Spatial and Temporal Varying Thresholds for Cloud Detection in GOES Imagery
Jedlovec, G.J.
Haines, S.L.
LaFontaine, F.J.
Marshall Space Flight Center, Earth Sci. Office, NASA, Huntsville, AL;
This paper appears in: Geoscience and Remote Sensing, IEEE Transactions on
Publication Date: June 2008
Volume: 46,
Issue: 6
On page(s): 1705-1717
Location: Edinburgh, UK,
ISSN: 0196-2892
INSPEC Accession Number: 10002960
Digital Object Identifier: 10.1109/TGRS.2008.916208
First Published: 2008-05-02
Current Version Published: 2008-05-16
Abstract
A new cloud detection technique has been developed and applied to GOES-12 Imager data. The bispectral composite threshold (BCT) technique uses only the 11- and 3.9- channels, and composite imagery generated from these channels, in a four-step cloud detection procedure to produce a binary cloud mask at single-pixel resolution. An innovative aspect of this algorithm is the use of 20-day composites of the 11- and the 11-3.9- channel difference imagery to represent spatially and temporally varying clear-sky thresholds for the bispectral cloud tests. The BCT cloud detection technique has been validated against a ldquotruthrdquo data set generated by the manual determination of the sky conditions from available satellite imagery for four seasons during 2003-2004. The day-and-night algorithm has been shown to determine the correct sky conditions 87.6% of the time (on average) over the eastern two-thirds of the U.S. and surroundings oceans. The incorrectly determined conditions arose from missing clouds 8.9% of the time or from overdetermining clouds 3.5% of the time. Nearly 82% of the misses came in the presence of low clouds. Only small variations in algorithm performance occurred between day-night, land-ocean, and between seasons. The algorithm performed best in the warmer seasons (90.9% correct during the summer versus 81.8% correct in the winter season) and during the day, when the solar illumination provides enhanced surface atmospheric cloud contrast in the infrared channels, and least well during the winter season. The algorithm was found to slightly underdetermine clouds at night and during times of low sun angle and tends to be cloud conservative during the day, particularly in the summertime.
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