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| Titel |
Stochastic bias correction of dynamically downscaled precipitation fields for Germany through Copula-based integration of gridded observation data |
| VerfasserIn |
G. Mao, S. Vogl, P. Laux, S. Wagner, H. Kunstmann |
| 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 ; 19, no. 4 ; Nr. 19, no. 4 (2015-04-17), S.1787-1806 |
| Datensatznummer |
250120683
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| Publikation (Nr.) |
 copernicus.org/hess-19-1787-2015.pdf |
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| Zusammenfassung |
| Dynamically downscaled precipitation fields from regional climate models
(RCMs) often cannot be used directly for regional climate studies. Due to
their inherent biases, i.e., systematic over- or underestimations compared
to observations, several correction approaches have been developed. Most
of the bias correction procedures such as the quantile mapping approach
employ a transfer function that is based on the statistical differences
between RCM output and observations. Apart from such transfer function-based statistical correction algorithms, a stochastic bias correction
technique, based on the concept of Copula theory, is developed here and
applied to correct precipitation fields from the Weather Research and
Forecasting (WRF) model. For dynamically downscaled precipitation fields we
used high-resolution (7 km, daily) WRF simulations for Germany driven by
ERA40 reanalysis data for 1971–2000. The REGNIE (REGionalisierung der NIEderschlagshöhen) data set from the German
Weather Service (DWD) is used as gridded observation data (1 km, daily) and
aggregated to 7 km for this application. The 30-year time series are
split into a calibration (1971–1985) and validation (1986–2000)
period of equal length. Based on the estimated dependence structure
(described by the Copula function) between WRF and REGNIE data and the
identified respective marginal distributions in the calibration period,
separately analyzed for the different seasons, conditional distribution
functions are derived for each time step in the validation period. This
finally allows to get additional information about the range of the
statistically possible bias-corrected values. The results show that the
Copula-based approach efficiently corrects most of the errors in WRF
derived precipitation for all seasons. It is also found that the
Copula-based correction performs better for wet bias correction than for
dry bias correction. In autumn and winter, the correction introduced a
small dry bias in the northwest of Germany. The average relative bias of
daily mean precipitation from WRF for the validation period is reduced
from 10% (wet bias) to −1% (slight dry bias) after the application
of the Copula-based correction. The bias in different seasons is corrected
from 32% March–April–May (MAM), −15% June–July–August (JJA), 4% September–October–November (SON) and 28% December–January–February (DJF) to 16%
(MAM), −11% (JJA), −1% (SON) and −3% (DJF),
respectively. Finally, the Copula-based approach is compared to the
quantile mapping correction method. The root mean square error (RMSE) and
the percentage of the corrected time steps that are closer to the
observations are analyzed. The Copula-based correction derived from the
mean of the sampled distribution reduces the RMSE significantly, while,
e.g., the quantile mapping method results in an increased RMSE for some regions. |
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