FAST-ML：一种用于热带气旋强度预报的物理-机器学习混合框架
FAST-ML: A Hybrid Physics-Machine Learning Framework for Tropical Cyclone Intensity Forecasting
- Georgia Institute of Technology（佐治亚理工学院）
- Cornell University（康奈尔大学）
- Sandia National Laboratories（桑迪亚国家实验室）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
FAST-ML提出物理-机器学习混合框架，通过双流神经参数化优化通风控制，在物理约束下提升热带气旋强度预报精度，显著降低CRPS和误报率，并具备跨流域迁移能力。
中文摘要 AI 辅助
快速增强（RI）仍然是热带气旋（TC）预报中最具影响力和最困难的方面之一。尽管全物理数值天气预报模型能够表示控制RI的过程，但解析风暴与环境之间的相互作用在计算上仍然昂贵，而纯粹的数据驱动方法往往缺乏物理可解释性。我们提出了FAST-ML，一个将数据驱动效率与物理约束相结合的混合框架。一个物理信息驱动的双流神经参数化方案输入三维ERA5场，以诊断通风控制——环境风切变和对流层中层熵亏缺。通过可微分的FAST强度模型端到端优化这些参数，该架构为观测驱动的参数优化建立了稳健的新范式，确保风暴演变严格受热力学原理约束。通过更好地捕捉风暴的连续强度演变，FAST-ML改进了其物理基线，在预报提前期上降低了集合CRPS，在60小时时降低约31%，并将RI误报率几乎减半，且未牺牲检测能力。在100成员的集合配置中，FAST-ML在评估的输入配置下对选定风暴产生的强度预报与FNV3相当。此外，对选定的东太平洋风暴进行的零样本测试提供了跨流域可迁移性的令人鼓舞的证据。FAST-ML提供了一个模块化的强度预报框架，可与外部提供的风暴路径和环境场耦合。它证明了在物理约束动力学中进行观测驱动的参数学习能够同时提高准确性、可解释性和计算效率。
英文摘要
Rapid intensification (RI) remains one of the most consequential and difficult aspects of tropical cyclone (TC) forecasting. Although full-physics numerical weather prediction models can represent the processes governing RI, resolving storm-environment interactions remains computationally expensive, while purely data-driven approaches often lack physical interpretability. We present FAST-ML, a hybrid framework that bridges data-driven efficiency with physical constraints. A physically informed dual-stream neural parameterization ingests 3D ERA5 fields to diagnose ventilation controls---environmental wind shear and mid-level entropy deficit. By optimizing these parameters end-to-end through a differentiable FAST intensity model, this architecture establishes a robust new paradigm for observation-driven parameter optimization, ensuring storm evolution remains strictly governed by thermodynamic principles. By better capturing the storm's continuous intensity evolution, FAST-ML improves upon its physical baseline, reducing ensemble CRPS across forecast lead times, with a reduction of approximately 31% at 60 h and nearly halving the RI false alarm ratio without sacrificing detection skill. In a 100-member ensemble configuration, FAST-ML produces intensity forecasts comparable to FNV3 for selected storms under the evaluated input configurations. Furthermore, zero-shot tests on selected Eastern Pacific storms provide encouraging evidence of cross-basin transferability. FAST-ML provides a modular intensity forecasting framework that can be coupled with externally supplied storm tracks and environmental fields. It demonstrates that observation-driven parameter learning within physically constrained dynamics simultaneously enhances accuracy, interpretability, and computational efficiency.
