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OBSERVATIONS OF VISIBILITY AND CHEMICAL COMPOSITIONS RELATED TO FOG, MIST AND HAZE IN SOUTH KOREA
YOON,M.B,CHUNG,Y.S,KIM,H.S 한국교원대학교 환경과학연구소 2000 환경연구논문집 Vol.- No.5
Since 1970, the yearly consumption of fossil fuel in south Korea has steadily increased to 165 MT from 14 MT. It has been found that the number of days of low visibility ( 10 ㎞) was significantly increased by the occurrence of fog, mist and haze. For example, the low visibility days in Seoul during 1989 were over 207 days in comparison with 21 days at a rural site. This similar trend has been observed in other large cities. In Chongwon of central Korea, daily measurements of visibility at 09 LST have been made since 1991. It is observed that the increase in the frequency of low visibility days was related to the increase in anthropogenic air pollution and water vapor in the study area. The occurrence of fog-mist-haze was much related to the influence of local, regional and synoptic meteorology. With the increase in both water supply and emission of air pollution, in Korea we commonly observe the typical historical type of London mist and haze. In this study, chemical analyses of fog, mist, haze and frost was carried out. According to analyses of data obtained in 1995, the pH values for 65.9% of all fog and mist samples collected were less than 5.6. The lowest pH value of fog 4.0. On the other hand, pH values observed for dew and frost in early spring were generally neutral to alkaline in nature, although there was an abundant existence of sulphates and nitrates. This suggests that characteristics of yellow sand and soil dust occurring in spring appear to determine the pH values in hydrometeors occurring on the Korean peninsula. A satellite observation of sea fog is also discussed. It has been observed that the advection fog occurred with air pollutants over the Yellow Sea which were moving out of China.
A New Application of Unsupervised Learning to Nighttime Sea Fog Detection
신대근,김재환 한국기상학회 2018 Asia-Pacific Journal of Atmospheric Sciences Vol.54 No.4
This paper presents a nighttime sea fog detection algorithm incorporating unsupervised learning technique. The algorithm is based on data sets that combine brightness temperatures from the 3.7 μm and 10.8 μm channels of the meteorological imager (MI) onboard the Communication, Ocean and Meteorological Satellite (COMS), with sea surface temperature from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA). Previous algorithms generally employed threshold values including the brightness temperature difference between the near infrared and infrared. The threshold values were previously determined from climatological analysis or model simulation. Although this method using predetermined thresholds is very simple and effective in detecting low cloud, it has difficulty in distinguishing fog from stratus because they share similar characteristics of particle size and altitude. In order to improve this, the unsupervised learning approach, which allows a more effective interpretation from the insufficient information, has been utilized. The unsupervised learning method employed in this paper is the expectation–maximization (EM) algorithm that is widely used in incomplete data problems. It identifies distinguishing features of the data by organizing and optimizing the data. This allows for the application of optimal threshold values for fog detection by considering the characteristics of a specific domain. The algorithm has been evaluated using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) vertical profile products, which showed promising results within a local domain with probability of detection (POD) of 0.753 and critical success index (CSI) of 0.477, respectively.