A Novel Sea State Classification Scheme of the Global CFOSAT Wind and Wave Observations

IF 3.3 2区 地球科学 Q1 OCEANOGRAPHY Journal of Geophysical Research-Oceans Pub Date : 2024-11-03 DOI:10.1029/2023JC020686
Huimin Li, Bertrand Chapron, Douglas Vandemark, Wenming Lin, Danièle Hauser, Yijun He, Fabrice Collard
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Abstract

Ocean waves are essential elements across the air-sea interface, regulating momentum and energy transfer. The mixture of wind sea and ocean swell coupled with surface winds results in diverse sea state conditions that modify the local air-sea interaction. Previous classifications of wind waves and swells are mostly binary that are insufficient to represent the complexity of sea states. In this study, we utilize wind and wave measurements from the China-France Oceanography Satellite (CFOSAT) to construct an observational wind-wave ensemble. Four key parameters: wind speed, significant wave height, inverse wave age, and spectral width are selected out of six variables based on their correlations. Employing the unsupervised learning of k-means clustering, global sea states are categorized into six distinct classes. These classes, characterized by unique centroids and separated in the feature space, represent specific wind regimes and degrees of wave development. Global occurrence highlights that each sea state is region-specific, bridging the spatial gap of swell and wind sea dominated areas, respectively. This new grouping scheme complements the traditional wind sea and/or swell classification by resolving the diversity of wave regimes. The six-class classification enables us to identify transitional states and hybrid conditions that may have been overlooked in the binary classification scheme, which shall help investigate the impact of ocean waves on the air-sea interaction under varying sea states.

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全球 CFOSAT 风浪观测数据的新型海况分类方案
海浪是整个海气界面的基本要素,调节着动量和能量的传递。风海和海涌的混合与海面风的作用导致了不同的海况条件,从而改变了当地的海气相互作用。以往对风浪和涌浪的分类大多是二元分类,不足以体现海况的复杂性。在本研究中,我们利用中法海洋卫星(CFOSAT)的风浪测量数据构建了观测风浪集合。根据风速、显波高、反波龄和频谱宽度这四个关键参数的相关性,从六个变量中选取了这四个参数。通过 k-means 聚类的无监督学习,全球海况被分为六个不同的类别。这些类别以独特的中心点为特征,在特征空间中相互分离,代表了特定的风况和波浪发展程度。全球海况的出现凸显了每种海况都具有区域特异性,分别弥补了涌浪和风海主导区域的空间差距。这种新的分组方案是对传统的风海和/或涌浪分类的补充,解决了波浪机制的多样性问题。六级分类使我们能够识别二元分类方案中可能被忽视的过渡状态和混合状态,这将有助于研究不同海况下海浪对海气相互作用的影响。
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来源期刊
Journal of Geophysical Research-Oceans
Journal of Geophysical Research-Oceans Earth and Planetary Sciences-Oceanography
CiteScore
7.00
自引率
13.90%
发文量
429
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