The development of a fast radiative transfer model based on an empirical orthogonal functions (EOF) technique

Abstract

Remote sensing with the new generation of highly spectrally resolving instruments like the Atmospheric Research Interferometer Evaluation System (ARIES) or the assimilation of highly resolved spectra from satellites into Numerical Weather Prediction (NWP) systems requires radiative transfer computations that deliver results essentially instantaneous. This paper reports on the development of such a new fast radiative transfer model. The model is based on an Empirical Orthogonal Functions (EOF) technique. The model can be used for the simulation of sensors with different characteristics and in different spectral ranges from the solar to the infrared. For the purpose of airborne remote sensing, the fast model has been designed to work on any altitude and for slant paths whilst looking down or up. The fast model works for situations with diverse temperature and humidity profiles to an accuracy of better than 0.01K for most of the instrument channels. The EOF fast model works for clear-sky atmospheres and is applicable to atmospheres with scattering layers of aerosols or clouds. The fast model is trained with a large set of diverse atmospheric training profiles. In forward calculations corresponding high resolution spectra are obtained. An EOF analysis is performed on these spectra and only the leading EOF are retained (data compression). When the fast model is applied to a new independent profile, only the weights of the EOF need to be calculated (=predicted). Monochromatic radiances at suitable frequencies are used as predictors. The frequency selection is done by a cluster algorithm, which sorts frequencies with similar characteristics into clusters.