A daytime cloud detection algorithm for FY-3A/VIRR data

Abstract

Cloud detection is essential for the retrieval of atmospheric and surface parameters and it directly impacts the quality of many satellite geophysical products used in weather, climate and environmental research. In this article, a daytime cloud detection algorithm based on multi-spectral thresholds is proposed to discriminate clouds from clear skies for the visible and infrared radiometer (VIRR), which is a key instrument on board the Chinese FengYun-3A (FY-3A) polar-orbiting meteorological satellite, launched 27 May 2008. The VIRR has ten bands in the wavelengths 0.43–12.5 μm and provides global observations of atmosphere, ocean and land in the visible and infrared regions of the spectrum. In this algorithm, the underlying surface is divided into five ecological types: snow/ice, desert, coastal, land and water, and seven spectral bands of the VIRR are used to indicate a level of confidence that the VIRR is observing clear skies. This algorithm also utilizes the 1.6 μm band and the difference between the 1.38 and 1.6 μm bands to respectively detect water cloud and high cloud. An example of cloud detection and a comparison with an official cloud masking product are given; the results show that this algorithm performs well and is better than the official algorithm in cloud detection.

Acknowledgements

This research was supported by CMA grant FiDAF-2-03 and Guangdong Meteorological Bureau grant 2008B12. The author would like to thank colleagues at Guangzhou Meteorological Satellite Ground Station for their assistance in this research. The author is very grateful to the referee for the helpful suggestions on the manuscript.