Atmospheric liquid water retrieval using a gated experts neural network

Abstract

Gated experts (GE) neural networks have been developed in order to retrieve atmospheric liquid water content over ocean from radiometer data. Gated experts neural networks are statistical models, which can model any general class of function. This paper focuses on the case where the complex transfer functions can be split on different simpler functions in order to improve the accuracy. Two atmospheric quantities are considered: the integrated cloud liquid water (iclw) and the surface rain rate (RR). In the case of iclw, the GE neural network finds two modes, splitting the problem into low and high iclw values. The physical meaning of those modes is discussed. A comparison with a standard regression algorithm and a multilayer perceptron neural network is done on simulated data and an "indirect comparison" is done using Special Sensor Microwave Imager (SSM/I) data. In the case of RR, the focus is on the ability of GE neural networks to perform a classification between rainy and nonrainy situations. Tropical Rainfall Measuring Mission (TRMM) data are used for rain-rate validation: rain-rate retrieval from the GE algorithm applied to actual TRMM Microwave Imager (TMI) measurements are compared with collocated precipitation radar (PR) rain rate.