This session explores advancements in understanding and forecasting the severe weather, such as moist convection, and Mei-yu frontal systems, focusing on severe weather phenomena. It integrates AI methods, numerical modeling with particular attention to moist-convective models with intermediate complexity, e.g. Aeolus 2.0 & mcTRSW models, and observational techniques to enhance forecasting accuracy. Key topics include AI-based weather forecasting, ensemble prediction, mesoscale models, AI-driven nowcasting, and remote sensing technologies. The session also delves into the dynamics of moist convection, cloud formation, precipitation patterns, and their relationship with extreme weather and climate change. A segment on Mei-yu frontal systems highlights field experiments, cloud microphysics, and model improvements for better precipitation forecasts. The session fosters interdisciplinary discussions on breakthroughs and challenges in weather science.
Understanding and forecasting the severe weather
Co-organized by HS13/NP5
Conveners:
Yong Wang,
Masoud Rostami
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Co-conveners:
Maxime TaillardatECSECS,
Lesley De Cruz,
Bijan Fallah,
Monika FeldmannECSECS,
Stéphane Vannitsem