TianQuan-S2S: A Subseasonal-to-Seasonal Global Weather Model via Incorporate Climatology State
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arXiv
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| Autori principali: | , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912962584772608 |
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| author | Li, Guowen Liu, Xintong Liu, Yang Chen, Mengxuan Cao, Shilei Wang, Xuehe Zheng, Juepeng Zhang, Jinxiao Liang, Haoyuan Zhang, Lixian Wang, Jiuke Jin, Meng Cheng, Hong Fu, Haohuan |
| author_facet | Li, Guowen Liu, Xintong Liu, Yang Chen, Mengxuan Cao, Shilei Wang, Xuehe Zheng, Juepeng Zhang, Jinxiao Liang, Haoyuan Zhang, Lixian Wang, Jiuke Jin, Meng Cheng, Hong Fu, Haohuan |
| contents | Accurate Subseasonal-to-Seasonal (S2S) forecasting is vital for decision-making in agriculture, energy production, and emergency management. However, it remains a challenging and underexplored problem due to the chaotic nature of the weather system. Recent data-driven studies have shown promising results, but their performance is limited by the inadequate incorporation of climate states and a model tendency to degrade, progressively losing fine-scale details and yielding over-smoothed forecasts. To overcome these limitations, we propose TianQuan-S2S, a global S2S forecasting model that integrates initial weather states with climatological means via incorporating climatology into patch embedding and enhancing variability capture through an uncertainty-augmented Transformer. Extensive experiments on the Earth Reanalysis 5 (ERA5) reanalysis dataset demonstrate that our model yields a significant improvement in both deterministic and ensemble forecasting over the climatology mean, traditional numerical methods, and data-driven models. Ablation studies empirically show the effectiveness of our model designs. Remarkably, our model outperforms skillful numerical ECMWF-S2S and advanced data-driven Fuxi-S2S in key meteorological variables. The code implementation can be found in https://github.com/zhangminglang42/TianQuan. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_09940 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TianQuan-S2S: A Subseasonal-to-Seasonal Global Weather Model via Incorporate Climatology State Li, Guowen Liu, Xintong Liu, Yang Chen, Mengxuan Cao, Shilei Wang, Xuehe Zheng, Juepeng Zhang, Jinxiao Liang, Haoyuan Zhang, Lixian Wang, Jiuke Jin, Meng Cheng, Hong Fu, Haohuan Machine Learning Accurate Subseasonal-to-Seasonal (S2S) forecasting is vital for decision-making in agriculture, energy production, and emergency management. However, it remains a challenging and underexplored problem due to the chaotic nature of the weather system. Recent data-driven studies have shown promising results, but their performance is limited by the inadequate incorporation of climate states and a model tendency to degrade, progressively losing fine-scale details and yielding over-smoothed forecasts. To overcome these limitations, we propose TianQuan-S2S, a global S2S forecasting model that integrates initial weather states with climatological means via incorporating climatology into patch embedding and enhancing variability capture through an uncertainty-augmented Transformer. Extensive experiments on the Earth Reanalysis 5 (ERA5) reanalysis dataset demonstrate that our model yields a significant improvement in both deterministic and ensemble forecasting over the climatology mean, traditional numerical methods, and data-driven models. Ablation studies empirically show the effectiveness of our model designs. Remarkably, our model outperforms skillful numerical ECMWF-S2S and advanced data-driven Fuxi-S2S in key meteorological variables. The code implementation can be found in https://github.com/zhangminglang42/TianQuan. |
| title | TianQuan-S2S: A Subseasonal-to-Seasonal Global Weather Model via Incorporate Climatology State |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2504.09940 |