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Compared with observation by ground meteorological stations, satellite monitoring of nighttime sea fog can provide a wider range of fog distribution. However, there is a lack of analysis on the applicability of several classical remote sensing retrieval algorithms for nighttime sea fog in Shandong coastal areas. Based on FY-4A geostationary meteorological satellite data, the method of dual channel difference (DCD), the method of temperature difference (DT), and the method of normalized fog index (NDFI) are used to retrieve the nighttime sea fog area in Shandong coastal area between January 2019 and December 2020. And the critical success index (CSI), the hit rate (HR), the probability of detection (POD), and the false alarm ratio (FAR) are used as index parameters to verify the retrieval results and discuss the applicability of the three methods based on the ground observation data. The results are shown below. 1) The nighttime sea fog often occurs in spring, winter and summer, and the frequency of nighttime sea fog is lower in autumn. In the statistical area, the occurrence frequency of nighttime sea fog in the Yellow Sea and the East China Sea is relatively high, followed by the South China Sea. 2) The performances of the three retrieval algorithms for nighttime sea fog are different in different seasons. The northern Bohai Sea performs better in winter, while other sea areas perform better in the fog season from April to July, and worse in July to September. Among the three inversion algorithms, the DT method performs the best because of its high CSI, high HR and low FAR. Due to the high POD and high FAR, the effect of DCD method comes second, and the NDFI method performs the worst. 3) The retrieval of nighttime sea fog in the Yellow Sea by the three algorithms is slightly better than that in the Bohai Sea. Among them, the DCD method and DT method have stronger retrieval ability of sea fog at night. 4) The applicability analysis of three retrieval algorithms of nighttime sea fog show that the retrieval ability of the DT method is stronger than that of the other two algorithms. The reason may be that the DT method considers the existence of inversion conditions in the algorithm process. The NDFI method is more sensitive to threshold.
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