# Development of Fog Detection Algorithm Using GK2A/AMI and Ground Data
## 1 Introduction  
Fog is a meteorological phenomenon that occurs near the Earth's surface and affects human activities in various ways [1, 2, 3]. Fog affects transportation due to low visibility [1, 2, 4, 5, 6]. Moreover, it often damages crops and aggravates air pollutants due to solar energy reduction and temperature inversion [7, 8]. Therefore, its accurate detection is important for not only reducing traffic problems but also studying climate and air quality [1]. Many studies have analyzed the characteristics of fog and detected it using ground observation, numerical model, and satellite data [3, 5, 9, 10, 11, 12, 13]. As the spatio-temporal variation of fog is strong, fog detection, especially with satellite data, is significantly advantageous [1, 14, 15]. Geostationary satellites have wide spatial coverage, unlike polar-orbit satellites and ground observation data [14, 16, 17, 18], with high temporal resolution being their biggest advantage because of the possibility of continuous monitoring of fog and utilization of their data for short-term forecasts [1]. The quality of satellite data, in terms of accuracy, number of channels, spatial resolution, and temporal frequency, has significantly improved, which has led to the development of many fog detection techniques using the satellite data. As satellites have different channels available for day and night, fog detection algorithms have been separately developed in most studies for day or night according to the solar zenith angle (SZA) [2]. Solar radiation is present during the day; thus, reflectance is the main parameter used to detect fog during daytime [3; 5; 17]. Conversely, solar radiation is not present at night; therefore, the dual channel difference (DCD: brightness temperature (BT) of 3.8 \(\upmu\)m - BT11.2) method, presented by [PERSON] et al. [19], is mainly used for nighttime. This method involves measuring the difference in the brightness temperatures of two channels based on the difference between the emissivities of short-wave infrared channel (3.8 \(\upmu\)m) and infrared channel (IR) of 11.2 \(\upmu\)m in the water drop [16; 20]. However, distinguishing fog from other objects only using two test elements for day and night has certain limitations. Particularly, low clouds are difficult to distinguish because of their physical properties being similar to those of fog [21]. To resolve this difficulty, some researchers have used the roughness of the upper surface of clouds and difference between surface and cloud top temperatures [2; 3; 13; 16; 20; 22; 23]. In addition, to eliminate cirrus clouds in the fog product, the brightness temperature difference (BTD) between 10 and 12 \(\upmu\)m has been utilized [13; 17]. As the reflectance of snow-covered areas is similar to that of fog, the normalized difference snow index (NDSI) has been used to determine whether the pixel is snow or fog [24; 25]. Despite these efforts, fog detection using satellite data remains a major issue because of the limitations of the satellites (such as those of polar orbiting satellites), and the complexity of fog. A polar orbiting satellite consists of many channels and can detect fog with significantly high spatial resolution; however, detecting a rapidly changing fog by observing the same area only twice a day is difficult. Conversely, fog can also be detected in real time using geostationary satellites, such as Communication, Ocean and Meteorological (COMS) and Multifunction Transport Satellite (MTSAT). However, these satellites have lower spatial resolutions compared to the polar orbit satellites. This makes the detection of locally occurring fog with geostationary satellites difficult. In addition, they only have five channels, with many researchers recommending the need for more channels to separate the fog from clear ground, cirrus or middle-high clouds, and snow [16; 17]. Recently, GEO-KOMPSAT-2A (GK2A) and Himawari-8, launched by South Korea and Japan, respectively, significantly addressed these limitations [2; 3; 20].