Satellite Data Processing for Meteorological Nowcasting and Very Short Range Forecasting Using Neural Networks

Abstract

This paper addresses the processing of satellite data with meteorological nowcasting and very short range forecasting purposes in the context of the SAF NWC (Satellite Application Facility for NoWCasting) project for Meteosat Second Generation (EUMESAT). Among the many aspects involved in nowcasting, air mass analysis (including vertical stability and water vapour distribution, and total water vapour content) is considered. Hence, the forecast characterization requires the quantification of the corresponding meteorological parameters. In general, this quantification has to rely on traditional tools, such as linear regression models, which provide partial information of the involved phenomena. Here, a Neural Network (NN) based model is proposed, where a Hebbian Neural Network (HNN) is combined with a Multilayer Perceptron (MLP), supervised NN. HNNs are used to perform a principal component analysis of the multi-spectral images so that the dimensionality of the problem is reduced keeping the relevant information. Then, the MLP is trained to perform a diagnosis associated with each pixel. The proposed combined architecture is evaluated with real data.