Abstract
Fog affects transportation due to low visibility and also aggravates air pollutants. Thus, accurate detection and forecasting of fog are important for the safety of transportation. In this study, we developed a decision tree type fog detection algorithm (hereinafter GK2A_FDA) using the GK2A/AMI and auxiliary data. Because of the responses of the various channels depending on the time of day and the underlying surface characteristics, several versions of the algorithm were created to account for these differences according to the solar zenith angle (day/dawn/night) and location (land/sea/coast). Numerical model data were used to distinguish the fog from low clouds. To test the detection skill of GK2A_FDA, we selected 23 fog cases that occurred in South Korea and used them to determine the threshold values (12 cases) and validate GK2A_FDA (11 cases). Fog detection results were validated using the visibility data from 280 stations in South Korea. For quantitative validation, statistical indices, such as the probability of detection (POD), false alarm ratio (FAR), bias ratio (Bias), and equitable threat score (ETS), were used. The total average POD, FAR, Bias, and ETS for training cases (validation cases) were 0.80 (0.82), 0.37 (0.29), 1.28 (1.16), and 0.52 (0.59), respectively. In general, validation results showed that GK2A_FDA effectively detected the fog irrespective of time and geographic location, in terms of accuracy and stability. However, its detection skill and stability were slightly dependent on geographic location and time. In general, the detection skill and stability of GK2A_FDA were found to be better on land than on coast at all times, and at night than day at any location.
Highlights
Fog is a meteorological phenomenon that occurs near the Earth’s surface and affects human activities in various ways [1,2,3]
Where N is the total number of fog cases; xi is the evaluation matrix, probability of detection (POD), false alarm ratio (FAR), bias ratio (Bias), and equitable threat score (ETS) of each fog case; and x is the average of xi
Pixels above 0 K were mainly found in sub-pixel-sized fog. ∆FTs showed a non-negligible difference between day and night (Figure 5b,d)
Summary
Fog is a meteorological phenomenon that occurs near the Earth’s surface and affects human activities in various ways [1,2,3]. 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]. A polar orbiting satellite consists of many channels and can detect fog with significantly high spatial resolution; detecting a rapidly changing fog by observing the same area only twice a day is difficult. Fog can be detected in real time using geostationary satellites, such as Communication, Ocean and Meteorological (COMS) and Multifunction Transport Satellite (MTSAT) These satellites have lower spatial resolutions compared to the polar orbit satellites. As the availability of satellite data differed between day and nighttime, the fog detection algorithm was developed separately, according to the SZA. Discussions on the performance of fog detection algorithms and conclusions are presented in Sections 4 and 5, respectively
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
