WeatherMesh-3: Fast and accurate operational global weather forecasting
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Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 12 of its 19 references that are in the map. First posted on arXiv 28 Mar 2025.
Builds on
12 of 19 references.
References · 12
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Built on by
Citing papers per year: 2025: 3; 2026: 1.
4 papers.
- 2025Generalized Neighborhood Attention: Multi-dimensional Sparse Attention at the Speed of Lightcited by 18
- 2025ARROW: An Adaptive Rollout and Routing Method for Global Weather Forecastingcited by 8
- 2025EPT-2 Technical Reportcited by 4
- 2026SoftSEEPS improves ML-based precipitation forecastingcited by 0
- 2026SoftSEEPS improves ML-based precipitation forecasting7 Oct 2026
- 2025ARROW: An Adaptive Rollout and Routing Method for Global Weather Forecasting10 Oct 2025
- 2025EPT-2 Technical Report13 Jul 2025
- 2025Generalized Neighborhood Attention: Multi-dimensional Sparse Attention at the Speed of Light23 Apr 2025