Neural network emulations for complex multidimensional geophysical mappings: Applications of neural network techniques to atmospheric and oceanic satellite retrievals and numerical modeling

Abstract

A group of geophysical applications, which from the mathematical point of view, can be formulated as complex, multidimensional, nonlinear mappings and which in terms of the neural network (NN) technique, utilize a particular type of NN, the multilayer perceptron (MLP), is reviewed in this paper. This type of NN application covers the majority of NN applications developed in geosciences like satellite remote sensing, meteorology, oceanography, numerical weather prediction, and climate studies. The major properties of the mappings and MLP NNs are formulated and discussed. Three particular groups of NN applications are presented in this paper as illustrations: atmospheric and oceanic satellite remote sensing applications, NN emulations of model physics for developing atmospheric and oceanic hybrid numerical models, and NN emulations of the dependencies between model variables for application in data assimilation systems.