2026-08-01 中国科学院（CAS）
The research is featured on the latest issue of Advances in Atmospheric Sciences. (Image by AAS)
＜関連情報＞
- https://english.cas.cn/newsroom/cas-in-media/202608/t20260803_1186836.shtml
- https://link.springer.com/article/10.1007/s00376-026-5784-3
相乗効果のあるアプローチ：熱帯低気圧予測における力学とAIのアンサンブル A Synergistic Approach: Dynamics–AI Ensemble in Tropical Cyclone Forecasting
Yonghui Li,Wansuo Duan,Hao Li,Wei Han,Han Zhang & Yinuo Li
Advances in Atmospheric Sciences Published:10 June 2026
DOI:https://doi.org/10.1007/s00376-026-5784-3
Abstract
This study addresses a critical challenge in AI-based weather forecasting by developing an AI-driven optimized ensemble forecast system using Orthogonal Conditional Nonlinear Optimal Perturbations (O-CNOPs). The system bridges the gap between computational efficiency and dynamic consistency in tropical cyclone (TC) forecasting. Unlike conventional ensembles limited by computational costs or AI ensembles constrained by inadequate perturbation methods, O-CNOPs generate dynamically optimized perturbations that capture fast-growing errors of the FuXi model while maintaining plausibility. The key innovation lies in producing orthogonal perturbations that respect FuXi’s nonlinear dynamics, yielding structures reflecting dominant dynamical controls and physically interpretable probabilistic forecasts. Demonstrating superior deterministic and probabilistic skills over the operational Integrated Forecasting System Ensemble Prediction System, this work establishes a new paradigm combining AI’s computational advantages with rigorous dynamical constraints. Success in TC track forecasting paves the way for reliable ensemble forecasts of other high-impact weather systems, marking a major step toward operational AI-based ensemble forecasting.
