用多圈层耦合模型提升极端天气的中长期预报精度
Skillful Subseasonal-to-Seasonal Forecasting of Extreme Events with a Multi-Sphere Coupled Probabilistic Model
- 通过最优传输机制耦合大气与多圈层边界条件
- 在45天预报中优于ECMWF和FuXi-S2S系统
- 可稳定生成180天滚动预测，适合气候预警应用
在气候变化加速背景下，准确预测次季节至季节尺度（S2S）极端事件对资源规划与灾害减缓至关重要。然而，复杂的多圈层相互作用与大气内在不确定性使此类预测仍具挑战。本文提出TianXing-S2S，一个全球范围的多圈层耦合概率模型，用于每日集合预报。该模型首先将多元多圈层预测因子编码至紧凑隐空间，再利用扩散模型生成每日集合预报。创新性地引入基于最优传输（OT）的耦合模块，优化大气与多圈层边界条件间的交互。在关键大气变量上，TianXing-S2S在1.5°分辨率下45天日均集合预报中超越欧洲中期天气预报中心（ECMWF）S2S系统及FuXi-S2S系统。模型成功实现热浪与异常降水等极端事件的技能性预报，并识别土壤湿度为关键前兆信号。此外，我们验证了TianXing-S2S可生成长达180天的稳定滚动预报，为变暖世界中的S2S研究建立稳健框架。
原文摘要 · Abstract (English)
Accurate subseasonal-to-seasonal (S2S) prediction of extreme events is critical for resource planning and disaster mitigation under accelerating climate change. However, such predictions remain challenging due to complex multi-sphere interactions and intrinsic atmospheric uncertainty. Here we present TianXing-S2S, a multi-sphere coupled probabilistic model for global S2S daily ensemble forecast. TianXing-S2S first encodes diverse multi-sphere predictors into a compact latent space, then employs a diffusion model to generate daily ensemble forecasts. A novel coupling module based on optimal transport (OT) is incorporated in the denoiser to optimize the interactions between atmospheric and multi-sphere boundary conditions. Across key atmospheric variables, TianXing-S2S outperforms both the European Centre for Medium-Range Weather Forecasts (ECMWF) S2S system and FuXi-S2S in 45-day daily-mean ensemble forecasts at 1.5 resolution. Our model achieves skillful subseasonal prediction of extreme events including heat waves and anomalous precipitation, identifying soil moisture as a critical precursor signal. Furthermore, we demonstrate that TianXing-S2S can generate stable rollout forecasts up to 180 days, establishing a robust framework for S2S research in a warming world.
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