Lightning-Fast Convective Outlooks: Predicting Severe Convective Environments with Global AI-based Weather Models

TL;DR

This paper evaluates AI-based weather models' ability to predict severe convective environments, demonstrating that some models match or outperform traditional forecasts in accuracy and speed for up to 10 days ahead.

Contribution

It provides a process-based assessment of AI weather models for severe storm prediction, highlighting their potential for hazard-driven applications and operational use.

Findings

GraphCast and Pangu-Weather outperform IFS in predicting instability and shear.

AI models produce medium-range forecasts within seconds with skill comparable to operational models.

AI models enable fast, inexpensive severe weather environment predictions.

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 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.