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

2026-07-23 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision & Pattern Recognition, Environmental Science & Earth Systems · Depth: Expert, quick

Summary

The Geospatial Diffusion-based Evolution Synthesis (GeoDES) model is introduced as a custom image-to-video diffusion model designed to address limitations in machine learning-based weather prediction. Existing regional models suffer from limited historical records and fixed boundaries, while global models are computationally expensive and too coarse for fine-grained storm dynamics. GeoDES overcomes these issues by focusing generation strictly on evolving storm structures, synthesizing physically consistent, high-fidelity weather events. This capability is crucial for stress-testing forecast models and expanding meteorological datasets. Evaluations show GeoDES outperforms prior methods, achieving 52% lower Peak Vorticity Error and 8% higher Anomaly Correlation Coefficient on the North Atlantic test set.

Key takeaway

For Machine Learning Engineers or Research Scientists developing weather prediction models, GeoDES offers a robust method to generate high-fidelity, physically consistent storm data. You should consider integrating GeoDES-generated synthetic data to augment your training datasets, particularly for cyclonic storm prediction, and to rigorously stress-test your forecast models, potentially improving their accuracy and resilience against extreme weather events.

Key insights

GeoDES synthesizes high-fidelity, physically consistent storm evolutions using an image-to-video diffusion model.

Principles

• Focusing generation on storm structure improves fidelity.
• Diffusion models can synthesize complex geospatial evolutions.
• Augmenting weather data enhances model stress-testing.

Method

GeoDES is a custom image-to-video diffusion model that generates evolving storm structures, ensuring physical consistency for weather event synthesis suitable for dataset expansion and model stress-testing.

In practice

• Generate synthetic storm data for model training.
• Stress-test existing weather forecast models.
• Expand limited regional meteorological datasets.

Topics

• Geospatial Diffusion Models
• Weather Augmentation
• Storm Prediction
• Image-to-Video Diffusion
• Meteorological Datasets

Best for: AI Scientist, Research Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.