ArticleGeophysical research letters2024
Lightning-Fast Convective Outlooks: Predicting Severe Convective Environments With Global AI-Based Weather Models.
Article in Geophysical research letters, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Evaluating the skill of AI and physics-based models for February 2020 North Atlantic storm hazards.npj natural hazards · 2026Article
- Improving predictions of convective storm wind gusts through statistical post-processing of neural weather models.npj natural hazards · 2025Article
- Lightning-Fast Convective Outlooks: Predicting Severe Convective Environments With Global AI-Based Weather Models.Geophysical research letters · 2024Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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