Asia-Pacific Journal of Atmospheric Sciences

Korean Meteorological Society (한국기상학회)
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A New Application of Unsupervised Learning to Nighttime Sea Fog Detection

  • Shin, Daegeun (Department of Atmospheric Sciences, Division of Earth Environmental System, Pusan National University) ;
  • Kim, Jae-Hwan (Department of Atmospheric Sciences, Division of Earth Environmental System, Pusan National University)
  • Received : 2016.08.24
  • Accepted : 2017.12.18
  • Published : 2018.11.30
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Abstract

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 $3.7{\mu}m$ and $10.8{\mu}m$ 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.

Keywords

Acknowledgement

Grant : Development of Atmosphere/aviation Algorithms, Development of Geostationary Meteorological Satellite Ground Segment

Supported by : ETRI (Electronics and Telecommunications Research Institute), NMSC (National Meteorological Satellite Center)

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