Lightning-Fast Convective Outlooks: Predicting Severe Convective Environments with Global AI-based Weather Models
This enables fast and inexpensive predictions of severe convective storms, which are dangerous weather phenomena, though it is incremental as it applies existing AI models to a new domain-specific problem.
The study evaluated three top-performing AI-based weather models for predicting severe convective environments up to 10 days ahead, finding that GraphCast and Pangu-Weather matched or exceeded the performance of the operational ECMWF IFS model in terms of instability and shear.
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 towards process-based evaluations lays the foundation for hazard-driven applications. We assess the forecast skill of three top-performing AI-models for convective parameters at lead-times of 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.