Improved Whitecap Quantification and Prediction Using Shipboard Remote Sensing and Machine Learning

Abstract

Whitecaps generated by wave breaking and air entrainment can be classified as active (stage A) or residual (stage B). Discrimination and measurement of each stage individually are essential for accurate parameterization of air-sea interaction processes, but conventional methods used for separation in visible images are subjective. This study provides a novel method to identify whitecap stages based on visible imagery using particle image velocimetry (PIV). A linear relationship was established between the lifetime of stage A and the timescale of averaged velocity. This novel method characterizes stage A whitecap lifetime using whitecap velocity and provides an objective approach to separate whitecap stages. To estimate active whitecap fraction, we introduced a pipeline for active whitecap fraction measurement. In this pipeline, a new horizon detection method is developed to stabilize and rectify images and a deep learning model based on U-Net is trained and validated to identify and extract active whitecaps. The model is applied to 48 hours of video footage collected during a cruise in Gulf of Mexico. It is determined that, as a function of wind speed, active whitecap fraction has significant variability and disparity compared to previous research. This finding indicates that secondary factors should be considered for accurate whitecap parameterization. This is explored using principal component analyses and random forest, which indicate sea surface temperature, swell and wave age are important to active whitecap fraction. The precise impact of sea surface temperature is further explored using analyses of variance (ANOVA), which suggest it has a positive correlation with active whitecap fraction. The decaying stage B foam with significant variability has been found to contribute 1.5 to 40 times more to total whitecap fraction than stage A foam. In this study, we present a novel model that describes the relationship between whitecap fraction and the evolution of whitecap area, providing a method to quantify the whitecap lifetime scale. The same data from a Gulf of Mexico cruise is processed using this method. The stage B lifetime scale shows weak positive correlation with active whitecap fraction and no correlation with sea surface temperature and wind speed