###### Abstract  
This study evaluated the performance of the operational GK2A (GEO-KOMPSAT-2A) fog detection algorithm (GK2A_FDA) using ground-based visibility meter data from 176 stations across South Korea from 2021 to 2023. According to the verification method using the nearest pixel and \(3\times 3\) neighborhood pixel approaches to the visibility meter, the 3-year average probability of detection (POD) is 0.59 and 0.70, the false alarm ratio (FAR) is 0.86 and 0.81, and the bias is 4.25 and 3.73, respectively. POD is highest during daytime (0.72; bias: 7.34), decreases at night (0.57; bias: 3.89), and is lowest at twilight (0.52; bias: 2.36). The seasonal mean POD is 0.65 in winter, 0.61 in spring and autumn, and 0.47 in summer, with August reaching the minimum value, 0.33. While POD is higher in coastal areas than inland areas, inland regions show lower FAR, indicating more stable performance. Over-detections occurred regardless of geographic location and time, mainly due to the misclassification of low-level clouds and cloud edges as fog. Especially after sunrise, the fog dissipated and transformed into low-level clouds. These findings suggest that there are limitations to improving fog detection levels using satellite data alone, especially when the surface is obscured by clouds, indicating the need to utilize other data sources, such as objective ground-based analysis data. fog; GK2A/AMI; visibility meter; detection level; regional and seasonal differences 2021-2023, Part I 2021-2023  
data [8; 9; 10]. Given the growing need for robust, high-resolution fog detection and forecasting to support transportation safety, recent approaches have increasingly integrated ground-based observations, buoys, and satellite products to analyze and classify fog occurrence [11; 12; 13; 14]. Satellite-based observations provide broad temporal and spatial coverage, enabling continuous monitoring of fog from its formation to dissipation and analysis of its spatial distribution. Before the GK2A(GEO-KOMPSAT-2A) launch, various geostationary and polar-orbiting satellites were used to study fog detection over the Korean Peninsula. [PERSON] et al. [15] developed an algorithm for detecting oceanic fog and low stratus by comparing hourly infrared radiance data with a clear-sky radiance composite map. Subsequent research utilized GOES (Geostationary Operational Environmental Satellite)-9, COMS (Communication, Ocean, and Meteorological Satellite), Himawari-8, MODIS (Moderate Resolution Imaging Spectroradiometer), and AMSR (Advanced Microwave Scanning Radiometer) data to investigate fog and sea fog characteristics during both day and night [16; 17; 18; 19; 20; 21; 22]. The GK2A fog detection algorithm (GK2A_FDA) constitutes an incremental improvement, incorporating both optical and textural characteristics of fog through additional multiple channels (1.6, 8.7, 10.5, 12.3, and 13.3 \(\upmu\)m) from the Advanced Meteorological Imager (AMI). Nevertheless, satellite-based fog detection remains challenging under a range of observational and atmospheric limitations, including multiple cloud layers obscuring the satellite's view, subpixel-scale fog, optically thin fog, orographic fog, and low-illumination conditions during the day-night transition period [16; 23; 24; 25]. In addition, low stratus clouds with optical and textural properties similar to fog often lead to false alarms [26; 27]. During daytime, cloud shadows and navigation errors can cause an underestimation of the reflectance background field, resulting in the misclassification of low stratus clouds as fog [19; 28]. Recent studies have applied data fusion and machine learning techniques to overcome these limitations and enhance satellite-based fog detection. [PERSON] et al. [25] utilized cloud base height data from Meteorological Aviation Routine Weather Reports and Meteosat Second Generation (MSG) satellite imagery to improve fog and low stratus (FLS) detection through a machine learning model. [PERSON] et al.