Improvement in Humidity Profile Retrieval for Hyperspectral Sounder Using Principal Component-Based Regression Algorithm

This study is an application of the hybrid regression method to improve the accuracy of retrieved humidity profile from infrared hyperspectral sounding observations. In hybrid regression method, a weighted average of two regression products that are derived using two different forms of predictand is computed. Regression coefficients for each form of predictand are computed using principal components of MetOp-IASI radiance spectra. First regression product uses logarithm of specific humidity as predictand, whereas second regression product uses only specific humidity as predictand. The weights used in hybrid regression are computed at different pressure levels based on error statistics of humidity retrieval from different predictands. The hybrid regression-based method shows improvement over the state-of-the-art regression method. Humidity profiles retrieved from different regression methods are validated with collocated ECMWF humidity profiles and radiosonde observations for dry, wet, and combined atmospheric conditions. For all cases, humidity retrieved from hybrid regression method is found to be the most accurate at all pressure levels.