CoAuthors

Bertrand Chapron (Ifremer, France); Douglas Vandemark (University of New Hampshire, US); Chen Wang (NUIST, China); Wenming Lin (NUIST, China); Daniele Hauser (LATMOS, France); Biao Zhang (NUIST, China); Yijun He (NUIST, China)

Sea state plays a crucial role in the global weather and climate models, which exhibits prominent spatial and temporal variability. In this study, a sea state ensemble composed of wind speed, significant wave height, inverse wave age and mean square velocity is uniquely created based on the concurrent observations by the China-France Oceanography Satellite (CFOSAT). We advance the sea state classification scheme by feeding the metrics into a k-means clustering algorithm. Six classes, each associated with unique wind and wave features are clearly distinguished. Global occurrence of frequency of each class can be robustly separated, reflecting the regional dependence of sea state conditions. The unique data set provides a first reference to help understand and monitor global sea state climate.