A novel sea state classification scheme based on global CFOSAT wind and wave observations
Event: 2022 Ocean Surface Topography Science Team Meeting
Presentation type: Type Forum only
Contribution: not provided
Sea state is recognized as an essential variable in the global climate system, and a complement to other widely used observations including sea surface temperature, sea level, and surface winds. Most previous global sea state investigations have utilized either model outputs or reanalysis data to explore the spatio-temporal characteristics. A main reason for this has been the lack of concurrent wind and wave measurements at global scale. This situation has improved with the launch of the China-France Oceanography SATellite (CFOSAT), which carries a wind scatterometer (SCAT) and a wave spectrometer (SWIM) aboard. In this study, we take advantage of the simultaneous CFOSAT wind and wave observations to develop a new approach for sea state classification. First, global average CFOSAT estimates of wind speed, significant wave height, inverse wave age and mean square slope are found to be consistent with previously reported data. A k-means clustering technique is applied to these measurements to classify sea state conditions into 6 pre-defined clusters in terms of the four-dimensional wind and wave ensemble. Each group has distinct wind and wave features, characterized by differing wind-swell dominance and sea state maturity, etc... The occurrence frequency of these groups across the globe portrays the spatial dominance of different sea state classes. Observed spatial distributions are expected to indicate variability in wave-induced momentum flux. Future efforts shall be devoted to building links between these sea state groups and local air-sea flux characteristics.