Towards a new generation of artificial‐intelligence‐based infrared atmospheric sounding interferometer retrievals of surface temperature: Part II – Assessment

Abstract

AbstractWhen using neural networks (NNs), the lack of input information characterizing the radiative transfer can result in regional biases, especially when retrieving surface properties. In the Part I companion article we explored localization techniques in an attempt to help the NN adjust its behaviour to local conditions. In this article we analyze results from an image‐processing approach, the novel localized convolutional NN (CNN) model for the retrieval of surface temperature (TS) over a fixed domain using infrared atmospheric sounding interferometer (IASI) observations. An in‐depth evaluation is performed. The localized‐CNN architecture is a promising artificial intelligence solution that provides retrievals similar to, if not better than, those of the European Organisation for the Exploitation of Meteorological Satellites' PWLR3 retrieval algorithm that also uses IASI observations, collocated with microwave data too. This shows the benefits of localizing the CNN retrieval. This image‐processing retrieval scheme allows interpolation of the TS below the clouds, and thus a preliminary analysis of the cloud impact on the TS is performed. The possibility to estimate retrieval uncertainties is investigated, and a practical solution, based on the binning of the input space, is proposed for CNN architectures. The best strategy for a global‐scale retrieval is yet to be found for such an image‐processing scheme, but potential solutions and their respective advantages and disadvantages are discussed.