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WeatherMesh-3: Fast and accurate operational global weather forecasting

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arxiv 2503.22235 v1 pith:FT4FEV47 submitted 2025-03-28 cs.LG cs.AI

classification cs.LGcs.AI
keywords operationalweatherforecastingglobalwm-3accuracylatentsingle
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 1 Pith paper

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  1. EPT-2 Technical Report

    cs.LG 2025-07 conditional novelty 4.0 of 10

    EPT-2 is a 9 km global AI weather model reported to beat Aurora, IFS HRES, and the ECMWF ENS mean over 0 to 240 hours on energy-relevant variables.

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