Improved Ocean–Fog Monitoring Using Himawari-8 Geostationary Satellite Data Based on Machine Learning With SHAP-Based Model Interpretation

Abstract

Ocean-fog is a type of fog that forms over the ocean and has a visibility of less than 1 km. Ocean-fog frequently causes incidents over oceanic and coastal regions; ocean-fog detection is required regardless of the time of day. Ocean-fog has distinct thermo-optical properties, and spatially and temporally extensive ocean-fog detection methods based on geostationary satellites are typically employed. Infrared channels of Himawari-8 were used to construct three machine learning models for the continuous detection of ocean-fog. In contrast, visible channels are valid only during the daytime. As control models, we used fog products from the National Meteorological Satellite Center (NMSC) and machine learning models trained by adding a visible channel. The extreme gradient boosting model utilizing infrared channels corrected ocean-fog perfectly day and night, with the highest F1 score of 97.93% and a proportion correct of 98.59% throughout the day. In contrast, the NMSC product had a probability of detection of 87.14%, an F1 score of 93.13%, and a proportion correct of 71.9%. As demonstrated by the qualitative evaluation, the NMSC product overestimates clouds over small and coarsely textured ocean-fog regions. In contrast, the proposed model distinguishes between ocean-fog, clear skies, and clouds at the pixel scale. The Shapley additive explanation analysis demonstrated that the difference between channels 14 and 7 was very useful for ocean-fog detection at night, and its extremely low values contributed significantly to distinguishing non-fog during the daytime. Channel 15, affected by water vapor absorption, contributed most to ocean-fog detection among atmospheric window channels. The research findings can be used to improve operational ocean-fog detection and forecasting.