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Keith D. Hutchison, Kenneth R. Hardy, and Bo-Cai Gao

Abstract

The accurate identification of optically thin cirrus clouds in global meteorological satellite imagery by automated cloud analysis algorithms is critical to environmental remote sensing studies, such as those related to climate change. While significant improvements have been realized with the arrival of multispectral, meteorological satellite imagery, collected by NOAA's Advanced Very High Resolution Radiometer (AVHRR), difficulties can be encountered when attempting to make pixel-level cloud decisions over large and diverse geographic areas found around the globe. These problems are due, in part, to the effects of atmospheric attenuation on cloud spectral signatures, caused primarily by variations in water vapor, since the signatures of water vapor and optically thin cirrus are similar in the nighttime AVHRR infrared channels. In this paper, the authors describe an improved thin-cirrus detection technique that uses the brightness temperature differences between AVHRR channel 3 and channel 5 along with total integrated water vapor information. It is concluded that algorithms must accurately compensate for the impact of water vapor on cloud spectral signatures for enhanced detection of optically thin cirrus clouds in nighttime AVHRR imagery.

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