[2603.20250] Developing Machine Learning-Based Watch-to-Warning Severe Weather Guidance from the Warn-on-Forecast System

Physics > Atmospheric and Oceanic Physics arXiv:2603.20250 (physics) [Submitted on 10 Mar 2026] Title:Developing Machine Learning-Based Watch-to-Warning Severe Weather Guidance from the Warn-on-Forecast System Authors:Montgomery Flora, Samuel Varga, Corey Potvin, Noah Lang View a PDF of the paper titled Developing Machine Learning-Based Watch-to-Warning Severe Weather Guidance from the Warn-on-Forecast System, by Montgomery Flora and 3 other authors View PDF HTML (experimental) Abstract:While machine learning (ML) post-processing of convection-allowing model (CAM) output for severe weather hazards (large hail, damaging winds, and/or tornadoes) has shown promise for very short lead times (0-3 hours), its application to slightly longer forecast windows remains relatively underexplored. In this study, we develop and evaluate a grid-based ML framework to predict the probability of severe weather hazards over the next 2-6 hours using forecast output from the Warn-on-Forecast System (WoFS). Our dataset includes WoFS ensemble forecasts valid every 5 minutes out to 6 hours from 108 days during the 2019--2023 NOAA Hazardous Weather Testbed Spring Forecasting Experiments. We train ML models to generate probabilistic forecasts of severe weather akin to Storm Prediction Center outlooks (i.e., likelihood of a tornado, severe wind, or severe hail event within 36 km of each point). We compare a histogram gradient-boosted tree (HGBT) model and a deep learning U-Net approach against a care...