Geospatial Diffusion-based Evolution Synthesis (GeoDES) for Storm-Centered Weather Augmentation

2026-07-21

AI Summary

This study addresses the longstanding challenge in numerical weather prediction wherein existing models struggle to simultaneously achieve high resolution and physical consistency in representing large-scale storm structures—regional models suffer from sparse observational data, while global models are computationally prohibitive and lack sufficient resolution. To overcome this, the authors propose GeoDES, a storm-centric image-to-video diffusion model that introduces, for the first time in meteorological synthesis, a tailored diffusion mechanism incorporating geospatial constraints and storm evolution priors. Evaluated on a North Atlantic test set, GeoDES reduces peak vorticity error by 52% and improves anomaly correlation by 8% over current state-of-the-art methods, demonstrating superior fidelity and physical plausibility. This approach establishes a new paradigm for data augmentation and stress-testing of forecasting systems.

Abstract

While machine learning-based weather models hold significant promise, they struggle to predict the detailed structure of large-scale weather systems such as cyclonic storms. Regional models are constrained by limited historical records within fixed geographic boundaries, while global models are computationally expensive and often operate at resolutions too coarse to capture fine-grained storm dynamics. To bridge this gap, we introduce the Geospatial Diffusion-based Evolution Synthesis (GeoDES) model, a custom image-to-video diffusion model. By focusing generation strictly on the evolving storm structure, GeoDES synthesizes physically consistent, high-fidelity weather events suitable for stress-testing forecast models and expanding meteorological datasets. Evaluations demonstrate that GeoDES outperforms prior methods on key metrics, achieving $52\%$ lower Peak Vorticity Error and $8\%$ higher Anomaly Correlation Coefficient than the next strongest methods on the North Atlantic test set.

Problem

• storm structure prediction
• weather modeling
• spatiotemporal resolution
• cyclonic storms
• data scarcity

Innovation

• diffusion model
• storm-centered weather augmentation
• geospatial synthesis
• high-fidelity weather generation
• image-to-video generation