利用多球体耦合概率模型实现极端事件的精妙亚季至季预测
Skillful Subseasonal-to-Seasonal Forecasting of Extreme Events with a Multi-Sphere Coupled Probabilistic Model
- Tongji University（同济大学）
- Fudan University（复旦大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
TianXing-S2S通过多球体耦合概率模型实现极端事件的亚季至季预测，优于现有系统并具备长期稳定预报能力。
AI中文摘要：
准确预测极端事件的亚季至季（S2S）预测对于应对加速的气候变化下的资源规划和灾害缓解至关重要。然而，由于复杂的多球体相互作用和大气固有不确定性，此类预测仍然具有挑战性。本文提出了TianXing-S2S，一种用于全球S2S每日集合预报的多球体耦合概率模型。TianXing-S2S首先将多样化的多球体预测器编码到一个紧凑的潜在空间中，然后利用扩散模型生成每日集合预报。在去噪器中引入了一种基于最优传输（OT）的新型耦合模块，以优化大气和多球体边界条件之间的相互作用。在关键大气变量上，TianXing-S2S在1.5分辨率下优于欧洲中期天气预报中心（ECMWF）S2S系统和FuXi-S2S在45天每日均值集合预报中。我们的模型实现了对极端事件的精妙亚季预测，包括热浪和异常降水，识别出土壤湿度作为关键的前兆信号。此外，我们证明TianXing-S2S可以生成稳定至180天的预报，建立了应对全球变暖的世界S2S研究的稳健框架。
英文摘要：
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.
