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| Titel |
Linear and nonlinear post-processing of numerically forecasted surface temperature |
| VerfasserIn |
M. Casaioli, R. Mantovani, F. Proietti Scorzoni, S. Puca, A. Speranza, B. Tirozzi |
| 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 ; 10, no. 4/5 ; Nr. 10, no. 4/5, S.373-383 |
| Datensatznummer |
250007838
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| Publikation (Nr.) |
 copernicus.org/npg-10-373-2003.pdf |
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| Zusammenfassung |
| In this paper we
test different approaches to the statistical post-processing of gridded
numerical surface air temperatures (provided by the European Centre for
Medium-Range Weather Forecasts) onto the temperature measured at surface
weather stations located in the Italian region of Puglia. We consider
simple post-processing techniques, like correction for altitude, linear
regression from different input parameters and Kalman filtering, as well
as a neural network training procedure, stabilised (i.e. driven into the
absolute minimum of the error function over the learning set) by means of
a Simulated Annealing method. A comparative analysis of the results shows
that the performance with neural networks is the best. It is encouraging
for systematic use in meteorological forecast-analysis service operations. |
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