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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.22235 |
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| _version_ | 1866909555985743872 |
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| author | Du, Haoxing Kim, Lyna Creus-Costa, Joan Michaels, Jack Shetty, Anuj Hutchinson, Todd Riedel, Christopher Dean, John |
| author_facet | Du, Haoxing Kim, Lyna Creus-Costa, Joan Michaels, Jack Shetty, Anuj Hutchinson, Todd Riedel, Christopher Dean, John |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_22235 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | WeatherMesh-3: Fast and accurate operational global weather forecasting Du, Haoxing Kim, Lyna Creus-Costa, Joan Michaels, Jack Shetty, Anuj Hutchinson, Todd Riedel, Christopher Dean, John Machine Learning Artificial Intelligence 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. |
| title | WeatherMesh-3: Fast and accurate operational global weather forecasting |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2503.22235 |