Global Prediction of Whitecap Coverage Using Transfer Learning and Satellite-Derived Data

Abstract

Whitecaps formed by breaking waves and air entrainment are readily visible on the ocean surface, with their high albedo significantly impacting the accuracy of remote sensing retrievals. While most traditional whitecap parameterizations rely only on wind speed, these approaches fail to explain complex variations in whitecap coverage. Satellite-derived whitecap data, based on brightness temperature variations from the WindSat radiometer, provide valuable global observations of whitecap coverage. To effectively utilize these satellite-derived data, we propose a transfer learning approach for predicting global whitecap coverage. The model is first pre-trained using modeling data based on statistical wave-breaking theory and subsequently fine-tuned with satellite-derived observations. The fine-tuned model demonstrates significant improvements over both the pre-trained model and traditional wind speed parameterizations when evaluated on independent satellite-derived test data. Through explainable deep learning methods, we identify that whitecap coverage is modulated by various atmospheric and wave parameters. The variable contribution analysis reveals the significant impacts of wind–wave interaction, wave states, and atmospheric stability on whitecap formation and coverage.

Keywords

• whitecap coverage
• wave breaking
• WindSat
• transfer learning
• explainable deep learning