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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 (IR) 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 IR channels corrected ocean–fog perfectly day and night, with the highest F1 score of 97.93% and a proportion correct (PC) 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 PC 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 nonfog 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.

Near-surface relative humidity (RHns) is an essential meteorological parameter for water, carbon, and climate studies. However, spatially continuous RHns estimation is difficult due to the spatial discontinuity of in situ observations and the cloud contamination of satellite-based data. This article proposed machine learning-based models to estimate spatially continuous daily RHns at 1 km resolution over Japan and South Korea under all sky conditions and examined the spatiotemporal patterns of RHns. All sky estimation of RHns using machine learning has been rarely conducted, and it can be an alternative to the currently available RHns data mostly from numerical models, which have relatively low spatial resolution. We combined two schemes for clear sky conditions (scheme A, which uses satellite and reanalysis data) and cloudy sky conditions (scheme B, which uses reanalysis data solely). The relatively small numbers of data in extremely low and high RHns conditions (i.e., 70%, respectively) were augmented by applying an oversampling method to avoid biased training. The machine learning models based on random forest (RF) and XGBoost were trained and validated using 94 in situ observation sites from meteorological administrations of both countries from 2012 to 2017. The results showed that XGBoost produced slightly better performance than RF, and the spatially continuous RHns model combined based on XGBoost yielded the coefficient of determination of 0.72 and a root-mean-square error of 10.61%. Spatiotemporal patterns of the estimated RHns agreed with in situ observations, reflecting the effect of topography on RHns. We expect that the proposed RHns model could be used in various environmental studies that require RHns under all sky conditions as input data.