WoFSCast: A Machine Learning Model for Predicting Thunderstorms at Watch‐to‐Warning Scales

Abstract Developing AI models that match or exceed the forecast skill of numerical weather prediction (NWP) systems but run much more quickly is a burgeoning area of research. Most AI‐NWP models, however, have been trained on global ECMWF Reanalysis version 5 data, which does not resolve storm‐scale evolution. We have therefore adapted Google's GraphCast framework for limited‐area, storm‐scale domains, then trained on archived forecasts from the Warn‐on‐Forecast System (WoFS), a convection‐allowing ensemble with 5‐min forecast output. We evaluate the WoFSCast predictions using object‐based verification, grid‐based verification, spatial storm structure assessments, and spectra analysis. The WoFSCast closely emulates the WoFS environment fields, matches 70%–80% of WoFS storms out to 2‐hr forecast times, and suffers only modest blurring. When verified against observed storms, WoFSCast produces contingency table statistics and fractions skill scores similar to WoFS. WoFSCast demonstrates that AI‐NWP can be extended to rapidly evolving, small‐scale phenomena like thunderstorms.