基于物理约束的生成式AI，提升全球台风预报精度与效率
Tianmu-TC: Physics-constraints Generative Artificial Intelligence for Global Tropical Cyclone Forecasting
- 引入物理约束机制，生成可控且低不确定性的台风路径和强度预测
- 在多个洋盆中超越传统气象模型与主流AI模型，计算成本更低
- 适用于数据稀疏、快速增强等复杂场景，适合灾害预警与应急决策
台风带来强风和暴雨，威胁巨大。但其路径与强度预报仍面临大气混沌及初始误差快速放大的挑战，导致不确定性持续增加。尽管数值天气预报（NWP）与深度学习模型取得进展，仍存在计算开销大、复杂气象下表现不佳的问题。本文提出Tianmu-TC——一种基于物理约束的全球台风生成式预报框架。该模型基于西太平洋数据训练，通过物理约束实现可控输出并降低预测不确定性，显著提升预报可靠性。实验表明，Tianmu-TC在多个大洋盆均优于确定性与集合气象人工智能模型，以及权威的NWP系统（如ECMWF），且计算成本大幅降低。进一步验证显示，其在数据稀疏、异常路径、快速增强与减弱等复杂情景下依然表现良好。结果表明，物理约束生成式AI为可靠、高效的全球台风预报提供了新路径。
原文摘要 · Abstract (English)
Tropical cyclones (TCs) pose severe risks from strong winds and heavy rainfall. However, forecasting their track and intensity remains challenging due to chaotic atmosphere and the rapid amplification of initial condition errors, leading to growing forecast uncertainty. While numerical weather prediction (NWP) and deep learning models have made progress, they remain computationally demanding and often fail under complex meteorological scenarios. Here, we present Tianmu-TC, a physics-constraints generative framework for global TC forecasting. Trained on Western North Pacific data, Tianmu-TC leverages physics-constraints to generate controllable outputs with reduced uncertainty thus improving forecast reliability. Experiments show Tianmu-TC outperforms deterministic and ensemble meteorological artificial intelligence models and authoritative NWP systems such as ECMWF in global ocean basins, with significantly lower computational cost. We further show Tianmu-TC performs well in challenging scenarios such as data sparsity, anomaly tracks, rapid intensification and weakening. These findings suggest physics-constraints generative AI offers a promising approach for reliable, efficient global TC forecasting.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。