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The vertical temperature profile of the atmosphere has an influence on thewidth and intensity of gaseous absorption lines. In the visible and nearinfrared part of the spectrum, this poses a problem for the fast forwardsimulation of the radiative transfer, needed in algorithms for the retrievalof any atmospheric or surface-related parameter from satellite measurements.We show that the main part of the global variability of temperature profilescan be described by their first 2 to 6 eigenvectors, depending on theaccuracy requirement, by performing a Principal Component Analysis (PCA) on aglobal set of temperature profiles from the Global Forecast System (GFS).Furthermore, we demonstrate the possibility to approximate the atmospherictransmittance in the O<sub>2</sub> A band for any temperature profile with almostperfect accuracy by a linear combination of the transmittances attributed toeach of the significant temperature eigenvectors. For the retrieval ofsurface pressure from O<sub>2</sub> A band measurements, this reduces the global rootmean square error from >30 hPa to better than 1 hPa by strongly reducingthe regional bias of surface pressure, retrieved on the assumption of anaverage temperature profile. The technique can be applied under scatteringconditions to eliminate temperature-induced errors in, e.g., simulatedradiances. In principal, the method can be useful for any problem includinggaseous absorption or emission with a significant influence of thetemperature profile, such as the retrieval of total water vapour contentor sea surface temperature

On the efficient treatment of temperature profiles for the estimation of atmospheric transmittance under scattering conditions