# Development of Fog Detection Algorithm Using GK2A/AMI and Ground Data
## 5 Conclusions  
Fog affects transportation due to low visibility and impacts aggravates air pollutants. Thus, accurate detection and forecasting of fog are important for the safety of transportation and air quality management. We developed a decision tree type fog detection algorithm (GK2A_FDA) using the second geostationary satellite of South Korea, GK2A/AMI, and auxiliary data, such as numerical model output and land cover data. The ultimate goal of this study was to reduce the damage caused by fog through real-time fog detection using GK2A/AMI as a high-resolution geostationary satellite. In fact, the fog detection algorithm developed in this study has been temporarily in operation since July 2019. The GK2A/AMI has 16 channels, with a 2 (0.5-1) km spatial resolution in infrared (visible) channels. It observes the full disk and East Asian region every 10 and 2 min, respectively. The spatial resolution and temporal frequency of the surface temperature predicted by the numerical model operated by the KMA were 10 km and 1 h, respectively. The results of GK2A_FDA were validated using the visibility data from 280 stations in South Korea. We selected 23 fog cases (3312 scenes) that occurred across South Korea and used 1728 scenes to determine the threshold values and 1584 scenes to validate GK2A_FDA. As the spatio-temporal resolution of the satellite data, model output, and visibility data were different, the spatio-temporal co-location process was applied to simultaneously use both data. Fog is a complicated phenomenon in terms of size and intensity. Additionally, AMI contains 16 channels and the quality of auxiliary data (e.g., numerical model output) has significantly improved; thus, we used its various combination of test elements for improving fog detection. We developed GK2A_FDA (nine types of fog detection algorithms) differently, according to the SZA (day, dawn, and night) and surface type (land, sea, and coast) considering the availability of satellite data during day and nighttime, and different background characteristics based on the geographic location. Thus, the test steps were 6 (6) and 5 (4) for day and nighttime on land (sea), respectively. The main test elements used for the day and night were the reflectance differences at 0.63 \(\upmu\)m and DCD between the fog and other objects, respectively. The temperature difference between the top of the fog and surface has been commonly used for separating fog from low-to-mid clouds. The threshold values were optimized through the sensitivity tests for every test element using the 12 training cases after the initial setting of threshold values in the histogram analysis for the selected fog pixels. The fog detection results had a spatio-temporal discontinuity because of the different development of GK2A_FDA according to SZA and geographic location. Therefore, we included the fog detected at nighttime to minimize the discontinuity at dawn and calculated the weight sum of the land and sea algorithms in the coastal area. In addition, we eliminated the falsely detected fog pixels appearing in the snow or desert area using the snow cover map and land cover data. The validation results of the 12 training and 11 validation cases showed that GK2A_FDA effectively detected the fog, irrespective of time and geographic location in terms of accuracy and  
Figure 14: Sample image of **(a)** the fog detection result for the sea fog at 02:00 KST on 03 August 2019. Navy blue line represents the CALIPSO track and green points on the track indicate the fog pixels redefined from the VFM data. **(b)** VFM data of the track depicted **(a)** over the sea. stability. The total average POD, FAR, Bias, and ETS for the training (validation) cases were 0.80 (0.82), 0.37 (0.29), 1.28 (1.16), and 0.52 (0.59), respectively. The SDs of the total averages of POD, FAR, Bias, and ETS for the training (validation) cases were 0.15 (0.19), 0.13 (0.28), 0.38 (1.00), and 0.11 (0.21), respectively.