# Development of Fog Detection Algorithm Using GK2A/AMI and Ground Data
## 3 Results
### Validation Results of Fog Detection Algorithm
#### 3.2.2 Quantitative Validation Results  
Table 5 shows the results of quantitative validation using the visibility meter for the 12 training cases. The total average POD, FAR, Bias, and ETS were 0.80, 0.37, 1.28, and 0.52, respectively. In general, GK2_FDA effectively detected the fog irrespective of the time and geographic location, in terms of POD, FAR, Bias, and ETS. However, the detection skill of GK2A_FDA was clearly dependent on the geographic location and time. In addition, the stability (SD: standard deviation) of GK2_FDA was slightly different based on the geographic location and time. As shown by the KSS, the temporally averaged detection level on the land (KSS: 0.46) was better than that (KSS: 0.31) over the coastal area, and the spatially averaged detection level during nighttime (POD: 0.80, FAR: 0.34, and ETS: 0.54) and dawn (POD: 0.85, FAR: 0.36, and ETS: 0.53) was better than that (POD: 0.77, FAR: 0.47, and ETS: 0.44) during daytime. The SDs of POD, FAR, Bias, and ETS (0.16, 0.10, 0.28, and 0.11) were the smallest at night, which indicated that the fog detection algorithm for night was least sensitive to the fog cases. As shown in Table 6 (validation cases), the fog detection level of GK2_FDA was very good and stable regardless of the region and time; the total POD, FAR, Bias, and ETS and their SDs were significantly similar to that of the training cases. The total average POD, FAR, Bias, and ETS for the 11 validation cases were 0.82, 0.29, 1.16, and 0.59, respectively; however, the SDs of POD, FAR, Bias, and ETS were 0.19, 0.28, 1.00, and 0.21, respectively, slightly higher than that of the training cases. In general, the fog detection level was slightly higher on land and slightly lower in the coastal area, irrespective of the time. In terms of fog detection time, the detection level was improved at all times, especially during the day (POD: 0.77 to 0.78, FAR: 0.47 to 0.30). However, GK2A_FDA over-detected the fog irrespective of the geographic location and time, similar to the training cases. Further, the detection \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Location**} & \multicolumn{2}{c}{**Land**} & \multicolumn{2}{c}{**Coast**} & \multicolumn{2}{c}{**Total**} \\ \hline
**Time** & & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** \\ \hline \multirow{6}{*}{Day} & POD & 0.79 & 0.26 & 0.71 & 0.22 & 0.77 & 0.20 \\  & FAR & 0.48 & 0.21 & 0.42 & 0.25 & 0.47 & 0.19 \\  & KSS & 0.30 & 0.35 & 0.29 & 0.20 & 0.30 & 0.26 \\  & Bias & 1.52 & 0.93 & 1.23 & 0.77 & 1.47 & 0.78 \\  & ETS & 0.44 & 0.19 & 0.46 & 0.15 & 0.44 & 0.16 \\ \hline \multirow{6}{*}{Night} & POD & 0.83 & 0.28 & 0.71 & 0.23 & 0.80 & 0.16 \\  & FAR & 0.32 & 0.18 & 0.42 & 0.10 & 0.34 & 0.10 \\  & KSS & 0.51 & 0.30 & 0.29 & 0.23 & 0.47 & 0.21 \\  & Bias & 1.22 & 0.62 & 1.23 & 0.48 & 1.21 & 0.28 \\  & ETS & 0.57 & 0.20 & 0.45 & 0.12 & 0.54 & 0.11 \\ \hline \multirow{6}{*}{Dawn/Twilight} & POD & 0.88 & 0.23 & 0.67 & 0.36 & 0.85 & 0.19 \\  & FAR & 0.36 & 0.23 & 0.37 & 0.32 & 0.36 & 0.20 \\ \cline{1-1}  & KSS & 0.52 & 0.26 & 0.30 & 0.51 & 0.50 & 0.25 \\ \cline{1-1}  & Bias & 1.37 & 0.67 & 1.06 & 1.32 & 1.33 & 0.61 \\ \cline{1-1}  & ETS & 0.54 & 0.17 & 0.49 & 0.18 & 0.53 & 0.16 \\ \hline \multirow{6}{*}{Total} & POD & 0.82 & 0.28 & 0.72 & 0.22 & 0.80 & 0.15 \\ \cline{1-1}  & FAR & 0.37 & 0.20 & 0.40 & 0.10 & 0.37 & 0.13 \\ \cline{1-1}  & KSS & 0.46 & 0.30 & 0.31 & 0.18 & 0.43 & 0.18 \\ \cline{1-1}  & Bias & 1.30 & 0.69 & 1.20 & 0.51 & 1.28 & 0.38 \\ \cline{1-1}  & ETS & 0.54 & 0.20 & 0.46 & 0.10 & 0.52 & 0.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of the validation results for the training cases according to time and geographic location. skill and stability of GK2A_FDA were evidently dependent on the geographic location (better at land than at the coast) and time (better during the night than day), similar to the training cases. Figure 11 shows the validation results based on the time for the 12 training and 11 validation cases used in this study.