###### Abstract  
A new data fusion technique based on Artificial Neural Networks (ANN) for the design of a rainfall retrieval algorithm is presented. The use of both VIS/IR (VISible and InfraRed) data from GEO (Geostationary Earth Orbit) satellite and of passive microwave data from LEO (Low Earth Orbit) satellite can take advantage of both types of sensors reducing their limitations. The technique can reconstruct the surface rain field with the MSG-SEVIRI (Meteosat Second Generation-Spinning Enhanced Visible Infrared Imager) spatial and temporal resolution, which means 3 km at the sub satellite point and 5 km at mid-latitudes, every 15 min, respectively. Rainfall estimations are also compared with H-SAF (Hydrology Satellite Application Facility) PR-OBS3A operational product showing better performance both on the identification of rainy areas and on the retrieval of the amount of precipitation. In particular, in the considered test cases, results report an improvement in average of 83% in terms of probability of rainy areas detection, of 45% in terms of false alarm rate, and of 47% in terms of root mean square error in the retrieval of the amount of precipitation. p +
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the physical connection, based on scattering and absorption mechanisms, between the visible and infrared part of the spectrum and precipitation is not as strong as for microwave, affecting the final accuracies. A rising number of techniques are being also developed to exploit the synergy between the polar-orbiting PMW retrievals (irregular, and more direct) with the geostationary VIS/NIR/IR observations (regular, and less direct). Most of them rely on some empirical inter-calibration operations [7]. This type of approach, however, may have generalization limitations as the calibration holds only in a specific geographic area or for a specific class of events. A motion-based technique has been also proposed [8], where the IR data provide a reasonable measure of cloud movement, which is then used to advect or morph the more direct PMW data between the successive satellite overpasses. A more conceptual cloud-development model, driven by GEO satellite imagery, and locally updated using MW-based rainfall measurements from LEO platforms, is described in [PERSON] et al. [9], such an approach has been refined in [10] where cloud classification is a specific step incorporated in the precipitation estimation method. In the context of the data fusion methods, the use of the Meteosat Second Generation (MSG) satellite mission from the European Organization for the Exploitation of Meteorological Satellites (EUMESTAT) has been only sparsely addressed [11]. MSG is a very important mission in the context of GEO satellites. It carries the SEVIRI instrument, a line-by-line scanning radiometer, which provides image data in four Visible (VIS) and Near-InfraRed (VNIR) channels and eight InfraRed (IR) channels. Although \"operational\" precipitation products are routinely produced within concerted programs, such as the EUMESTAT Satellite Applications Facility on Support to Operational Hydrology and Water Management (H-SAF) [12], more investigation is needed to analyze the potential of a data fusion approach merging information provided by PMW and MSG. The objective of this paper is to provide a contribution along this direction. The final goal is to improve the accuracy of up-to-date precipitation products based on EUMESTAT SEVIRI data. To this purpose, we propose an innovative methodology for merging MW and VIS/NIR/IR MSG observations based on neural networks (NN). In fact, rainfall estimation is a problem that contains many unknown processes. The inverse problem of the radiative transfer process involves an ill-posed scenario which yields many solutions for a given set of brightness temperatures. In such circumstances, NN models, which can learn input-output significant relationships directly from the training data and, if the training process is carried out properly, are able to generalize out of them, can be very useful.