天目-TC：用于全球热带气旋预报的物理约束生成式人工智能
Tianmu-TC: Physics-constraints Generative Artificial Intelligence for Global Tropical Cyclone Forecasting
- College of Computer Science and Technology, Zhejiang University of Technology（浙江工业大学计算机科学与技术学院）
- School of Computer Science and Engineering, Tianjin University of Technology（天津理工大学计算机科学与工程学院）
- School of Earth Sciences, Zhejiang University（浙江大学地球科学学院）
- School of Control Science and Engineering, Shandong University（山东大学控制科学与工程学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究针对热带气旋预报的不确定性与高成本问题，提出物理约束生成式框架Tianmu-TC，该模型在全球各洋盆的预报性能优于权威系统，且在复杂场景下表现良好，计算成本显著更低。
AI中文摘要：
热带气旋（TC）因强风与强降雨带来严重风险，但受大气混沌特性及初始条件误差快速放大影响，其路径与强度预报仍具挑战性，导致预报不确定性不断增大。尽管数值天气预报（NWP）与深度学习模型已取得一定进展，但计算成本高且在复杂气象场景下常失效。本文提出Tianmu-TC，一种用于全球TC预报的物理约束生成式框架，基于西北太平洋数据训练，该模型利用物理约束生成可控输出，降低不确定性以提升预报可靠性。实验显示，Tianmu-TC在全球各洋盆中表现优于确定性与集合气象人工智能模型及ECMWF等权威NWP系统，且计算成本显著更低；其在数据稀疏、异常路径、快速增强及减弱等挑战性场景中亦表现良好。这些结果表明，物理约束生成式AI为可靠高效的全球TC预报提供了极具前景的途径。
英文摘要：
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.
