## 4 Discussion  
The GK2A_FDA, currently operational at the National Meteorological Satellite Center of Korea, is subdivided into nine sub-algorithms based on spatial (land, coast, and ocean) and temporal (daytime, twilight, and nighttime) domains. This study evaluated six sub-algorithms, excluding the ocean category, using ground-based visibility data for inland and coastal regions across three time zones (day, night, and twilight). Although the GK2A_FDA demonstrates operational stability in fog detection, it generally exhibits over-detection and shows spatial and temporal discontinuities in its performance. In particular, false detections often occur during the fog dissipation phase after sunrise, where only low-level clouds remain near the surface, yet the satellite continues to detect fog. This type of false detection is challenging to address using satellite data alone. [PERSON] and [PERSON] [46], in their development of a cloud-type classification algorithm using satellite data, incorporated reanalysis fields such as RH, wind speed, and precipitation to determine fog probability. They calculated fog likelihood based on RH thresholds and categorized the probability into three levels. Although the algorithm exhibited high POD and FAR, its performance decreased during twilight due to limitations inherent to satellite-based detection. Nevertheless, their study demonstrated the potential for distinguishing fog-prone areas more effectively by integrating surface observations. Similarly, the Himawari fog detection algorithm uses model-derived RH, flagging areas with values above 85% as fog [47]. The GOES-based fog/low stratus detection algorithm also uses vertical RH fields from numerical models to detect fog and low stratus probabilistically [48]. As emphasized in previous studies, high RH is essential for cloud and fog formation. [PERSON] [49] found that, although the correlation varies by region and altitude, RH is the strongest predictor of cloud cover among the variables evaluated. [PERSON] et al. [50] noted that while relatively high RH does not guarantee cloud formation, clouds will not form without local supersaturation. Increased wind speed enhances turbulent mixing near the surface, promoting the evaporation of fog droplets and contributing to radiation fog's  
Figure 10: Monthly fog occurrence frequency detected by the visibility meter and GK2A_FDA (10 min interval) (2021–2023): (**a**) inland area, (**b**) coastal area. dissipation [4]. Therefore, in cases where stratiform clouds or their edges--optically and structurally similar to fog--are misclassified as fog, incorporating ground-based RH and wind speed data is likely to reduce false detections. \(\Delta\)FTs (the difference between the fog-top and modeled surface temperature, \(\text{CSR\_BT11.2\,\upmu m}\text{--BT11.2\,\upmu m}\)) is a key element of the GK2A_FDA used to distinguish fog from clouds [31]. Since fog formation requires supersaturation, this study analyzed \(\Delta\)FTs and RH conditions under fog and non-fog situations. Figure 11 presents the density ratio of hits and false alarms, as described in [51], for each bin of relative humidity (in 1% intervals) and \(\Delta\)FTs (in 0.5 \({}^{\circ}\)C intervals), based on three years of data. \[\text{Ratio}=\text{False Alarm Count/Hit count} \tag{4}\]  
The pink regions represent areas where only false detections occurred, widely distributed over low-RH conditions. These false-only zones are most pronounced in spring and winter. False detections occurring regardless of RH in the \(\Delta\)FTs range above 5 \({}^{\circ}\)C are attributable to the relaxed thresholds (up to 10 \({}^{\circ}\)C) used in the coastal algorithm, originally designed to detect dense sea fog. The \(\Delta\)FTs density plot (right side of Figure 11) indicates that most false detections are concentrated between 0 and 3.5 \({}^{\circ}\)C, clearly demonstrating the challenge of distinguishing fog from low stratus clouds.