Abstract. The vertical temperature profile of the atmosphere has an influence on the width and intensity of gaseous absorption lines. In the visible and near infrared part of the spectrum, this poses a problem for the fast forward simulation of the radiative transfer, needed in algorithms for the retrieval of any atmospheric or surface-related parameter from satellite measurements. We show that the main part of the global variability of temperature profiles can be described by their first 2 to 6 eigenvectors, depending on the accuracy requirement, by performing a Principal Component Analysis (PCA) on a global set of temperature profiles from the Global Forecast System (GFS). Furthermore, we demonstrate the possibility to approximate the atmospheric transmittance in the O2 A band for any temperature profile with almost perfect accuracy by a linear combination of the transmittances attributed to each of the significant temperature eigenvectors. For the retrieval of surface pressure from O2 A band measurements, this reduces the global root mean square error from >30 hPa to better than 1 hPa by strongly reducing the regional bias of surface pressure, retrieved on the assumption of an average temperature profile. The technique can be applied under scattering conditions to eliminate temperature-induced errors in, e.g., simulated radiances. In principal, the method can be useful for any problem including gaseous absorption or emission with a significant influence of the temperature profile, such as the retrieval of total water vapour content or sea surface temperature.
Treatment of overlapping gaseous absorption with the correlated-kmethod in hot Jupiter and brown dwarf atmosphere models