Lightning-Fast Convective Outlooks: Predicting Severe Convective Environments With Global AI-Based Weather Models.

Autor: Feldmann M; Institute of Geography Oeschger Centre for Climate Change Research University of Bern Bern Switzerland., Beucler T; Faculty of Geosciences and Environment Expertise Center for Climate Extremes University of Lausanne Lausanne Switzerland., Gomez M; Faculty of Geosciences and Environment Expertise Center for Climate Extremes University of Lausanne Lausanne Switzerland., Martius O; Institute of Geography Oeschger Centre for Climate Change Research University of Bern Bern Switzerland.
Jazyk: angličtina
Zdroj: Geophysical research letters [Geophys Res Lett] 2024 Nov 28; Vol. 51 (22), pp. e2024GL110960. Date of Electronic Publication: 2024 Nov 21.
DOI: 10.1029/2024GL110960
Abstrakt: Severe convective storms are among the most dangerous weather phenomena and accurate forecasts mitigate their impacts. The recently released suite of AI-based weather models produces medium-range forecasts within seconds, with a skill similar to state-of-the-art operational forecasts for variables on single levels. However, predicting severe thunderstorm environments requires accurate combinations of dynamic and thermodynamic variables and the vertical structure of the atmosphere. Advancing the assessment of AI-models toward process-based evaluations lays the foundation for hazard-driven applications. We assess the forecast skill of the top-performing AI-models GraphCast, Pangu-Weather and FourCastNet for convective parameters at lead-times up to 10 days against reanalysis and ECMWF's operational numerical weather prediction model IFS. In a case study and seasonal analyses, we see the best performance by GraphCast and Pangu-Weather: these models match or even exceed the performance of IFS for instability and shear. This opens opportunities for fast and inexpensive predictions of severe weather environments.
(© 2024. The Author(s).)
Databáze: MEDLINE
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