Storm-scale forecasting from observations

Accurate short-term prediction of clouds and precipitation is critical for severe weather warnings, aviation safety, and renewable energy operations. Traditional mesoscale numerical weather prediction models require significant modeling expertise and computational infrastructure. We introduce Stormscope, a family of transformer-based generative diffusion models trained directly on high-resolution, multi-band geostationary satellite imagery and ground-based radar over the Continental United States. Stormscope produces forecasts at a temporal resolution as high as 10 min and 6-km spatial resolution. Geostationary satellites and ground-based radar provide high-resolution, high-frequency observations essential for characterizing the evolving structure of the mesoscale atmosphere. Evaluated against extrapolation methods and operational mesoscale NWP models such as HRRR, Stormscope achieves leading performance on standard verification metrics including Fractions Skill Score and Continuous Ranked Probability Score across forecast horizons from 1 to 6 hours. By operating in native observation space, Stormscope establishes a new paradigm for AI-driven nowcasting with direct applicability to operational forecasting workflows. The approach is highly extensible, with demonstrated computational scaling to larger domains and higher resolutions. Critically, because Stormscope relies solely on globally ubiquitous satellite observations and radar where available, it offers a pathway to extend skillful mesoscale forecasting to oceanic regions and countries without existing strong operational mesoscale modeling programs.