Use of multivariate regression for analysis of CO{sub 2} laser lidar data from long pathlengths in ambient atmosphere

Until recently use of lasers for long path absorption measurements has relied on using differential absorption at two wavelengths to look for one species at a time in the atmosphere. With the advent of multi-line CO{sub 2} lasers it is now feasible to generate 30 to 40 lines in a rapid burst to look for spectra of all the chemical species that may be present. Measurements have been made under relatively constant meteorological conditions in a summertime desert environment with a multi-line tunable laser. Multivariate regression analysis of this data shows that the spectra can be accurately fit using a small number of spectral factors or eigenvectors of the time dependent spectral data matrix. The factors can be rationalized in terms of lidar system effects and atmospheric composition changes.