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

Abstract

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 O 2 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 O 2 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 temperatureinduced 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.

Fast reconstruction of hyperspectral radiative transfer simulations by using small spectral subsets: application to the oxygen A band

“…The temperature profile was explicitly excluded from the state vector to keep it simple but realistic. As shown by Lindstrot and Preusker (2012), the spectral variability due to the temperature profile of the atmosphere can be accounted for by performing radiative transfer simulations only for a set of principal temperature components. The radiance spectrum can then be constructed as linear superposition of the simulated spectra for the principal temperature profile components, by using the expansion coefficients of the actual temperature profile in the space spanned by the principal temperature profile components.…”

“…Secondly, by making use of the inherent redundancy of line-by-line calculations by using data reduction techniques such as principal component analysis (e.g., Efremenko et al, 2013;Jolliffe, 2002), the number of individual radiative transfer simulations is reduced. Approaches have been published for the IR spectral region by Liu et al (2006) and the VIS to SWIR region by Natraj et al (2010Natraj et al ( , 2005 and Lindstrot and Preusker (2012).…”

Retrieval of Daytime Total Column Water Vapour from OLCI Measurements over Land Surfaces

“…Consequently, the knowledge of the actual temperaturepressure profile is necessary in order to simulate the correct atmospheric transmittance. References [22,50] showed, that it is sufficient to use the surface pressure and the 2 mtemperature to approximate the transmittance corresponding to the actual temperature profile by adequately mixing the pre-calculated transmittance values corresponding to two close standard profiles.…”

SVEEEETIES: singular vector expansion to estimate Earth-like exoplanet temperatures from infrared emission spectra

“…As an alternative to parameterized temperature representations or level-by-level discretization, an expansion of the temperature profile using an appropriate set of basis function can be used. Singular value decomposition (SVD) and principal component analysis (PCA) have been established as powerful tools for solving inverse problems (e.g., Jarchow & Hartogh 1998;Lindstrot & Preusker 2012;Waldmann et al 2015a;Damiano et al 2019;Fan et al 2019). B-spline approximations of the unknown profile are another widely used approach for solving inverse problems (e.g., O'Sullivan & Wahba 1985;Doicu et al 2005).…”