Severe Weather Forecasts from Artificial Intelligence Weather Prediction Models

Abstract Using recent observations of severe weather events, random forest (RF) machine learning models are trained to predict severe weather at 1–8-day lead times mimicking operational convective outlooks produced at the NOAA Storm Prediction Center. The RFs use environments simulated by three global artificial intelligence–based weather prediction (AIWP) models: PanguWeather, FourcastNet v2-small, and Graphcast. Individual hazard forecasts and any hazard forecasts are generated at all lead times and are compared to operational machine learning–based hazard guidance products from the Global Ensemble Forecast System Machine Learning Probabilities model. Skill metrics are computed for forecasts generated across 2024, and ensembles of RF forecasts are created for an example case to understand the value of AIWP-based machine learning guidance products. Skillful individual hazard forecasts were obtained out to day 7, but the operational machine learning guidance often performed better at longer lead times for the any hazard forecasts and at shorter lead times for individual hazards. Combining the AIWP inputs as a single metric (i.e., median) did overcome some of the d