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arXiv · 2503.22235

WeatherMesh-3: Fast and accurate operational global weather forecasting

Haoxing Du·Lyna Kim·Joan Creus-Costa·Jack Michaels·Anuj Shetty·Todd Hutchinson·Christopher Riedel·John Dean
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Abstract

We present WeatherMesh-3 (WM-3), an operational transformer-based global weather forecasting system that improves the state of the art in both accuracy and computational efficiency. We introduce the following advances: 1) a latent rollout that enables arbitrary-length predictions in latent space without intermediate encoding or decoding; and 2) a modular architecture that flexibly utilizes mixed-horizon processors and encodes multiple real-time analyses to create blended initial conditions. WM-3 generates 14-day global forecasts at 0.25-degree resolution in 12 seconds on a single RTX 4090. This represents a >100,000-fold speedup over traditional NWP approaches while achieving superior accuracy with up to 37.7% improvement in RMSE over operational models, requiring only a single consumer-grade GPU for deployment. We aim for WM-3 to democratize weather forecasting by providing an accessible, lightweight model for operational use while pushing the performance boundaries of machine learning-based weather prediction.

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Haoxing Du, Lyna Kim, Joan Creus-Costa, Jack Michaels, Anuj Shetty, Todd Hutchinson, Christopher Riedel, John Dean. 2025-03-28. WeatherMesh-3: Fast and accurate operational global weather forecasting. https://arxiv.org/abs/2503.22235

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