Arctic Fog Detection Using Infrared Spectral Measurements

Author:

Yi Li1,Li King-Fai2,Chen Xianyao3,Tung Ka-Kit4

Affiliation:

1. Physical Oceanography Laboratory, Collaborative Innovation Center of Marine Science and Technology, and Ocean–Atmosphere Interaction and Climate Laboratory, Ocean University of China, Qingdao, Shandong, China

2. Department of Environmental Sciences, University of California, Riverside, Riverside, California, and Department of Applied Mathematics, University of Washington, Seattle, Washington

3. Physical Oceanography Laboratory, Collaborative Innovation Center of Marine Science and Technology, Ocean University of China, and Qingdao National Laboratory of Marine Science and Technology, Qingdao, China

4. Department of Applied Mathematics, University of Washington, Seattle, Washington

Abstract

AbstractThe 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.

Funder

Belmont Forum and US National Science Foundation

National Aeronautics and Space Administration

Belmont Forum and Natural Science Foundation of China

National Key Basic Research Program of China

National Natural Science Foundation of China

Shandong Provincial Natural Science Foundation of China

Fundamental Research Funds for the Central Universities of China

National Key Research and Development Program of China

Publisher

American Meteorological Society

Subject

Atmospheric Science,Ocean Engineering

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