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Satellite data can be very useful in applications where extensive spatial information is needed, but sometimes missing data due to presence of clouds can affect data qual- ity. In this study a methodology for pre-processing sea sur- face temperature (SST) data is proposed. The methodology, that processes measures in the visible wavelength, is based on an Artificial Neural Network (ANN) system. The effec- tiveness of the procedure has been also evaluated compar- ing results obtained using an interpolation method. After the methodology has been identified, a validation is performed on 3 different episodes representative of SST variability in the Mediterranean sea. The proposed technique can process SST NOAA/AVHRR data to simulate severe storm episodes by means of prognostic meteorological models.

Artificial Neural Networks to reconstruct incomplete satellite data: application to the Mediterranean Sea Surface Temperature