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

Abstract

A 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.

The Role of Negative Buoyancy in Surface-Based Convection and Its Representation in Cumulus Parameterization Schemes

“…However, the algorithm is known to produce reasonably good results for optically thin cloud conditions (Weisz et al 2007b). Thus, in order to filter out grid points with thick clouds, we obtain cloud thickness values using the University of Wisconsin's AIRS regression retrieval algorithm package (UWAIRS, version 3.0; Smith et al 2012). This algorithm uses a dual-regression technique that determines the presence of a cloud and associated cloud-thickness value based on the differences between clear-trained and cloudy-trained solutions at each grid point (Smith et al 2012).…”

“…Thus, in order to filter out grid points with thick clouds, we obtain cloud thickness values using the University of Wisconsin's AIRS regression retrieval algorithm package (UWAIRS, version 3.0; Smith et al 2012). This algorithm uses a dual-regression technique that determines the presence of a cloud and associated cloud-thickness value based on the differences between clear-trained and cloudy-trained solutions at each grid point (Smith et al 2012). The assigned cloud thickness values may range from 0 to 3, with 0 indicating clear or thin cloud conditions and 3 representing locations with thick clouds (Smith et al 2012).…”

“…This algorithm uses a dual-regression technique that determines the presence of a cloud and associated cloud-thickness value based on the differences between clear-trained and cloudy-trained solutions at each grid point (Smith et al 2012). The assigned cloud thickness values may range from 0 to 3, with 0 indicating clear or thin cloud conditions and 3 representing locations with thick clouds (Smith et al 2012). In this study, only grid points with 0 cloud thickness are considered.…”

An Approach for Improving Cirrus Cloud-Top Pressure/Height Estimation by Merging High-Spatial-Resolution Infrared-Window Imager Data with High-Spectral-Resolution Sounder Data

“…With AIRS as the HSRS, the CTP retrievals are derived at single-FOV resolution from the dual-regression (DR) retrieval method (Weisz et al 2011;Smith et al 2012). The DR method, which uses statistical datasets stratified by cloud heights, provides accurate profile, surface, and cloud-property retrievals from highspectral-radiance measurements under clear skies as well as below thin and/or scattered-to-broken cloud conditions.…”

“…Past studies demonstrated the use of the hyperspectral IR bands for cloud-property retrieval (Zhou et al 2007(Zhou et al , 2009Weisz et al 2007b). An AIRS research algorithm that is based on eigenvector regression and was developed at the Cooperative Institute for Meteorological Satellite Studies/ University of Wisconsin-Madison is used to derive AIRS-only sounding profiles (temperature, moisture, and ozone) and simultaneously cloud-top pressure (CTP) at single AIRS FOV resolution (Weisz et al 2011;Smith et al 2012). Synergistic use of imager and sounder measurements has been shown to improve cloud-top retrievals at sounder resolution (Li et al 2004(Li et al , 2005.…”