A Regularized Neural Net Approach for Retrieval of Atmospheric and Surface Temperatures with the IASI Instrument

Abstract

In this paper, a fast atmospheric and surface temperature retrieval algorithm is developed for the high-resolution Infrared Atmospheric Sounding Interferometer (IASI) spaceborne instrument. This algorithm is constructed on the basis of a neural network technique that has been regularized by introduction of information about the solution of the problem that is in addition to the information contained in the problem (a priori information). The performance of the resulting fast and accurate inverse radiative transfer model is presented for a large diversified dataset of radiosonde atmospheres that includes rare events. Two configurations are considered: a tropical-airmass specialized scheme and an all-airmasses scheme. The surface temperature for tropical situations yields an rms error of 0.4 K for instantaneous retrievals. Results for atmospheric temperature profile retrievals are close to the specifications of the World Meteorological Organization, namely, 1-K rms error for the instantaneous temperature retrieval with 1-km vertical resolution.