Dual-Regression Retrieval Algorithm for Real-Time Processing of Satellite Ultraspectral Radiances

Abstract

fast physically based dual-regression (DR) method is developed to produce, in real time, accurate profile and surface- and cloud-property retrievals from satellite ultraspectral radiances observed for both clear- and cloudy-sky conditions. The DR relies on using empirical orthogonal function (EOF) regression ?clear trained? and ?cloud trained? retrievals of surface skin temperature, surface-emissivity EOF coefficients, carbon dioxide concentration, cloud-top altitude, effective cloud optical depth, and atmospheric temperature, moisture, and ozone profiles above the cloud and below thin or broken cloud. The cloud-trained retrieval is obtained using cloud-height-classified statistical datasets. The result is a retrieval with an accuracy that is much higher than that associated with the retrieval produced by the unclassified regression method currently used in the International Moderate Resolution Imaging Spectroradiometer/Atmospheric Infrared Sounder (MODIS/AIRS) Processing Package (IMAPP) retrieval system. The improvement results from the fact that the nonlinear dependence of spectral radiance on the atmospheric variables, which is due to cloud altitude and associated atmospheric moisture concentration variations, is minimized as a result of the cloud-height-classification process. The detailed method and results from example applications of the DR retrieval algorithm are presented. The new DR method will be used to retrieve atmospheric profiles from Aqua AIRS, MetOp Infrared Atmospheric Sounding Interferometer, and the forthcoming Joint Polar Satellite System ultraspectral radiance data.