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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2603.28173 |
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| _version_ | 1866912988208824320 |
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| author | Chen, Weiqi Wang, Wenwei Yuan, Qilong Shen, Lefei Peng, Bingqing Chen, Jiawei Wu, Bo Sun, Liang |
| author_facet | Chen, Weiqi Wang, Wenwei Yuan, Qilong Shen, Lefei Peng, Bingqing Chen, Jiawei Wu, Bo Sun, Liang |
| contents | Data-driven weather models have advanced global medium-range forecasting, yet high-resolution regional prediction remains challenging due to unresolved multiscale interactions between large-scale dynamics and small-scale processes such as terrain-induced circulations and coastal effects. This paper presents a global-regional coupling framework for kilometer-scale regional weather forecasting that synergistically couples a pretrained Transformer-based global model with a high-resolution regional network via a novel bidirectional coupling module, ScaleMixer. ScaleMixer dynamically identifies meteorologically critical regions through adaptive key-position sampling and enables cross-scale feature interaction through dedicated attention mechanisms. The framework produces forecasts at $0.05^\circ$ ($\sim 5 \mathrm{km}$ ) and 1-hour resolution over China, significantly outperforming operational NWP and AI baselines on both gridded reanalysis data and real-time weather station observations. It exhibits exceptional skill in capturing fine-grained phenomena such as orographic wind patterns and Foehn warming, demonstrating effective global-scale coherence with high-resolution fidelity. The code is available at https://anonymous.4open.science/r/ScaleMixer-6B66. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_28173 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Skillful Kilometer-Scale Regional Weather Forecasting via Global and Regional Coupling Chen, Weiqi Wang, Wenwei Yuan, Qilong Shen, Lefei Peng, Bingqing Chen, Jiawei Wu, Bo Sun, Liang Machine Learning Artificial Intelligence Data-driven weather models have advanced global medium-range forecasting, yet high-resolution regional prediction remains challenging due to unresolved multiscale interactions between large-scale dynamics and small-scale processes such as terrain-induced circulations and coastal effects. This paper presents a global-regional coupling framework for kilometer-scale regional weather forecasting that synergistically couples a pretrained Transformer-based global model with a high-resolution regional network via a novel bidirectional coupling module, ScaleMixer. ScaleMixer dynamically identifies meteorologically critical regions through adaptive key-position sampling and enables cross-scale feature interaction through dedicated attention mechanisms. The framework produces forecasts at $0.05^\circ$ ($\sim 5 \mathrm{km}$ ) and 1-hour resolution over China, significantly outperforming operational NWP and AI baselines on both gridded reanalysis data and real-time weather station observations. It exhibits exceptional skill in capturing fine-grained phenomena such as orographic wind patterns and Foehn warming, demonstrating effective global-scale coherence with high-resolution fidelity. The code is available at https://anonymous.4open.science/r/ScaleMixer-6B66. |
| title | Skillful Kilometer-Scale Regional Weather Forecasting via Global and Regional Coupling |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2603.28173 |