Retrieval Atmospheric Water Vapor and Other Trace Gas Profiles from Pseudo-Monochromatic Radiance Spectra

Hyper-spectral instruments provide advanced technology to indirectly measure water vapor, carbon dioxide, methane and ozone profiles simultaneously. This study provides a new approach to process observed hyper-spectral radiance data for improving the measurement accuracy of water vapor and other trace gas profiles. It uses an empirical transformation matrix to convert an instrument radiance spectrum into a theoretical pseudo-monochromatic radiance spectrum. Eigenvector regression is used to produce the empirical transformation. Although the transformation does not produce the monochromatic spectra without error, it is shown that this transformation error in the radiance spectrum is generally well below the instrument noise level for most of the spectral channels. The reduction in instrument noise results from the noise filtering effect of the eigenvector transformation. The accuracy of water vapor and other trace gas profiles retrieved from the pseudo-monochromatic radiance benefits from the noise reduced spectrum. Another major advantage of this approach is that it eliminates the need to build different fast radiative transfer models for different observing instruments. A different transformation matrix is required for different instrument spectral channel characteristics but not for the radiative transfer model and algorithm.