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WeatherMesh-3: Fast and accurate operational global weather forecasting

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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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cs.LG 1

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2025 1

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CONDITIONAL 1

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EPT-2 Technical Report

cs.LG · 2025-07-13 · conditional · novelty 4.0

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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  • EPT-2 Technical Report cs.LG · 2025-07-13 · conditional · none · ref 5 · internal anchor

    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.