TianQuan-S2S: A Subseasonal-to-Seasonal Global Weather Model via Incorporate Climatology State

• URL: http://arxiv.org/abs/2504.09940v4
• Date: Sun, 26 Oct 2025 11:45:48 GMT
• Title: TianQuan-S2S: A Subseasonal-to-Seasonal Global Weather Model via Incorporate Climatology State
• Authors: Guowen Li, Xintong Liu, Yang Liu, Mengxuan Chen, Shilei Cao, Xuehe Wang, Juepeng Zheng, Jinxiao Zhang, Haoyuan Liang, Lixian Zhang, Jiuke Wang, Meng Jin, Hong Cheng, Haohuan Fu,
• Abstract summary: Accurate Subseasonal-to-Seasonal (S2S) forecasting is vital for decision-making in agriculture, energy production, and emergency management.<n>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.<n>We propose TianQuan-S2S, a global S2S forecasting model that integrates initial weather states with climatological means via climatology into patch embedding and enhancing variability capture through an uncertainty-augmented Transformer.
• Score: 23.8714341737652
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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