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| Titel |
Evaluation of a bias correction method applied to downscaled precipitation and temperature reanalysis data for the Rhine basin |
| VerfasserIn |
W. Terink, R. T. W. L. Hurkmans, P. J. J. F. Torfs, R. Uijlenhoet |
| 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 ; 14, no. 4 ; Nr. 14, no. 4 (2010-04-22), S.687-703 |
| Datensatznummer |
250012270
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| Publikation (Nr.) |
 copernicus.org/hess-14-687-2010.pdf |
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| Zusammenfassung |
| In many climate impact studies hydrological models are forced with
meteorological data without an attempt to assess the quality of these data.
The objective of this study was to compare downscaled ERA15 (ECMWF-reanalysis
data) precipitation and temperature with observed precipitation and
temperature and apply a bias correction to these forcing variables.
Precipitation is corrected by fitting it to the mean and coefficient of variation
(CV) of the observations. Temperature is corrected by fitting it to the mean and
standard deviation of the observations. It appears that the uncorrected ERA15
is too warm and too wet for most of the Rhine basin. The bias correction
leads to satisfactory results, precipitation and temperature differences
decreased significantly, although there are a few years for which the
correction of precipitation is less satisfying. Corrections were largest
during summer for both precipitation and temperature. For precipitation alone
large corrections were applied during September and October as well. Besides
the statistics the correction method was intended to correct for, it is also
found to improve the correlations for the fraction of wet days and lag-1
autocorrelations between ERA15 and the observations. For the validation
period temperature is corrected very well, but for precipitation the RMSE of
the daily difference between modeled and observed precipitation has increased
for the corrected situation. When taking random years for calibration, and
the remaining years for validation, the spread in the mean bias error (MBE)
becomes larger for the corrected precipitation during validation, but the
overal average MBE has decreased. |
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