# Development of Fog Detection Algorithm Using GK2A/AMI and Ground Data
## 4 Discussion  
In this study, we developed a decision tree type fog detection algorithm using GK2A/AMI and different types of auxiliary data, including the output of numerical forecast mode. For this, the most recent geostationary satellite data (GK2A/AMI, Himawari-8/AHI, Geostationary Operational Environmental Satellite (GOES)-R/Advanced Baseline Imager (ABI)), along with numerical model results, land/sea mask, land cover, snow cover data, etc. were required for fog detection using GK2A_FDA. The main advantages of GK2A_FDA were the different availability of satellite data based on the time of the day (day/dawn/night) and different backgrounds based on the geographic locations (land/coast/sea) considered. In addition, we not only evaluated the detection skills (POD, FAR, Bias, and ETS) but also the stability (their standard deviations) of GK2A_FDA using ground-observed visibility data for the 23 fog cases. The total average POD, FAR, Bias, and ETS for the 12 training (11 validation) cases were 0.80 (0.82), 0.37 (0.29), 1.28 (1.16), and 0.52 (0.59), respectively, with their standard deviations being 0.15 (0.19), 0.13 (0.28), 0.38 (1.00), and 0.11 (0.21), respectively. Table 7 shows the validation results of fog detection using various satellite and auxiliary data using the ground observation data for the past five years (2014-2019). Due to difference in the areas analyzed, analysis periods, and satellite and auxiliary data used in this study, directly comparing them was difficult. However, when the number of fog cases and visibility meter data were used for validation, the detection level of the fog detection algorithm developed in this study was found to be similar or superior to that of the past algorithms. In previous studies, all PODs and FARs were found to be less than 0.7 but greater than 0.31, respectively. Figure 12: Scatter plots between the number of fogs and statistical skill scores (POD and FAR) based on the time of the day ((**a**) day, (**b**) night, (**c**) dawn/twilight, and (**d**) total). Correlation coefficients (Corr.) between the number of fogs and statistical skill scores are shown at the top of each figure according to the training cases (T), validation cases (V), and all cases (A). Square and triangle points in each figure indicate POD and FAR, respectively. The training and validation cases are shown separately, with or without shading. Although we tried to minimize the spatio-temporal discontinuities in the fog detection results, non-negligible spatio-temporal discontinuities existed in the fog detection results, as shown in Figure 13. Temporal and spatial discontinuities were found at the temporal boundary between dawn and daytime (Figure 13a,b), spatial boundary, and coastal lines (Figure 13c,d). Compared to the fog detection result at dawn (Figure 13a), a new fog was abruptly detected during daytime at the right side of the boundary line (Figure 13b). The coastal line contains many non-fog areas surrounded by the fog areas (Figure 13c,d). When considering the strong spatial continuity of the sea fog, we obtained blank areas, which can be considered as a type of spatial discontinuity. We optimized the threshold of SZA for separating dawn and daytime using various sensitivity tests; however, the discontinuities continued in many cases. A complete elimination of spatio-temporal discontinuities was impossible because the channels used were fundamentally different. Despite these fundamental limitations, more studies should be carried out for continuously detecting the fog, without considering time and geographic location.