Open AccessError reduction in retrievals of atmospheric species from symmetrically measured lidar sounding absorption spectra
Author(s) -
Jeffrey R. Chen,
Kenji Numata,
Shuying Wu
Publication year - 2014
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.22.026055
Subject(s) - lidar , optics , depth sounding , spectral line , atmospheric optics , wavelength , laser , noise (video) , atmospheric sounding , environmental science , remote sensing , absorption (acoustics) , line (geometry) , observational error , distortion (music) , optical depth , physics , materials science , geology , meteorology , mathematics , amplifier , oceanography , geometry , statistics , cmos , optoelectronics , aerosol , astronomy , artificial intelligence , computer science , image (mathematics)
We report new methods for retrieving atmospheric constituents from symmetrically-measured lidar-sounding absorption spectra. The forward model accounts for laser line-center frequency noise and broadened line-shape, and is essentially linearized by linking estimated optical-depths to the mixing ratios. Errors from the spectral distortion and laser frequency drift are substantially reduced by averaging optical-depths at each pair of symmetric wavelength channels. Retrieval errors from measurement noise and model bias are analyzed parametrically and numerically for multiple atmospheric layers, to provide deeper insight. Errors from surface height and reflectance variations are reduced to tolerable levels by "averaging before log" with pulse-by-pulse ranging knowledge incorporated.