用于连续重建热带气旋近地面风廓线的物理约束隐式轮廓网络
A Physics-Constrained Implicit Profile Network for Continuous Reconstruction of Tropical Cyclone Near-Surface Wind Profiles
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- Shanghai Typhoon Institute, CMA（中国气象局上海台风研究所）
- University of Miami（迈阿密大学）
机构由 AI 辅助整理，请以论文原文为准。
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中文总结 AI 辅助
本研究提出一种结合机器学习与物理原理的物理约束隐式轮廓网络，利用二十余年NOAA飓风观测，从稀疏观测连续重建热带气旋近地面风廓线，可扩展卫星地表风测量为三维风场，助力飓风相关研究与应用。
中文摘要 AI 辅助
在海洋表面附近，热带气旋风速随高度快速变化，但直接测量有限，因为飞机投落送仅提供稀疏且不规则的观测数据。连续风廓线对于理解飓风边界层过程、改进风暴潮预测、支持近海工程以及评估沿海灾害至关重要。本研究中，我们开发了一种将机器学习与物理原理相结合的人工智能模型，用于从稀疏观测中重建连续风廓线。该模型旨在保留观测到的地表风，同时生成风速和风向随高度的真实变化。使用超过二十年的NOAA飓风观测进行的测试表明，该方法能在广泛的风暴强度范围内准确重现热带气旋风的垂直结构。该框架还可将卫星衍生的地表风测量扩展为三维近地表风场，为飓风研究、业务预报和工程应用提供新机遇。
英文摘要
Near the ocean surface, tropical cyclone winds change rapidly with height, but direct measurements are limited because aircraft dropsondes provide only sparse and irregular observations. Continuous wind profiles are important for understanding hurricane boundary-layer processes, improving storm-surge prediction, supporting offshore engineering, and assessing coastal hazards. In this study, we developed an artificial intelligence model that combines machine learning with physical principles to reconstruct continuous wind profiles from sparse observations. The model is designed to preserve the observed surface winds while generating realistic changes in wind speed and direction with height. Tests using more than two decades of NOAA hurricane observations show that the method accurately reproduces the vertical structure of tropical cyclone winds over a wide range of storm intensities. The framework can also extend satellite-derived surface wind measurements into three-dimensional near-surface wind fields, providing new opportunities for hurricane research, operational forecasting, and engineering applications.
