reanalysis v5 (ERA5) to identify and analyse the southward propagation of the NCS. Satellite measurements from the Himawari-8 Advanced Himawari Imager and satellite-derived cloud physical properties reveal the cloud signatures of the NCS. High-resolution Weather Research & Forecasting Model (WRF) simulations were performed to understand the mesoscale dynamic process of the NCS's interaction with the enhanced precipitation at the diurnal scale.
Our results suggest that this extreme event resulted from the interaction of an NCS event and the diurnally forced convection. A persistent northwesterly wind near the surface over the Java Sea induced an intense low-level wind convergence from the meridional moisture transport associated with the NCS and the equatorial trough over Java. This promoted the necessary unstable conditions for organised convection during the afternoon-evening. The cloud populations and diurnal cycle of heavy rainfall in western Java were affected by the frontal region of the NCS with the offshore propagating land breeze from Java and Sumatra, as well as the intense convergence of moisture air in the internal seas of the MC. Our analysis also suggests that the presence of this strong cross-equatorial flow in the MC induced moisture transport from the southern part of Sumatra to the western region of Java. The findings outlined here could be utilised to enhance our understanding of severe weather in the MC.
How to cite: Lopez-Bravo, C.: A high-resolution modelling and observational analysis of an extreme rainfall event driven by the Northerly Cold Surge and intraseasonal tropical variability in Jakarta: January 2020, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14567, https://doi.org/10.5194/egusphere-egu25-14567, 2025.
Urban resilience to extreme weather events is increasingly threatened by the intensification of short-duration rainfall, often leading to urban flooding. This study focuses on improving the prediction of heavy rainfall in the Metropolitan Area of Barcelona, located on the Catalan Mediterranean coast in the northeast of the Iberian Peninsula, using high-resolution radar products and rain gauge data. Despite the decrease in average of annual rainfall in the AMB over recent decades, the intensity rates of some storm events are among the highest of the existing series, with occasional convective events causing urban flooding and severe disruptions for the urban region. The latest climate change reports (IPCC 2022) point towards an increase in frequency and intensity of heavy rainfall events in the region.
An extensive dataset of rainfall days spanning from 2014 to 2022 is analysed, including volumetric radar products (VIL, Echo Top), surface rainfall measurements, and incident reports. A bottom-up approach is used to identify 45 intense convective days with significant impacts in the study region. A radar-based nowcasting approach is introduced, utilizing a two-dimensional radar product with three-dimensional atmospheric information to enhance early warnings in the urban region, with high spatial resolution. This approach focuses on the convective parts of storms through Vertical Integrated Liquid (VIL) density-based tracking and nowcasting with six-minute temporal updates to characterize storm centroids and their evolution. The density of VIL (DVIL), derived from radar composites, provides vertical storm structure information in a two-dimensional format, enabling faster data processing without losing volumetric capabilities.
The findings reveal spatial coherence between maximum DVIL intensities and maximum rainfall locations, with all events exceeding the 2.5 g/m³ DVIL threshold coinciding with high-intensity rainfall. Centroid trajectories show seasonal patterns, with some summer events originating from scattered sources and moving more slowly, while some autumn ones align along the coast and propagating inland. The time lag between initial DVIL detection and peak precipitation for the analysed days ranges from 30 minutes to over two hours, offering critical lead times for early warnings.
This study demonstrates the strengths and limitations of DVIL as a predictor of heavy rainfall in urban areas. The RaNDeVIL module shows promise for operational nowcasting, with necessary improvements to address complex interactions of the storm dynamics and more complex modelling to nowcast longer timescales. These advancements aim to enhance resilience to intense precipitation in the Metropolitan Area of Barcelona under changing climatic conditions.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement 101037193.
How to cite: Esbri, L., Rigo, T., Llasat-Botija, M., and Llasat, M. C.: Using VIL density for identification of storm nuclei, tracking and nowcasting in the Barcelona Metropolitan Area, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13191, https://doi.org/10.5194/egusphere-egu25-13191, 2025.
Water vapour fluxes, originating mainly from the Atlantic, North Africa, and the Mediterranean region, play a critical role in shaping the climate dynamics of the Mediterranean Basin, especially during the summer months. These fluxes significantly influence relative humidity levels in the troposphere, affecting both local and regional weather patterns, such as intense rainfall events and prolonged droughts, while also contributing to the amplification of heatwaves through enhanced surface radiation trapping. This study uses observational data collected during the Mediterranean Experiment for Sea Salt and Dust Ice Nuclei (MESSA-DIN) from July to September 2021 in Soverato, southern Italy, to characterise the synoptic conditions of the severe summer of 2021.
A combination of ground-based remote sensing instruments revealed intense and persistent water vapour transport in the mid-troposphere. ERA5 data were used to identify the moisture dynamics over the Mediterranean Basin. The comparison between ERA5 reanalysis data and ground-based measurements further highlighted discrepancies in the representation of water vapour, particularly a dry bias in relative humidity in the range between 500 hPa and 300 hPa. While ERA5 provided a coherent and detailed representation of synoptic patterns and showed general agreement in the time evolution of the atmospheric vertical structure with observations, it exhibited a dry bias in relative humidity (RH) values compared to a ground-based microwave profiler (MWP). However, the magnitude of the bias also depends on the bias affecting the MWP retrieval, typically within 10-15% RH in the mid-troposphere. ERA5 also overestimates the presence of both cold and warm clouds, while ground instruments detected much less frequent cloud cover. This emphasizes the need for improving reanalysis performance in complex coastal and orographic settings. The bias in ERA5 was further assessed using GRUAN data from the Potenza station and regular upper-air data from Mediterranean stations.
The study underscores the importance of ground-based measurements, such as those from microwave radiometers, in improving weather forecasts for extreme events. Despite their lower vertical resolution, these instruments—both on their own and when combined with higher-resolution measurement techniques such as Raman lidars and upper-air soundings—provide continuous, real-time measurements of atmospheric water vapour. These measurements are essential for enhancing our understanding of water vapour fluxes and their impact on cloud formation, as well as for improving the accuracy of high-resolution forecasting models, especially in the representation of extreme weather events in the Mediterranean and Central Europe.
How to cite: Madonna, F., Gandolfi, I., Rosoldi, M., Karimian Saracks, F., Hesham Essa, Y., and Salicone, G.: The Role of Water Vapour in Shaping Mediterranean Summer Climate: Findings from MESSA-DIN 2021 measurement campaing in southern Italy, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11378, https://doi.org/10.5194/egusphere-egu25-11378, 2025.
Based on the brightness temperature observed by the Fengyun-4A satellite, eight hundred mesoscale convective systems (MCSs) are identified in the middle reaches of the Yangtze River Basin during the warm seasons of 2018–2021, and these MCSs are categorized into the quasistationary (QS) type and the outward-moving (OM) type. Afterward, the initiations of the MCSs are backward tracked using a hybrid method of areal overlapping and optical flow. Then, the intensity, evolution and distribution of cloud-to-ground (CG) lightning and radar composite reflectivity (CR) associated with MCSs are explored.
The QS-MCSs primarily occur in July and August and are mainly initiated in the afternoon. The OM-MCSs mostly occur in June and July with two initiation peaks at noon and late night, respectively. The QS-MCSs are mainly initiated in mountainous areas. In contrast, the OM-MCSs are mainly initiated in plain areas. Compared to the OM-MCSs, the QS-MCSs show notable diurnal variation in intensity and develop more rapidly. The geographical distribution of CG lightning associated with MCSs shows that the highest occurrence tends to appear over the transition zone of the Poyang Lake Plain and the surrounding mountains. The CG lightning associated with MCSs features a relative lower proportion of negative CG lightning occurrences. An overall negative correlation between brightness temperature and the peak current of CG lightning is documented with seasonal variations. The advection of ice particles associated from convective cores into nearby stratiform regions caused by relatively stronger mid-to-upper-level winds, may explain the positive correlations in May and September. A time lag of 0–2 h between the CG lightning occurrence peak and the MCS extent maximum is found. As the MCS develops, the proportion of convective clouds decreases, the proportion of nonprecipitating anvil increases, and the proportion of stratiform consistently maintains 50%–60% of the MCS extent, dominating throughout its life span. The main region for stratiform is primarily in the southern part of the MCS, while convective clouds are mainly in the northern part, possibly due to the influence of the Meiyu front.
How to cite: Sun, J. and Fu, Y.: The Intensity, Evolution, and Distribution of Cloud-to-Ground Lightning and Radar Reflectivity throughout the Life Cycle of Mesoscale Convective Systems over Southern China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5335, https://doi.org/10.5194/egusphere-egu25-5335, 2025.
Mesoscale vortices in the boundary layer are characterized by short lifespans, small spatial scales, and difficulty in prediction, leading to their frequent oversight in operational forecasting. This oversight often results in lower accuracy for precipitation forecasting associated with these vortices. From April 2 to April 3 2023, a squall line event triggered by vortices extending from the lower troposphere to the boundary layer occurred across eastern Hubei to western Anhui. This event developed ahead of a shallow mid-tropospheric trough, while the lower levels were influenced by southwest flow. High-resolution numerical simulations successfully reproduced the evolution of the vortex and the organizational development of the squall line. Dynamic diagnosis revealed that the nocturnal boundary layer vortex (925 hPa) was initiated by the intensification of the nocturnal jet and the blocking effect of terrain. Subsequently, through vertical advection of horizontal vorticity from boundary layer to lower level, the vortex at the lower troposphere (850 hPa) developed and intensified. Later, under the combined influence of horizontal divergence and horizontal advection, the vortex rapidly strengthened, creating favorable convergence conditions for the squall line's development due to the northerly flow west of the vortex and the southwest flow south of it.
How to cite: Zhang, Y., Xi, X., and Sun, J.: The formation and evolution mechanism of the boundary layer vortex east of thesecond-step terrain along the middle reaches of the Yangtze River, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15786, https://doi.org/10.5194/egusphere-egu25-15786, 2025.
During 29th July to 1st August in 2023, a persistent heavy rainfall event (“23·7” event) hit North China causing severe floods, enormous infrastructure damage and large economy loss. Observational analysis shows that the extremely large accumulation of precipitation and long duration of this event are closely related to a slowly moving landfall typhoon “Dusuari” over North China due to the blocking effect of an anomalous high over the mid- and high-latitude Asia. The anomalous southeasterly flow induced by the typhoon “Dusuari” and another typhoon “Kanu” over the East China Sea jointly built a highly efficient channel of water vapor supplying from southern oceans towards North China. A water vapor budget analysis indicates that precipitation of this event is mainly caused by dynamic process involving strong ascending motion. Accompanying strong water vapor transportation and convergence over North China, large amount of latent heat is released in the middle and lower troposphere. The physical mechanisms of heavy rainfall-induced diabatic heating in maintaining the precipitation over North China is further investigated using statistics analysis and numerical experiments. On one hand, the latent heating released by heavy rainfall induces significant uplifting flows which causes more precipitation. On the other hand, the heavy rainfall-induced diabatic heating contributes to enhancement of the westward extension of high-pressure dam around the Mongolian Plateau through a regional meridional circulation. This strengthened high pressure dam sustained the cyclonic circulation of “Dusuari” over North China, leading to continuous heavy rainfall there.
How to cite: Zhao, W.: Mechanisms of persistent extreme rainfall event in North China, July 2023: Role of atmospheric diabatic heating, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1762, https://doi.org/10.5194/egusphere-egu25-1762, 2025.
This study examines the spatial and temporal distributions of short-term heavy rain (SHR) in the middle Yangtze River basin (MYRB) in the summers of the past decade. SHR events are most frequent during the annual Meiyu periods, significantly contributing to total precipitation. Additionally, these events generally last longer and tend to peak at night. The occurrence of SHR events decrease from southeast to northwest, influenced by the monsoonal flow and the small-scale terrain. Moisture convergence prior to Meiyu SHR events is predominantly influenced by both southerly and easterly winds below 700 hPa. Frequent low-level jets and quasi-steady cyclonic circulation lead to strong southerly winds prevailing over the eastern MYRB, while weaker easterly winds dominate in the west. Wind profiles derived from wind profile radar products illustrate the preceding changes in wind speed, wind directions, and vertical wind shear below 4 km above ground level (AGL), as well as the timing of these changes. In the plain area of southeastern MYRB, accelerated southwesterlies are observed 3 to 4 hours before SHR events, accompanied by an intensification of southerly winds near the boundary layer top 2 hours prior. Within the hour leading up to the SHR events, wind speeds sharply rise to their peak. In front of the mountains in west MYRB, southwesterlies strengthen 5 hours in advance but then weaken as they shift to northerlies. Just before the SHR events, however, reinforced northerlies occur near the surface. In the mountainous region of western MYRB, while changes in wind speed are minimal due to topographic blocking, the frequency of southeasterly components below 2 km AGL significantly increases 4 hours before SHR events. The preceding timing of significant vertical wind shear coincides with the increase in wind speed and the change in wind direction. Understanding the detailed characteristics of wind profiles preceding the SHR events during the Meiyu seasons can provide valuable insights for localized severe weather early warning systems.
How to cite: Wang, J., Cui, C., Wang, X., and Wang, X.: Wind profile warning characteristics of short-term heavy rain during the Meiyu season, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1763, https://doi.org/10.5194/egusphere-egu25-1763, 2025.
This study introduces Aeolus 2.0[1, 2], a novel multilayer moist-convective Thermal Rotating Shallow Water (mcTRSW) model designed to simulate atmospheric dynamics under various forcings, such as increased radiative or thermal forcing, as well as the effects of latent heat release and radiative transfer on meso- and large-scale dynamics. The model incorporates a novel moist-convective scheme that respects conservation laws, a new bulk aerodynamic scheme for sea surface evaporation and sensible heat flux, and provides a computationally efficient yet physically robust framework, bridging the gap between idealized models and complex general circulation models. Aeolus 2.0 integrates barotropic and baroclinic processes, enabling detailed investigations of phenomena such as zonal wind variability, heatwaves, and seasonal energy fluxes.
The model has already been applied to various atmospheric phenomena, such as simulating the Madden-Julian Oscillation (MJO)[3], large-scale localized extreme heatwaves[4], and atmospheric responses to increased radiative forcing during solstices and equinoxes[1]. In this presentation, we showcase the results of the latter. The findings highlight significant changes in zonal wind velocity and meridional temperature gradients, with notable hemispheric asymmetry. Specifically, increased radiative forcing enhances subtropical westerly jet velocities and mid-latitude temperatures during the solstices, while reducing polar cyclone zonal wind velocities in the affected hemisphere. Poleward eddy heat fluxes were consistently observed across hemispheres, and heatwave intensity and duration were amplified over both land and ocean regions.
References:
[1] Rostami, M., Petri, S., Fallah, B., Fazel-Rastgar, F. (2025). Aeolus 2.0's thermal rotating shallow water model: A new paradigm for simulating extreme heatwaves, westerly jet intensification, and more. Physics of Fluids, 37 (1), 016604. https://doi.org/10.1063/5.0244908.
[2] Rostami, M., Petri, S., Guimaräes, S.O., Fallah, B. (2024). Open-source stand-alone version of atmosphere model Aeolus 2.0 Software. Geoscience Data Journal, 11, 1086–1093. https://doi.org/10.1002/gdj3.249. (Link to Zenodo: https://doi.org/10.5281/zenodo.10054154)
[3] Rostami, M., Zhao, B. & Petri, S. (2022). On the genesis and dynamics of madden–Julian oscillation-like structure formed by equatorial adjustment of localized heating. Quarterly Journal of the Royal Meteorological Society, 148, 3788–3813. https://doi.org/10.1002/qj.4388.
[4] Rostami, M., Severino, L., Petri, S., & Hariri, S. (2023). Dynamics of localized extreme heatwaves in the mid-latitude atmosphere: A conceptual examination. Atmospheric Science Letters, e1188. https://doi.org/10.1002/asl.1188 .
How to cite: Rostami, M., Petri, S., Fallah, B., and Fazel-Rastgar, F.: On the Dynamical Core of Aeolus 2.0: An Atmospheric Model Using a Moist-Convective Thermal Rotating Shallow Water Framework, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5038, https://doi.org/10.5194/egusphere-egu25-5038, 2025.
This research aims to examine the evolution of the large-scale localized buoyancy anomalies in mid-latitude regions, investigating the adjustments in the atmosphere for moist-convective environments. For the global dynamical simulation, the two-layer moist-convective thermal rotating shallow water (mcTRSW) model Aeolus2.0 with intermediate complexity was employed. The concept of two interacting layers enabled the study of the dynamics of localized extreme heatwaves in baroclinic and barotropic situations. The model initialization comprises daily averaged velocity and potential temperature variables from ERA5 data. The results reveal the presence of a circular positive buoyancy anomaly in the lower layer, while the upper layer shows opposite circular rotation wind movement for some of the cases analyzed. The condensed liquid water content anomaly evolution shows that baroclinic localized buoyancy perturbation should play an important role for increased cloud formation and condensation, as a result of the heatwave propagation in the atmosphere for those extreme forcings. Comparing the strong and weak buoyancy anomalies results, we can notice the prolonged effects of baroclinic initial condition over the barotropic case.
How to cite: Oliveira Guimarães, S., Rostami, M., and Petri, S.: An Intermediate Complexity Approach to the Dynamics of Localized Extreme Heatwaves in the Mid-Latitude Atmosphere for moist-convective environments using Aeolus2.0, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18630, https://doi.org/10.5194/egusphere-egu25-18630, 2025.
High-resolution climate projections are crucial for assessing the future impacts of climate change. Statistical, dynamic, or hybrid climate data downscaling is often employed to create the datasets required for impact modelling. In this study, we utilize the COSMO-CLM (CCLM) version 6.0, a regional climate model, to investigate the advantages of dynamically downscaling a general circulation model (GCM) from CMIP6, with a focus on Central Asia (CA). The CCLM, running at a 0.22° horizontal resolution, is driven by the MPI-ESM1-2-HR GCM (at 1° spatial resolution) for the historical period 1985–2014 and projections for 2019–2100 under three shared socioeconomic pathways (SSPs): SSP1-2.6, SSP3-7.0, and SSP5-8.5 (Fallah et al., 2025). Using the CHIRPS gridded observation dataset for evaluation, we assess the performance of the CCLM driven by ERA-Interim reanalysis over the historical period.
The added value of CCLM, particularly over mountainous areas in CA, is evident, with a reduction in mean absolute error and bias of climatological precipitation by 5 mm/day for summer and 3 mm/day for annual values (Fallah et al., 2024). While no error reduction is achieved for winter, the frequency of extreme precipitation events improves in the CCLM simulations. Future projections indicate an increase in the intensity and frequency of extreme precipitation events in CA by the century’s end, particularly under the SSP3-7.0 and SSP5-8.5 scenarios. The number of days with more than 20 mm of precipitation increases by more than 90, and the annual 99th percentile of total precipitation increases by over 9 mm/day in mountainous areas.
A convolutional neural network (CNN) is also trained to map GCM simulations to their dynamically downscaled CCLM counterparts. The CNN successfully emulates the GCM-CCLM chain across large areas of CA but demonstrates reduced skill when applied to other GCM-CCLM chains. This downscaling approach and CNN architecture provide an alternative to traditional methods and could be a valuable tool for the scientific community involved in downscaling CMIP6 models (Harder et al., 2023).
In future work, we aim to extend this approach by training a neural network model to map the available GCM-RCM model chains for CORDEX-EU and applying the trained model to decadal prediction ICON simulations. This will enable the production of CORDEX-EU-like regional ICON simulations, bridging the gap between global and regional climate information on decadal timescales. By integrating decadal predictions into the framework, we aim to enhance the usability of regionalized climate data for short-term climate planning and decision-making.
References:
- Fallah, B., Russo, E., Menz, C., Hoffmann, P., Didovets, I., and Hattermann, F. F.: Anthropogenic influence on extreme temperature and precipitation in Central Asia, Sci. Rep., 13, 6854, https://doi.org/10.1038/s41598-023-33921-6, 2023.
- Fallah, B., Menz, C., Russo, E., Harder, P., Hoffmann, P., Didovets, I., and Hattermann, F. F.: Climate Model Downscaling in Central Asia: A Dynamical and a Neural Network Approach, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2023-227, accepted, 2025.
- Harder, P., Hernandez-Garcia, A., Ramesh, V., Yang, Q., Sattegeri, P., Szwarcman, D., Watson, C., and Rolnick, D.: Hard-Constrained Deep Learning for Climate Downscaling, J. Mach. Learn. Res., 24, 1–40, 2023.
How to cite: Fallah, B. and Rostami, M.: Precipitation Downscaling Using Dynamical and Neural Network Approaches., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10090, https://doi.org/10.5194/egusphere-egu25-10090, 2025.
Seamless prediction systems provide frequently updated forecasts across different timescales by combining observations, such as weather radar data, with numerical weather prediction (NWP) models. These systems are increasingly needed by users like hydrological services, local authorities, renewable energy operators, and smartphone apps to make better and earlier decisions. This is especially true for precipitation, which is highly variable in space and time and strongly influences downstream models like (urban) hydrology. To achieve this, forecasts must not only be fast and accurate but also come with calibrated ensembles to estimate uncertainty and propagate errors properly.
In Belgium, Project IMA (inspired by the Japanese word for "now" or "soon") is the seamless prediction system developed by the Royal Meteorological Institute (RMI). It uses RMI’s observation network, including RADQPE for gauge-corrected precipitation estimates, the pysteps-be probabilistic rainfall nowcasting system, the INCA-BE nowcasting system, and the ACCORD NWP models ALARO and AROME. Unlike many other systems, Project IMA offers seamless ensemble precipitation nowcasts for lead times up to 6 hours, updated every 5 minutes, designed to improve flash flood predictions and quantify their uncertainty.
This presentation will showcase recent developments in Project IMA, including updates to the open-source pysteps framework, such as an improved runtime efficiency, code structure and better representation of extremes. We will discuss new deep learning-based methods for blending forecasts to extend their lead time and improve accuracy, calibration, and usefulness for end users such as hydrologists, crisis managers and water authorities.
Project IMA aims to ensure a rapid transfer from research to operations and encourages open-source contributions to ensure transparency and reproducibility. It supports the United Nations’ “Early Warnings for All” initiative, which strives to make forecasts more accessible and actionable by 2027.
How to cite: De Cruz, L., De Kock, S., Van Ginderachter, M., Reyniers, M., Deckmyn, A., Dehmous, I., Dewettinck, W., Erdmann, F., Imhoff, R., Moraux, A., Reinoso-Rondinel, R., Veldhuizen, M., Casey, J. J., Faleu Kemajou, L., Kumar, A., and Van Nieuwenhuize, V.: Advances in Project IMA, the Seamless Prediction Programme of the Royal Meteorological Institute of Belgium, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19706, https://doi.org/10.5194/egusphere-egu25-19706, 2025.
Proper scoring rules are an essential tool to assess the predictive performance of probabilistic forecasts. However, propriety alone does not ensure an informative characterization of predictive performance and it is recommended to compare forecasts using multiple scoring rules. With that in mind, interpretable scoring rules providing complementary information are necessary. We formalize a framework based on aggregation and transformation to build interpretable multivariate proper scoring rules. Aggregation-and-transformation-based scoring rules can target application-specific features of probabilistic forecasts, which improves the characterization of the predictive performance. This framework is illustrated through examples taken from the weather forecasting literature and numerical experiments are used to showcase its benefits in a controlled setting. Additionally, the framework is tested on real-world data of postprocessed wind speed forecasts over central Europe. In particular, we show that it can help bridge the gap between proper scoring rules and spatial verification tools.
How to cite: Pic, R., Dombry, C., Naveau, P., and Taillardat, M.: Interpretable ultivariate scoring rules based on aggregation and transformation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2224, https://doi.org/10.5194/egusphere-egu25-2224, 2025.
A point-forecast is defined as a single-value forecast expressed in the unit of a variable of interest. A deterministic forecast for 2m temperature at Vienna tomorrow is a point-forecast. Point-forecasts are required by some forecast users and for various applications. When an ensemble prediction system is at hand, a point-forecast can take the form of a distribution functional such as the ensemble mean or an ensemble quantile. In this context, we introduce a new type of point-forecast based on the concept of crossing-point forecast (Ben Bouallègue, 2021). We argue that this self-adaptive forecast should be better suited for some users than other point-forecasts. More precisely, we demonstrate that the so-called crossing-point quantile is an optimal forecast in terms of Pierce Skill Score (or equivalently in terms of area under the ROC curve) for any event of interest.
Ben Bouallègue Z (2021), On the verification of the crossing-point forecast, Tellus A. DOI:10.1080/16000870.2021.1913007
How to cite: Ben Bouallegue, Z. and Taillardat, M.: The crossing-point quantile: an optimal point-forecast in terms of ROC areas. , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8449, https://doi.org/10.5194/egusphere-egu25-8449, 2025.
Reliable climate forecasts are crucial for adapting to future challenges, particularly in urban flood management, where pluvial flooding poses a significant threat. This study focuses on the verification and enhancement of rainfall data for urban flood modelling by analysing critical aspects such as total depth, intensities, seasonality, dry weather periods, and spatial distribution during extreme storm events.
In Graz, Austria, a network of 23 high-resolution precipitation measurement stations covering 120 km², including 13 stations with over a decade of data, was utilized to calibrate a regional climate model through a downscaling approach. This provided minute-level rainfall data for each station, enabling a detailed comparison of historical measurements from the past 10 years with climate model outputs for the current state of the climate. Subsequently, changes in key rainfall characteristics were assessed for the near future (2040–2050) and far future (2090–2100).
Our analysis evaluated yearly precipitation totals, spatial rainfall distribution, intensity-duration-frequency (IDF) functions, and the seasonality of extreme rainfall events. The results revealed promising alignment with historical data, though discrepancies were noted for shorter durations and seasonal shifts. Specifically, heavy rainfall events were projected to occur more frequently in autumn in the future, a trend absent in historical observations.
This study underscores the importance of statistically robust downscaling and verification techniques in blending observational and model-based forecasts to enhance the reliability of climate predictions. These advancements provide critical insights for urban flood resilience planning and illustrate the evolving nature of extreme rainfall under changing climatic conditions.
How to cite: Pichler, M. and Muschalla, D.: Spatial and Temporal Verification of High-Resolution Modelled Rainfall Data for Urban Flood Risk Assessment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21081, https://doi.org/10.5194/egusphere-egu25-21081, 2025.
From July 29 to August 1, 2023, extreme heavy rainfall occurred in the Chinese HUABEI region. Heavy rainstorm occurred in the most areas of Beijing, Tianjin and Hebei province. The daily precipitation of 14 national meteorological observatories exceeded the historical extreme value. The process intensity exceeded the three extreme rainstorm processes in the history of HUABEI region. Studying the causes of extreme heavy precipitation in HUABEI and evaluating the predictive performance of the model for extreme heavy precipitation is beneficial for improving the application and forecasting ability of the model. This article analyzes the weather scale characteristics and anomalies of this precipitation process from factors such as height field, wind field, divergence field, vorticity field, and water vapor. The dynamic and thermal structure of the vortex and the cause of the upper level continental high are analyzed using the method of cyclone phase space map and full type vorticity equation. Finally, the predictive ability of the model for extreme precipitation is tested. The following main conclusions have been drawn:(1) The precipitation process is divided into two stages. Before the 31st, it was caused by the residual vortex circulation of the "Dussuri", with strong precipitation intensity and range. After the 31st, it was formed by the convergence of the easterly jet on the west side of the subtropical high pressure and its interaction with the terrain. Precipitation was mainly concentrated in the northern part of China, with weaker rainfall intensity compared to the previous period.(2) The key impact systems of this process are the 200hPa high trough and continental high pressure, the 500hPa blocking high pressure, and the residual circulation of the low-level "Dussuri". The divergence in front of the 200hPa high altitude trough is beneficial for maintaining upward movement in the North China region; At 500hPa, there is a blocking high pressure in the northern and eastern parts of North China, which is conducive to the maintenance of low-level vortex systems. The "Dussuri" convergence circulation is the triggering system of the process.(3) The water vapor conditions during this process were exceptionally good, mainly consisting of three water vapor transport paths: the southerly water vapor transport of the South China Sea monsoon, the eastward water vapor transport of the residual circulation of "Dussuri", and the southeast water vapor transport path of typhoon "Kanu".(4) During the northward movement, the residual vortex of the Dussuri maintains a quasi symmetric and warm center structure, with weak cold advection in the upper level of the vortex on the 30th.(5) The uneven vertical distribution of condensation latent heat heating generates negative vorticity in the upper troposphere, ensuring the stable maintenance of continental high pressure.(6) In global model forecasting, the CMA model cannot report a blocking high pressure above 96 hours of time. The EC deterministic model can predict heavy precipitation processes within a 120 hour time frame, and the ensemble forecast can have a predictable time frame of up to 7 days.
How to cite: guan, Y.: Analysis and Model Verification of Extreme rainfall Processes in Huabei of China in 2023, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2697, https://doi.org/10.5194/egusphere-egu25-2697, 2025.
In discussions about climate change, the focus is usually on rising temperatures. However, it is important to understand the significant impact of climate change on the entire weather system. The cloud feedback mechanism is one of the most complex factors in the climate system. This is because clouds can have a heating and cooling effect at the same time, and this balance has a significant influence on the global radiation balance. To understand how all the different factors work together to create a complex system, we need to look closely at how these factors have changed over time.
The aim of this research is to examine changes in cloud cover and convective parameters, as well as the background, causes and effects of these changes in Central Europe between 1983 and 2022. The research uses data from the ERA5 reanalysis database. Aside from the analysis of environmental conditions, an objective cyclone identifying method is used to determine regions under low- or high-pressure weather system influence.
The statistical analysis shows that in general, the decrease in ERA5 low-level cloud cover is associated with an increase in cloud base. Medium- and high-level cloud cover, however, is influenced by changes in large-scale circulation systems.
Low-level cloud cover decrease in the northern regions of the study area is likely due to increasing temperatures and decreasing boundary layer humidity. Though temperatures in the Mediterranean region also have risen, the increase in the frequency of negative NAO situations, and an increase in Mediterranean cyclone and low-pressure system activity - the latter of which is likely induced by the higher evaporation of the Mediterranean Sea - resulted in the increase in cloud cover over the central Mediterranean region. We have also observed an increase in the CAPE (convective area pressure energy) in the Mediterranean during the summer months, which leads to an increase in the frequency of heavy thunderstorms and extreme precipitation events in this area, contributing to the intensification of weather extremes in the region. Changes over the study area are not linear but show a region dependent 10-20 years periodical pattern which is also investigated.
How to cite: Soós, V. and Hajnalka, B.: Impact of climate change on ERA5 cloud cover and convective parameters in Central Europe (1983-2022), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13477, https://doi.org/10.5194/egusphere-egu25-13477, 2025.
The MicroWave Humidity Sounder II (MWHS II) is a cross-track microwave sounder flying on FengYun (FY)-3C satellite. It has 15 channels ranging from 89.0 to 191.0 GHz, eight (channels 2-9) of which are located near 118.75 GHz along an oxygen absorption line, five (channels 11-15) of which are located near 183.31 GHz water vapor absorption line and the remaining two channels 1 and 10 are two window channels centered at 89.0 and 150.0 GHz. A new precipitation detection algorithm for 118GHz channels was developed based on the radiation characters of the double O2 absorption bands (118 and 50-60 GHz). Since both of the 118 GHz and 50-60 GHz oxygen absorption bands are sensitive to atmospheric temperature, the radiation observed in the two bands has a specific inherent constraint relationship under the clear-sky conditions. However, the frequencies of 118 GHz channels are approximately twice that of the 50-60 GHz channels, and the two bands have different absorption and scattering characteristics for atmospheric hydrometeors. The radiance transfer mode VDISORT was used to simulate the sensitivity of the 118 GHz and 50-60 GHz channels to five kinds of hydrometeors (cloud water, rainwater, ice, snow, and graupel) in the cloud atmosphere. The results show that the 50-60 GHz channels are more sensitive to rainwater, and the 118 GHz channels are more sensitive to the other four types of hydrometeors. Therefore, the inherent constraint of the observational radiance between 118 GHz and 50-60 GHz channels under clear-sky condition is no longer valid for a cloudy scenario. In this paper, the machine learning system TensorFlow was used to construct a model for predicting the brightness of 118 GHz channels using 50-60 GHz observations under clear-sky conditions, and the accuracy of the prediction model was validated using independent samples. Then this neural network-based predictive model was used for 118 GHz channel precipitation detection. When the difference between actual observed and predicted bright temperature for 118 GHz channel is more massive than three times of the standard deviation of the prediction model, it is thought that the MWHS II observation is contaminated by precipitation or cloud. At last, this new precipitation detection algorithm for 118 GHz was validated by simulated measurements. The results show that both the precipitation detection POD (test probability) and PC (correct rate) for 118 GHz channels are above 90%.
How to cite: Guo, Y.: A precipitation detection algorithm for 118GHz channels based on FY-3C MWHS II and FY-3C MWTS II, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3880, https://doi.org/10.5194/egusphere-egu25-3880, 2025.
Based on the minute-resolution meteorological elements data observed at 70 automatic weather stations in Jiangsu, the second-resolution sounding data of 3 sounding stations and the fog droplet spectrum data of 21 dense fog events, from January 1, 2013 to December 31, 2023, the spatial and temporal distribution, boundary layer structure and microphysical structure characteristics of the fog at different grades in Jiangsu were analyzed. The results show that in recent years, the number of fog hours in Jiangsu are distributed along the Yangtze River and to the north along the Huaihe River. The average annual fogging time at each station is 318.5h, the strong dense fog and extremely dense fog were mainly concentrated along the Huaihe River and its north, accounting for 16.4% of the total fog hours. The probability of occurrence of fog in Jiangsu is the highest at 05:50, and the probability of occurrence of fog in winter, spring, summer and autumn is the highest at 07:10, 05:50, 05:20 and 05:50, respectively. The temperature structure of fog at different grades between 0 and 1500 m has inversion layer, and with the increase of fog intensity, the inversion intensity increases. And the relative humidity is saturated in the lower layer, but with the increase of fog intensity, the relative humidity of upper layer decreases. With the increase of fog intensity, the number of fog drops of different sizes all increase, and the spectrum of fog drops expands obviously when strong dense fog or extremely dense fog occurs.
How to cite: Wang, H., Zhang, Z., and Liu, D.: Characteristics of the Macro- and Micro-Structures of Different Grades of Fog in Jiangsu, China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7697, https://doi.org/10.5194/egusphere-egu25-7697, 2025.
From 08:00 on July 29 to 08:00 on August 2, 2023, under the influence of typhoon "Dussuri", an extremely heavy rainstorm process occurs in Hebei and Beijing. The precipitation in some areas of the windward slope of Taihang Mountains exceeds 250mm, and in some areas it exceeds 500mm. The distribution of heavy precipitation is basically consistent with the terrain of the windward slope. Using the 6-minute radar retrieved wind field network data developed by the CMA (China meteorological administration) Meteorological Observation Center for analysis, it is found that from 13:00 on July 29th to 20:00 on August 1st, a southeast-oriented ultra-low-level jet greater than 12 m/s was maintained in the 925-hPa field over Hebei and Beijing. The angle between the jet and the Taihang Mountains is almost 90°, and at the same time, a 850-hPa typhoon trough stays on the windward slope for a long time, resulting in stable and less movement of heavy precipitation echoes. This series of factors together led to the occurrence of the extremely heavy rainstorm process. Using the ERA5 hourly reanalysis data as the initial field and based on the WRF4.5 model, a sensitivity test is conducted on this process using three-layer bidirectional nesting (grid spacing of 9km, 3km, and 1km, respectively). The experiment reduces the Yanshan and Taihang Mountains to half of their original heights and 50 meters, respectively (equivalent to the altitude of Beijing). The experimental results indicate that: (1) Precipitation impact: Due to the easterly winds brought by typhoons, the eastern side of Taihang Mountains is on the windward slope, which has a significant impact on precipitation. When the height of Taihang Mountains decreases, the precipitation intensity significantly weakens; When the terrain height drops to 50m, the precipitation location is biased to the west compared to the actual situation. (2) The experiment showed that the blocking effect of Taihang Mountains formed mesoscale low vortex and convergence line on the windward slope. When the height of Taihang Mountains drops to half of its original height or only 50 meters, the mesoscale low vortex and convergence line move westward to Shaanxi Province. (3) The vertical profile analysis along the east-west direction of Taihang Mountains shows strong upward movement in the windward slope area, with positive vorticity in the lower level and negative vorticity in the upper level. When the height of Taihang Mountains decreases, the upward movement significantly weakens, and the positive and negative vorticity weakens until it disappears, indicating that the dynamic effect of terrain has a significant impact on precipitation processes. (4) The Yanshan Mountains are oriented east-west, and parallel to the environmental winds. Therefore, when its height decreases, its impact on physical quantities such as precipitation, wind field, vertical velocity, and vorticity is relatively small.
Key words: terrain, "23.7" extremely heavy rainstorm, analysis
How to cite: huang, X., wu, Z., xue, F., and fu, C.: Analysis and research on the impact of terrain on the "23.7" extremely heavy rainstorm, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2024, https://doi.org/10.5194/egusphere-egu25-2024, 2025.
Based on the observational and forecast datasets from precipitation merging product, radiosonde, Doppler radar, wind profiler radar and ECMWF product, the evolution and causes of the heavy precipitation process of Meiyu in the middle and lower reaches of the Yangtze River in China from June 21 to 22, 2024 were analyzed. The results show as the followings. (1) The heavy precipitation was mainly distributed in the northern part of Hunan Province, the southeastern part of Hubei Province and the western part of Anhui Province, with the main period from 15:00 on June 21 to 15:00 on June 22, especially in the early morning of June 22. The rain belt was located to the north of the subtropical high, in the north of the low-level jet, and at the front side of the moving trough line. (2) The K index exceeded 38℃ in all areas, and the CAPE before and after this heavy precipitation process was over 800 J/kg and less than 100 J/kg, respectively, indicating the evolution characteristics of unstable atmospheric stratification as well as the energy accumulation and release. (3) In the early stage of this process, the surface high temperature was distributed to the south of Wuhan, and the near-surface convergence line extended from the eastern part of Henan Province to the central part of Hubei Province. In the middle stage of this process, the convergence line moved eastward. In the later stage of this process, there was a significant cold pool over the land surface along the Yangtze River. The near-surface high temperature and convergence line were the triggering mechanisms of the heavy precipitation, while the cold pool led to the gradual weakening of the precipitation. (4) The water vapor flux was mainly located in the northern part of Hunan Province, the eastern part of Hubei Province as well as the southern part of Anhui Province, and gradually moved eastward. The flux values in the middle and lower layers were relatively high in the early morning of June 22. There were two water vapor transport belts in the lower layer, corresponding to different heavy precipitation centers. (5) The approximately east-west oriented echo band moved from west to east through the forms of merging, strengthening and dissipating. The south side of the echo band was the mesoscale linear or hook-shaped strong echo accompanied by high echo top and strong VIL. The meso-β scale convective system was composed of several meso-γ scale convective cells, and the meso-γ scale convective cells caused strong cumulative precipitation through the ‘train effect’.
How to cite: Zhou, H., Ge, N., and Qi, W.: Evolution and Cause Analysis of a Heavy Precipitation Process of Meiyu Along Yangtze River, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3417, https://doi.org/10.5194/egusphere-egu25-3417, 2025.
The spatiotemporal dependence structure in postprocessed weather forecast variables is essential for reliable hydrological and socio-economic applications. However, in univariate postprocessing, where statistical or advanced machine learning techniques are applied independently in each margin, the multivariate dependence structure present in the raw ensemble forecasts is lost. To restore the disrupted spatial or temporal dependence structure of univariately postprocessed forecasts, copula-based methods are traditionally applied as an additional step that utilizes dependency information from raw ensemble forecasts or historical observations. However, such a two-step framework faces difficulty incorporating exogenous variables to model the dependence structure. To overcome these limitations, a multivariate non-parametric data-driven distributional regression postprocessing technique based on a generative neural network is employed to draw samples directly from multivariate predictive distribution as output [1]. This study focuses on preserving temporal dependency and investigates the performance of a multivariate generative model against two-step approaches to postprocess a 2-meter temperature forecast with a one-month lead time over the Trentino-South Tyrol region in the northeastern Italian Alps. The forecast dataset is a fifth-generation seasonal weather forecast system (SEAS5) generated by the European Centre for Medium-Range Weather Forecasts (ECMWF), which has a 0.125° x 0.125° horizontal grid resolution with 25 ensemble members over a reforecast period from 1981 to 2016. The reference dataset is the high-resolution (250 m x 250 m) gridded observational data over the region. The results are presented using multivariate proper scoring rules (i.e., energy and variogram scores) to measure the overall discrepancy and dependence structure in the postprocessed forecast. The performance analysis reveals that the multivariate generative postprocessing model outperforms the two-step approach over the entire region.
References:
[1] Chen, J., Janke, T., Steinke, F. & Lerch, S. Generative Machine Learning Methods for Multivariate Ensemble Postprocessing. Ann. Appl. Stat. 18, 159–183 (2024).
How to cite: Uttarwar, S. B., Chen, J., Lerch, S., and Majone, B.: Lead time-dependent postprocessing of 2-meter temperature forecast using a multivariate generative machine learning model , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6937, https://doi.org/10.5194/egusphere-egu25-6937, 2025.
This paper proposes a novel method for predicting icing on overhead contact lines by integrating physical modeling with Transformer-based deep learning, addressing the limitations of traditional meteorological models in complex weather conditions and terrains. The method combines physical factors such as meteorological data (e.g., temperature, humidity, wind speed) and topographic features to construct a physical model for initial predictions, while leveraging the Transformer model's robust capability in processing time-series data to capture the nonlinear dynamics of the icing process. Experimental results demonstrate that the proposed method significantly outperforms traditional single meteorological models in prediction accuracy across various weather conditions, particularly excelling in extreme weather and complex terrain scenarios. This approach provides reliable technical support for disaster prevention, mitigation, and early warning systems in the transportation sector, offering substantial practical value for engineering applications.
How to cite: Huai, X., Kang, W., Li, B., Luo, J., Dai, W., and Liu, R.: A Study on Catenary Icing Prediction Method Integrating Physical Modeling and Transformer-Based Deep Learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7540, https://doi.org/10.5194/egusphere-egu25-7540, 2025.
Forecasts on transportation meteorology, such as pavement temperature, are becoming increasingly important in the face of global warming and frequent disruptions from extreme weather and climate events. In this study, we propose a pavement temperature forecast model based on stepwise regression—model output statistics (SRMOS) at the short-term timescale, using highways in Jiangsu, China, as examples. Experiments demonstrate that the SRMOS model effectively calibrates against the benchmark of the linear regression model based on surface air temperature (LRT). The SRMOS model shows a reduction in mean absolute errors by 0.7–1.6 °C, with larger magnitudes observed for larger biases in the LRT forecasts. Both forecasts exhibit higher accuracy in predicting minimum nighttime temperatures compared to maximum daytime temperatures. Additionally, it overall shows increasing biases from the north to the south, and the SRMOS superiority is greater over the south with larger initial LRT biases. Predictor importance analysis indicates that temperature, moisture, and larger-scale background are basically the key predictors in the SRMOS model for pavement temperature forecasts, of which the air temperature is the most crucial factor in the model’s construction. Although larger-scale circulation backgrounds are generally characterized by relatively low importance, their significance increases with longer lead times. The presented results demonstrate the considerable skill of the SRMOS model in predicting pavement temperatures, highlighting its potential in disaster prevention for extreme transportation meteorology events.
How to cite: Zhu, S., Lyu, Y., Wang, H., Zhou, L., and Zhu, C.: Pavement Temperature Forecasts Based on Model Output Statistics: Experiments for Highways in Jiangsu, China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10119, https://doi.org/10.5194/egusphere-egu25-10119, 2025.
Accurate visibility forecasting is essential for aviation, road safety, and maritime operations as well as communicating the weather on a daily basis to the public. Despite advancements in Numerical Weather Prediction (NWP) models, it is well understood in the forecasting community that NWP visibility forecasts are inherently poor, often suffering from calibration issues and systematic biases. In post-processing we can enhance skill, however, it is very difficult to add skill when the input data are particularly poor, so this diagnostic remains a known problem.
This study explores the application of different parametric and non-parametric statistical post-processing techniques to enhance the accuracy and reliability of visibility forecasts. The chosen method will build upon a new visibility scheme at the Met Office, VERA (Visibility Employing Realistic Aerosol), which uses a more physically realistic representation of the condensation nuclei required to form fog and therefore produces a better distribution of visibility for statistical post-processing to work with.
The calibration methods included in this study include Quantile Regression Random Forests, Reliability Calibration, Bayesian Additive Regression Trees, and finally Distributional Regression Networks using truncated normal and log normal Continuous Ranked Probability Score loss functions, as well as threshold weighted variants of these loss functions. These methods are tailored, where appropriate, to better support the characteristics of visibility data.
The methodology is tested on an extensive training dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF), which spans 20 years of reforecasts and several European countries capturing a wide range of visibility conditions, including the rarer low visibility events which are most impactful.
Initial results demonstrate that Quantile Regression Random Forests post-processed forecasts show a marked reduction in Root Mean Square Error compared to raw NWP outputs, and work is in progress to compare this to other methods. These improvements, so far, highlight the great potential of statistical post-processing in refining visibility predictions and supporting decision-making in weather-sensitive sectors.
How to cite: Hurst, K. and Evans, G.: Improvements to NWP visibility forecasts using statistical post-processing, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9468, https://doi.org/10.5194/egusphere-egu25-9468, 2025.
Based on hourly precipitation data, the warm-sector rainfall events in Beijing-Tianjin-Hebei region are selected and classified using objective methods. There are 33 warm-sector rainfall events in this region from 2010 to 2023. They mainly occur during June and August with the most in July. The average lifetime of these warm-sector rainfall events is 5.44 h. The warm-sector rainfall events are mainly concentrated in the center of the Beijing-Tianjin-Hebei region, and the frequency of occurrence in the east is higher than that in the west. The frequency of occurrence in Beijing is much higher than that in other regions, and it is mainly concentrated in the terrain bell mouth of northeast Beijing. According to the circulation situation that generates warm-sector rainfall, three types of precipitation are obtained: low-vortex type, shear-line type and southerly-wind type. The occurrence months, starting times and locations of warm-sector rainfall events in different types are slightly different. Based on the analysis of the synthetic circulation situation, the dynamic, water vapor and low-level vertical motion conditions of the low-vortex type is most favorable for warm-sector rainfall. The vertical upward movement of shear line warm-sector rainfall events is strong in Beijing; The dynamic condition of southerly-wind type is the weakest, but the water vapor condition is more favorable and the occurrence is related to the topographic distribution of Beijing-Tianjin-Hebei.
How to cite: Liu, R.: Selection and Classification of Warm-Sector Rainfall Events in Beijing-Tianjin-Hebei, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14147, https://doi.org/10.5194/egusphere-egu25-14147, 2025.
Convective events pose a significant threat to society due to the associated heavy rainfall, large hail, strong winds, and lightning. Location and timing determination of convective precipitation is still a challenge for modern meteorology. Despite the good skills of current weather forecasting tools in the prediction of the large-scale environment facilitating the onset of convective phenomena, the multitude of spatial scales involved in such events makes their characterization, observation, and forecast a difficult task. The problem is further complicated by their rapid temporal development, which lasts from minutes to a few hours depending on the specific case.
Recent research indicates that the predictability of these events can be strongly improved accounting for local meteorological observations.
The goal of the ICREN (Intense Convective Rainfall Events Nowcasting) project is to enhance the nowcasting of convective events by:
- exploiting the information made available by local standard and non-conventional observations of meteorological variables
- integrating physically based Numerical Weather Prediction (NWP) models with data-driven black box Neural Networks (NNs).
The NWP model is used to support the NN by means of pseudo-observations (forecasted variables); while the fast computational speed of the NN enables advancing predictions in time and generating ensemble forecasts of convective phenomena.
The project is carried out in the Seveso River basin (almost 300 km2) in Northern Italy. In this region, convective events trigger floods and flash floods heavily impacting the large urban area of Milan.
Within the project, the Weather Research and Forecasting (WRF) NWP model is employed. By using three nested grids, the model achieves a 2 kkm x 2 km spatial resolution over the test area. To optimize the prediction of meteorological variables required by the NN, the model assimilates lightning observations and GNSS-derived Zenith Tropospheric Delays (ZTDs), both of which enhance the representation of local atmospheric humidity.
Several NN models have been trained on standard meteorological data, GNSS ZTDs, and radar-derived parameters—including the position, velocity, and attenuation of convective cells—to identify the architecture best suited for predicting 10-minute accumulated rainfall from 10 minutes up to 1 hour following the detection of a convective event in the test area.
The best-performing models are used to generate ensemble predictions of rainfall events by suitably perturbing the input variables.
Results from the WRF model, the NN predictions and the ensemble forecasts will be presented along with initial integration outcomes for selected convective events occurring in the test area in 2019.
This work is supported by the ICREN-PRIN project (MUR- CUP: D53D23004770006).
How to cite: Venuti, G., Song, X., Federico, S., Guariso, G., Sangiorgio, M., Pasquero, C., Hassantabar Bozroudi, S. H., Mohamed, A. B. E. A., Zaf, R. D., Luini, L., Nebuloni, R., and Realini, E.: Ensemble Convective Rainfall Nowcasting by integrating Numerical Weather Prediction models and Neural Networks: the ICREN project, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15639, https://doi.org/10.5194/egusphere-egu25-15639, 2025.
The importance of precipitation nowcasting is gradually expanding due to the increasing frequency and intensity of localized rainfall caused by climate change. The growth and decay processes of precipitation are critical factors influencing the accuracy of precipitation nowcasting, necessitating advanced modeling approaches. This study proposes a novel methodology that integrates artificial intelligence (AI) with high-resolution radar data to predict the growth and decay processes of precipitation, incorporating these predictions into a radar-based nowcasting model. In this study, AI was applied to predict radar-based precipitation intensity change rates up to two hours ahead, and these predictions were integrated into a precipitation nowcasting model. The AI effectively learned the spatiotemporal patterns of nonlinear precipitation evolution using the RainNet architecture. The AI was trained on three years (2021 – 2023) of radar-derived precipitation intensity change rates, with one year (2020) used for validation to evaluate its performance. The nowcasting model was developed using cross-correlation techniques to calculate motion vectors of the precipitation system at different spatial scales, and a semi-Lagrangian backward extrapolation method was employed for precipitation prediction. Integrating AI-predicted precipitation intensity change rates into the nowcasting model resulted in significant improvements in prediction performance. The results showed a 10% improvement in precipitation prediction accuracy compared to the baseline nowcasting model that did not incorporate AI-based precipitation intensity change rate predictions. The model effectively captured rapid changes in precipitation intensity, demonstrating the utility of AI-based predictions for short-term nowcasting. This study highlights the potential of combining traditional nowcasting models with AI techniques, presenting a promising approach for enhancing precipitation prediction accuracy.
This research was supported by the "Development of radar based severe weather nowcasting technology (KMA2021-03122)" of "Development of integrated application technology for Korea weather radar" project funded by the Weather Radar Center, Korea Meteorological Administration.
How to cite: Kim, K.-H., Ko, K., and Nam, K.-Y.: Enhancing Radar-Based Precipitation Nowcasting Model with AI-Predicted Precipitation Intensity Change Rates, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16130, https://doi.org/10.5194/egusphere-egu25-16130, 2025.
How to cite: Lakatos-Szabó, M.: A comparative study of imputation methods for improving statistical post-processing of weather forecasts, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19873, https://doi.org/10.5194/egusphere-egu25-19873, 2025.
Parametric approaches to post-processing methods are widely used today, as they provide full predictive distributions for the weather variable of interest. These methods rely on training data consisting of historical forecast-observation pairs to estimate their parameters. Consequently, post- processed forecasts are generally restricted to locations with accessible training data. To overcome this limitation, we introduce a general clustering-based interpolation technique that extends calibrated predictive distributions from observation stations to any location within the ensemble domain where ensemble forecasts are available. Using the ensemble model output statistics (EMOS) post-processing technique, we conduct a case study based on 10-m wind speed ensemble forecasts from the European Centre for Medium-Range Weather Forecasts. The results illustrate the effectiveness of the proposed method, demonstrating its advantages over both regionally estimated and interpolated EMOS models as well as raw ensemble forecasts.
Reference: Baran, S. and Lakatos, M. (2024) Clustering-based spatial interpolation of parametric post-processing models. Wea. Forecasting 9, 1591-1604.
Research was supported by the Hungarian National Research, Development and Innovation Office under Grant No. K142849.
How to cite: Nagy-Lakatos, M. and Baran, S.: Clustering-based spatial interpolation of parametric post-processing models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16934, https://doi.org/10.5194/egusphere-egu25-16934, 2025.
Senegal, located in the West Sahel region, frequently experiences flooding driven by mesoscale convective systems (MCSs), which contribute 90% of the region’s rainfall. Current early warning systems for hydrological extremes struggle with timely and accurate predictions, necessitating advancements in precipitation nowcasting. Nowcasting describes short-term weather forecasts with a lead time of typically less than two hours. In this region traditional numerical weather models have limited accuracy in predicting short-term events, and nowcasting models therefore outperform numerical weather prediction in this time frame. Precipitation nowcasts can be helpful in supporting and informing decision makers on time to adapt to the risk and protect society from hydrological extremes.
A major challenge in developing warning systems for this region is the lack of radar data coverage, which is typically used in nowcasting models, compounded by a sparse ground-based observational network. Increasing the data availability and understanding the properties of MCSs could enhance the predictability of regional weather conditions, which is a primary objective of the High-resolution weather observations East of Dakar (DakE)-project. During the project, 14 automated weather stations have already been installed east of Dakar.
The objective of this study, which is part of the DakE-project, is to integrate the in-situ station data with satellite data to develop a precipitation nowcasting model that is optimally adapted to local conditions considering different spatial and temporal scales. An optical flow routine, based on statistical extrapolation of the current state of the atmosphere, is used for this purpose. To incorporate a stochastic term, which represents the unpredictable component, the STEPS (short-term ensemble prediction system) approach is applied. The skill of the forecast depends, among other things, on the geographical location, the spatial and temporal scales and the meteorological conditions, since developments that do not fulfil the steady-state assumption, such as the initiation, growth and termination of convective systems, are not resolved. The next step is to investigate whether these shortcomings can be compensated by implementing machine learning approaches.
References:
Anderson, Seonaid R., et al. "Nowcasting convective activity for the Sahel: A simple probabilistic approach using real‐time and historical satellite data on cloud‐top temperature." Quarterly Journal of the Royal Meteorological Society150.759 (2024): 597-617.
Mathon, V., Laurent, H., & Lebel, T. (2002). Mesoscale convective system rainfall in the Sahel. Journal of Applied Meteorology and Climatology, 41(11), 1081-1092.
Pulkkinen, S., Nerini, D., Pérez Hortal, A. A., Velasco-Forero, C., Seed, A., Germann, U., & Foresti, L. (2019). Pysteps: An open-source Python library for probabilistic precipitation nowcasting (v1. 0). Geoscientific Model Development, 12(10), 4185-4219.
Taylor, Christopher M., et al. "Nowcasting tracks of severe convective storms in West Africa from observations of land surface state." Environmental Research Letters 17.3 (2022): 034016.
Keywords: Nowcasting, Senegal, Mesoscale Convective System, Precipitation
How to cite: Berghöfer, M.-B., Monroy, D. L., and Härter, J. O.: Nowcasting precipitation events from mesoscale convective systems for Dakar, Senegal , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17279, https://doi.org/10.5194/egusphere-egu25-17279, 2025.
Extreme rainfall events represent a substantial risk to regions across the globe, including the Central Europe. The 2002 Central European flood was a devastating natural disaster affecting countries like Germany, Austria, the Czech Republic, and Hungary. Intense rainfall, saturated soils, and overflowing rivers caused severe flooding, displacing many and leading to significant loss of life. With damages exceeding €20 billion, it remains one of Europe’s most costly flood events, heavily impacting historic cities such as Prague and Dresden (Chorynski et al., 2012).
The spatial and temporal resolution of climate models can present challenges when simulating extreme rainfall events at regional or local scales in term of both the intensity and spatial distribution of precipitation. Therefore, In this study the implementation of high-resolution RCMs with "explicit" convection has been applied which directly resolves deep convection on the model grid without relying on parameterization schemes, known as convection-permitting (CP) models (Prein et al., 2013a,b). This study evaluates the performance of RegCM5 in simulating two consecutive extreme rainfall events (6–7 and 11–13 August 2002) over Central Europe and the Czech Republic, comparing 12 km and 3 km i.e. CP-RCM simulations along with sensitivity of planetary boundary layer (PBL) scheme Holtslag and UW. The results reveal significant discrepancies in the 12km RCM simulations, particularly in Czech Republic, where they struggle to capture the rainfall patterns of both events. The model configurations with UW PBL closely follow the observed extreme rainfall patterns, demonstrating improved alignment with the events. While CP simulations improve the representation of small-scale processes, accurately capturing localized extreme events, particularly the first spell, remains challenging. These findings highlight the potential of CP-RCM simulations for extreme precipitation in terms of climate adaptation, infrastructure development, and policy planning to mitigate the potential risks
How to cite: Verma, S., Crespo, N. M., Belda, M., Halenka, T., Huszar, P., and Holtanova, E.: Assessing the Performance of Convection-Permitting Regional Climate Models in Simulating the 2002 Extreme Rainfall Event Over Central Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19296, https://doi.org/10.5194/egusphere-egu25-19296, 2025.
IAGOS, or the In-service Aircraft for a Global Observing System project, is a European Infrastructure project consisting of scientific measurement packages attached to commercial aircraft. Operating since 1994, this programme provides a unique long-timeseries dataset of flight data across the globe, with thousands of flights per year providing a strong base for statistical studies.
Here, we use flight times derived from IAGOS metadata to quantify the role of the El Nino - Southern Oscillation (ENSO), the Quasi-Biennial Oscillation, the solar cycle and the North Atlantic Oscillation (NAO) on trans-Atlantic flight times. We do this both by subsetting the data in various ways and via regression methods. This allows us to statistically assess the effects of these large-scale atmospheric-dynamical processes on trans-Atlantic flight times. We also calculate the additional costs associated with these effects in terms of both carbon dioxide emissions and fuel costs, allowing us to understand how climate processes drive them.
Depending on season and direction of flights, we show that these four climate indices can explain as much as 1/3 of the total variance in trans-Atlantic flight times. At a flight-time level and particularly in winter, the NAO dominates flight times and is the most important factor in one-way fuel costs: flights at peak NAO+ can be as much as 83 minutes longer than the equivalent flight at peak NAO- when crossing the Atlantic. However, at a whole-dataset level, ENSO is shown to be much more important in driving net round-trip costs. We further estimate that the monthly cost of these four climate indices can be as high as 100 kT of additional CO2 or USD 20 million at 2023 flight volumes and fuel prices.
How to cite: Wright, C.: IAGOS estimates of climate-process costs for trans-Atlantic flights, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3842, https://doi.org/10.5194/egusphere-egu25-3842, 2025.
Posters virtual: Tue, 29 Apr, 14:00–15:45 | vPoster spot 5
EGU25-888 | ECS | Posters virtual | VPS2
Evaluation of Vertically Integrated Liquid Water Content in Indian Summer Monsoon Clouds Using Dual-Polarimetric Doppler Weather RadarTue, 29 Apr, 14:00–15:45 (CEST) | vP5.1
Accurate evaluation of cloud microphysical variables is essential for improving cloud parameterization and weather forecasting. However, obtaining high-resolution, spatially and temporally extensive observation dataset remains a challenge due to the limitations of in situ measurements. Therefore, this study addresses this gap by assessing existing equations for estimating vertically integrated liquid water content (VIL, kg/m²) from liquid water content (LWC, g/m3) using C-band dual-polarised doppler weather radar (DWR) data from IMD Jaipur station over 78 deep convective summer monsoon days in the years 2020-2022. A long-term climatological study (2003-2023) of total column cloud liquid water (TCCLW, kg/m2) from ERA5, liquid water cloud water content (LWCP, kg/m2) from MODIS and rainfall data from IMD, IMERG, and GPCP datasets is also performed. VIL is computed as the vertical integral of LWC across atmospheric layers using four reflectivity-LWC (Z-LWC) relationships and one reflectivity-differential reflectivity (Z, ZDR-LWC) relationship from existing literature. The performance of each equation is evaluated by comparing radar-derived VIL with satellite-derived parameters like MODIS cloud liquid water path (LWP, kg/m2) and TCCLW. The results show that VIL values increase with rainfall intensity and cloud vertical height, leading to higher estimation errors. Among the equations tested, the hybrid ZDR-based equation consistently demonstrated superior performance, particularly during high-intensity rainfall events, with lower root mean square error (RMSE) and mean absolute error (MAE) values which also captured more detailed spatial patterns of liquid water distribution and reduced bias, making it the most reliable estimator. Despite some limitations, such as beam blockage and slight spatial shifts due to interpolation, the study highlights the advantages of incorporating polarimetric radar products for VIL estimation. These findings provide a foundation for improving real-time precipitation forecasts and understanding cloud microphysics, with future work aimed at refining the methodology by addressing data gaps and enhancing cloud-type-specific estimators.
How to cite: Sabu, A., Syed, H. A., Das, S., Panda, S. K., Sharma, D., and Pal, J.: Evaluation of Vertically Integrated Liquid Water Content in Indian Summer Monsoon Clouds Using Dual-Polarimetric Doppler Weather Radar, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-888, https://doi.org/10.5194/egusphere-egu25-888, 2025.
