Initial Cloud Detection Using the EOF Components of High-Spectral-Resolution Infrared Sounder Data

Abstract

Measured cloud spectral signatures in high-resolution infrared interferometer data have been separated from the clear-air signatures using singular value decomposition. Sets of empirical orthogonal functions (EOFs) have then been created from these signatures to investigate the possibility of cloudy view discrimination without the use of any background data. The measured data have been taken by the Airborne Research Interferometer Evaluation System (ARIES), which is specifically designed to gather data from an aircraft that are representative of the forthcoming Infrared Atmospheric Sounding Interferometer (IASI). EOF sets were based on 78 diverse modeled clear-air spectra, supplemented by selected measured spectra. Video data gave independent verification of cloudy and cloud-free views. The development of a cloud-detection scheme is detailed, and several possible cloud-detection procedures were tested. The most promising procedure is presented. Comparative tests are made with cloud-detection algorithms developed for earlier satellite instruments. The results are encouraging; clouds were detected in the measured test data with similar success to other schemes but without requiring prior information or even the uncompressing of transmitted data. With the prospect of IASI (and similar) data being compressed for transmission using EOFs, the procedure here could be implemented in NWP centers as an initial very inexpensive but accurate method to create a cloud-filtering mask.