Towards a new generation of artificial-intelligence-based infrared atmospheric sounding interferometer retrievals of surface temperature: Part I - Methodology

Multilayer perceptrons have been popular in the remote-sensing community for the last 30 years, in particular for the infrared atmospheric sounding interferometer instrument. However, for coarse-resolution infrared instruments such as the infrared atmospheric sounding interferometer, these algorithms are currently used at the pixel level and are trained at a global scale. This can result in regional biases that not only affect the quality of the retrieval but can potentially also propagate into model forecasts if these results were to be assimilated. To help reduce these biases, we try to help the neural network (NN) adjusting its behaviour to local conditions; we call this "localization". We investigate how to localize traditional multilayer perceptrons applied at the pixel scale and compare this with novel artificial intelligence image-processing techniques, more particularly convolutional NNs and so-called "localized convolutional NNs". These approaches are tested for the retrieval of surface temperature over a fixed domain. Different techniques are proposed to localize both approaches. Further evaluation of the retrieval methods will be covered in a part II companion paper.