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| Titel |
Testing the performance of three nonlinear methods of time seriesanalysis for prediction and downscaling of European daily temperatures |
| VerfasserIn |
J. Miksovsky, A. Raidl |
| 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 ; 12, no. 6 ; Nr. 12, no. 6 (2005-11-09), S.979-991 |
| Datensatznummer |
250010900
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| Publikation (Nr.) |
 copernicus.org/npg-12-979-2005.pdf |
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| Zusammenfassung |
| We investigated the usability of the method of local linear models (LLM),
multilayer perceptron neural network (MLP NN) and radial basis function
neural network (RBF NN) for the construction of temporal and spatial transfer
functions between different meteorological quantities, and compared the
obtained results both mutually and to the results of multiple linear regression
(MLR). The tested methods were applied for the short-term prediction of daily
mean temperatures and for the downscaling of NCEP/NCAR reanalysis data, using
series of daily mean, minimum and maximum temperatures from 25 European
stations as predictands. None of the tested nonlinear methods was recognized
to be distinctly superior to the others, but all nonlinear techniques proved
to be better than linear regression in the majority of the cases. It is also
discussed that the most frequently used nonlinear method, the MLP neural
network, may not be the best choice for processing the climatic time series
- LLM method or RBF NNs can offer a comparable or slightly better performance
and they do not suffer from some of the practical disadvantages of MLPs.
Aside from comparing the performance of different methods, we paid attention
to geographical and seasonal variations of the results. The forecasting
results showed that the nonlinear character of relations between climate
variables is well apparent over most of Europe, in contrast to rather weak
nonlinearity in the Mediterranean and North Africa. No clear large-scale
geographical structure of nonlinearity was identified in the case of
downscaling. Nonlinearity also seems to be noticeably stronger in winter
than in summer in most locations, for both forecasting and downscaling. |
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