Learning Accurate Storm-Scale Evolution from Observations

Accurate short-term prediction of clouds and precipitation is critical for severe weather warnings,
aviation safety, and renewable energy operations. Forecasts at this timescale are provided by
numerical weather models and extrapolation methods, both of which have limitations. Mesoscale
numerical weather prediction models provide skillful forecasts at these scales but require significant
modeling expertise and computational infrastructure, which limits their accessibility.
Extrapolation-based methods are computationally lightweight but degrade rapidly beyond 1-2
hours. This presents an opportunity for data-driven forecasting directly from observations using
geostationary satellites and ground-based radar, which provide high-frequency, high-resolution observations
that capture mesoscale atmospheric evolution. We introduce Stormscope, a family of
transformer-based generative diffusion models trained on high-resolution, multi-band geostationary
satellite imagery and ground-based weather radar over the continental United States. Stormscope
produces forecasts at a temporal resolution of 10 min and 6km spatial resolution, which
are competitive with state-of-the-art mesoscale NWP models for lead times up to 6 hours. Its
generative architecture enables large ensemble forecasts of explicit mesoscale dynamics for robust
uncertainty quantification. Evaluated against extrapolation methods and operational mesoscale
NWP models, Stormscope achieves leading performance on standard deterministic and probabilistic
verification metrics across forecast horizons from 1 to 6 hours. By operating in observation
space, Stormscope establishes a new paradigm for multi-modal AI-driven nowcasting with direct
applicability to operational forecasting workflows. The approach is extensible, with demonstrated
computational scaling to larger domains and higher resolutions. As Stormscope relies on globally
available satellite observations (and radar where available), it offers a pathway to extend skillful
mesoscale forecasting to oceanic regions and countries without strong operational mesoscale
modeling programs.