Arctic Fog Detection Using Infrared Spectral Measurements

• Published in: Journal of atmospheric and oceanic technology 2019-08, Vol.36 (8), p.1643-1656
• Main Authors: Yi, Li, Li, King-Fai, Chen, Xianyao, Tung, Ka-Kit
• Format: Article
• Language: English
• Subjects: Algorithms; Arctic observations; Arctic sea ice; Aviation; Climate change; Clouds; Coastal zone; Detection; False alarms; Fog; Fog formation; Global warming; Ground-based observation; Ice; Ice environments; Ice melting; Light aircraft; Meteorological satellites; Monitoring; Probability theory; Satellite observation; Satellites; Sea ice; Statistical methods; Temperature differences; Temperature gradients; Training; Transport
• ISSN: 0739-0572; 1520-0426
• DOI: 10.1175/JTECH-D-18-0100.1

The rapid increase in open-water surface area in the Arctic, resulting from sea ice melting during the summer likely as a result of global warming, may lead to an increase in fog [defined as a cloud with a base height below 1000 ft (~304 m)], which may imperil ships and small aircraft transportation in the region. There is a need for monitoring fog formation over the Arctic. Given that ground-based observations of fog over Arctic open water are very sparse, satellite observations may become the most effective way for Arctic fog monitoring. We developed a fog detection algorithm using the temperature difference between the cloud top and the surface, called ∂ T in this work. A fog event is said to be detected if ∂ T is greater than a threshold, which is typically between −6 and −12 K, depending on the time of the day (day or night) and the surface types (open water or sea ice). We applied this method to the coastal regions of Chukchi Sea and Beaufort Sea near Barrow, Alaska (now known as Utqiaġvik), during the months of March–October. Training with satellite observations between 2007 and 2014 over this region, the ∂ T method can detect Arctic fog with an optimal probability of detection (POD) between 74% and 90% and false alarm rate (FAR) between 5% and 17%. These statistics are validated with data between 2015 and 2016 and are shown to be robust from one subperiod to another.