用动态优化扰动提升台风预报精度，实现高效与物理合理的统一
A Synergistic Approach: Dynamics-AI Ensemble in Tropical Cyclone Forecasting
- 引入正交非线性最优扰动法生成符合动力学规律的初始误差
- 在台风路径预测中优于传统集合预报系统，提升确定性和概率预报能力
- 适合气象预报、AI+气候建模领域研究者参考
本研究针对基于AI的天气预报中集合预报系统面临的计算效率与动力一致性矛盾，提出一种基于正交条件非线性最优扰动（O-CNOPs）的智能优化集合预报体系。该方法生成符合FuXi模式非线性动力学特性的动态优化扰动，捕捉快速增长误差，同时保持物理合理性。关键创新在于构造正交扰动结构，反映主导动力控制机制，实现可解释的概率预报。实验表明，该系统在确定性与概率预报性能上均优于运行中的集成预报系统（IFS-EPS），为台风路径预报提供了更可靠方案。该成果推动了人工智能与物理约束结合的集合预报新范式，有望拓展至其他高影响天气系统的预报应用。
原文摘要 · Abstract (English)
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 FuXi model while maintaining plausibility. The key innovation lies in producing orthogonal perturbations that respect FuXi 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 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.
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