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| Titel |
Comparison of data-driven methods for downscaling ensemble weather forecasts |
| VerfasserIn |
Xiaoli Liu, P. Coulibaly, N. Evora |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 12, no. 2 ; Nr. 12, no. 2 (2008-03-20), S.615-624 |
| Datensatznummer |
250010580
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| Publikation (Nr.) |
 copernicus.org/hess-12-615-2008.pdf |
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| Zusammenfassung |
| This study investigates dynamically different data-driven methods,
specifically a statistical downscaling model (SDSM), a time lagged
feedforward neural network (TLFN), and an evolutionary polynomial regression
(EPR) technique for downscaling numerical weather ensemble forecasts
generated by a medium range forecast (MRF) model. Given the coarse
resolution (about 200-km grid spacing) of the MRF model, an optimal use of
the weather forecasts at the local or watershed scale, requires appropriate
downscaling techniques. The selected methods are applied for downscaling
ensemble daily precipitation and temperature series for the Chute-du-Diable
basin located in northeastern Canada. The downscaling results show that the
TLFN and EPR have similar performance in downscaling ensemble daily
precipitation as well as daily maximum and minimum temperature series
whatever the season. Both the TLFN and EPR are more efficient downscaling
techniques than SDSM for both the ensemble daily precipitation and
temperature. |
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