## 5 Conclusions  
This study quantitatively evaluated the fog detection performance of the GK2A_FDA over a three-year period (2021-2023), using 176 ground-based visibility meters. A detailed assessment was conducted to identify the limitations and improvement potential of the GK2A_FDA, focusing on variations in performance by geographic region and time of day. To this end, multiple datasets were utilized, including GK2A fog and cloud detection products, CTH, visibility meter data, surface air temperature, RH, wind speed, and ceilometer-based cloud base height. Among the nine GK2A_FDAs, this study focused on six sub-algorithms (land and coastal regions during daytime, nighttime, and twilight) that can be evaluated using surface visibility data. Although oceanic fog detection performance was not addressed here, the recent expansion of Korea's ocean observation network is expected to enable such evaluations in future studies. Over the entire study period (156,369 scenes at 10 min intervals), the GK2A_FDA product using the nearest-pixel method yielded a POD of 0.59, FAR of 0.86, and bias of 4.25. When the 3 \(\times\) 3 neighborhood pixel method was applied to compensate for navigation errors and the limited spatial representativeness of visibility meters, the performance improved, with a POD of 0.70, FAR of 0.81, and bias of 3.73. Regardless of the validation method, the long-term results showed lower POD and significantly higher FAR and bias  
Figure 12: Ratio distribution of false alarm counts to hit counts according to relative humidity and SZA over (**a**) inland area and (**b**) coastal area, along with the frequency distribution of GK2A false detections by SZA bin (**left**). than those found in previous case-based evaluations. These degraded performance metrics are likely because the algorithm was initially optimized for selected fog events with broad spatial coverage [28]. However, it is now applied year-round under various meteorological and temporal conditions. Evaluations of the top 10% of foggy days revealed substantially better performance, with a POD of 0.72 (0.82), FAR of 0.65 (0.59), and bias of 2.08 (2.02) for the nearest pixel (3 \(\times\) 3 neighborhood pixel). Detection performance varied considerably by region and time of day. In inland areas, the highest and most stable performance was observed in autumn, when fog frequently occurred, while June and winter exhibited strong over-detection. Coastal areas also tended to produce high false detections, although the tendency weakened during autumn. By the time of day, daytime had the highest POD, followed by nighttime and twilight, with similar trends for FAR and bias. Although the coastal algorithm generally outperformed the inland algorithm in POD, it also showed a stronger tendency for over-detection--especially during the daytime, where FAR and bias were the highest. The algorithm primarily relies on brightness temperature differences between the infrared and mid-infrared channels at night. Although the POD is lower at night than during the day, FAR and bias are also reduced, resulting in more stable performance. Twilight, where a simplified algorithm is applied, showed the lowest accuracy across all metrics, likely due to the limitations in using visible and infrared channels under high SZA. Seasonally, POD was highest in winter, followed by autumn and spring, and lowest in summer. However, autumn demonstrated the most stable performance overall due to lower FAR and bias. Station-based analysis also showed that more than 25% of the top-performing stations were concentrated in autumn, while the lowest-performing stations were predominantly observed in summer. High-performance and low-performance cases were analyzed to explore the causes of detection variability. In the radiation fog event on 29 September 2022, the GK2A_FDA reliably detected fog across all regions and periods (e.g., at 06:00 LST: POD = 0.97, FAR = 0.36, Bias = 1.51).