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Development of an automated classification scheme for detection of polar stratospheric clouds over Antarctica using AVHRR imagery

Although polar stratospheric clouds (PSCs) are a critical component in the ozone depletion process, their timing, duration, geographic extent, and annual variability are not well understood. The goal of this study is the development of an automated classification scheme for detecting PSCs using NOAA AVHRR data. Visual interpretation, density slicing, and standard multispectral classification detect most optically thick PSCs, but only some thin PSCs. Two types of automated techniques for detecting thin PSCs are being investigated: namely, multispectral classification methods, including the use of texture and other imagederived features, and back-propagation neural networks, including the use of hyperspatial and hypertemporal data. UARS CLAES temperature and aerosol extinction coefficient data are being used as a verification dataset. If successful, this classification scheme will be used to process the entire record of AVHRR data in order to assemble a long-term PSC climatology.