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| Titel |
Artificial Neural Networks to reconstruct incomplete satellite data: application to the Mediterranean Sea Surface Temperature |
| VerfasserIn |
E. Pisoni, F. Pastor, M. Volta |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 15, no. 1 ; Nr. 15, no. 1 (2008-02-05), S.61-70 |
| Datensatznummer |
250012554
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| Publikation (Nr.) |
 copernicus.org/npg-15-61-2008.pdf |
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| Zusammenfassung |
| 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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