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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Volume 15, issue 1
Nonlin. Processes Geophys., 15, 61–70, 2008
https://doi.org/10.5194/npg-15-61-2008
© Author(s) 2008. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
Nonlin. Processes Geophys., 15, 61–70, 2008
https://doi.org/10.5194/npg-15-61-2008
© Author(s) 2008. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.

  05 Feb 2008

05 Feb 2008

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

E. Pisoni1, F. Pastor2, and M. Volta1 E. Pisoni et al.
  • 1Department of Electronic for Automation, University of Brescia, Via Branze 38, 25123 Brescia, Italy
  • 2Centro Estudios Ambientales del Mediterraneo, C. Charles Darwin 14, Paterna, Valencia, Spain

Abstract. 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 quality. In this study a methodology for pre-processing sea surface temperature (SST) data is proposed. The methodology, that processes measures in the visible wavelength, is based on an Artificial Neural Network (ANN) system. The effectiveness of the procedure has been also evaluated comparing 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.

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