# 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
In general, the fog detection level was found to be significantly dependent on the characteristics (e.g., fog area) of the fog cases, irrespective of the detection time. In terms of POD and FAR, higher POD and lower FAR was observed for the cases with a large number of fog points and vice versa. The best and worst results were found in V3-V6 and T4, and V9-V10 cases, respectively, which had the highest and lowest numbers of fog points, respectively. The larger SD in the validation compared to the training cases was due to the larger variation in the number of fog points in the validation cases. For July 26 (T4), POD was equal to FAR at all times. A detailed analysis of the satellite images to find the causes of the low level of fog detection (not shown) revealed many fog pixels being covered with low-mid clouds, with no removal of the cloud pixels during validation because of the forecast error of CSR_BT11. This suggested that the quality of CSR_BT11 is very important in separating low to mid clouds from the fog. For 10 February 2020 (V9), FAR was higher than POD, regardless of the time. In this case, detecting the fog was significantly difficult because of the extremely local occurrence of the fog, with a semi-transparent cirrus above it. In the cases of T9 and V2, detection levels were found to be good at night and dawn, but POD was found to be a little higher than or similar to FAR during the day (Figure 11a). Similar to the V9 case, these was extremely locally occurring fog. Although GK2A_FDA was developed to remove partly and thin cirrus clouds, it still falsely detected partly clouds as fog on 17 September 2019 (T9). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Land**} & \multicolumn{2}{c}{**Coast**} & \multicolumn{2}{c}{**Total**} \\ \hline
**Time** & & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** \\ \hline \multirow{4}{*}{Day} & POD & 0.78 & 0.11 & 0.82 & 0.16 & 0.78 & 0.12 \\  & FAR & 0.31 & 0.25 & 0.12 & 0.10 & 0.30 & 0.24 \\  & KSS & 0.48 & 0.26 & 0.70 & 0.22 & 0.49 & 0.27 \\  & Bias & 1.13 & 0.62 & 0.93 & 0.17 & 1.12 & 0.58 \\  & ETS & 0.56 & 0.18 & 0.73 & 0.15 & 0.57 & 0.26 \\ \hline \multirow{4}{*}{Night} & POD & 0.84 & 0.25 & 0.69 & 0.18 & 0.83 & 0.20 \\  & FAR & 0.26 & 0.28 & 0.49 & 0.31 & 0.28 & 0.27 \\  & KSS & 0.58 & 0.44 & 0.20 & 0.39 & 0.55 & 0.38 \\  & Bias & 1.14 & 0.95 & 1.34 & 1.29 & 1.16 & 0.99 \\  & ETS & 0.62 & 0.23 & 0.41 & 0.25 & 0.60 & 0.22 \\ \hline \multirow{4}{*}{Dawn/Twilight} & POD & 0.86 & 0.32 & 0.78 & 0.23 & 0.86 & 0.20 \\  & FAR & 0.31 & 0.31 & 0.48 & 0.35 & 0.30 & 0.21 \\  & KSS & 0.54 & 0.52 & 0.29 & 0.49 & 0.55 & 0.25 \\  & Bias & 1.25 & 0.79 & 1.49 & 1.26 & 1.23 & 0.63 \\  & ETS & 0.54 & 0.18 & 0.69 & 0.27 & 0.56 & 0.18 \\ \hline \multirow{4}{*}{Total} & POD & 0.83 & 0.24 & 0.72 & 0.18 & 0.82 & 0.19 \\  & FAR & 0.28 & 0.29 & 0.43 & 0.32 & 0.29 & 0.28 \\ \cline{1-1}  & KSS & 0.56 & 0.42 & 0.29 & 0.40 & 0.54 & 0.37 \\ \cline{1-1}  & Bias & 1.15 & 0.96 & 1.25 & 1.29 & 1.16 & 1.00 \\ \cline{1-1}  & ETS & 0.60 & 0.22 & 0.46 & 0.26 & 0.59 & 0.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Same as Table 5 except for validation cases. Figure 12 shows the relation between the number of fog stations and detection skill of GK2A_FDA according to the time of the day. The detection level of GK2A_FDA was closely linked with the number of fog stations, irrespective of the time of the day. In general, POD and FAR were positively and negatively related to the number of fog stations, respectively. In particular, POD was significantly affected by the number of fog stations during dawn (Corr. : 0.65) and nighttime (Corr. : 0.74). However, the positive relation of POD with the detection skill and number of fogs was not clear for daytime, contrary to the relation observed for the training (Corr. : 0.47) and validation (Corr. : \(-\)0.13) cases. Moreover, the negative relation (Corr.