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| Titel |
Precipitation bias correction of very high resolution regional climate models |
| VerfasserIn |
D. Argüeso, J. P. Evans, L. Fita |
| 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 ; 17, no. 11 ; Nr. 17, no. 11 (2013-11-06), S.4379-4388 |
| Datensatznummer |
250085985
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| Publikation (Nr.) |
 copernicus.org/hess-17-4379-2013.pdf |
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| Zusammenfassung |
Regional climate models are prone to biases in precipitation that are
problematic for use in impact models such as hydrology models. A large number
of methods have already been proposed aimed at correcting various moments of
the rainfall distribution. They all require that the model produce the same
or a higher number of rain days than the observational data sets, which are
usually gridded data sets. Models have traditionally met this condition
because their spatial resolution was coarser than the observational grids.
But recent climate simulations use higher resolution and the models are
likely to systematically produce fewer rain days than the gridded
observations.
In this study, model outputs from a simulation at 2 km resolution are
compared with gridded and in situ observational data sets to determine whether
the new scenario calls for revised methodologies. The gridded observations
are found to be inadequate to correct the high-resolution model at daily
timescales, because they are subjected to too frequent low intensity
precipitation due to spatial averaging. A histogram equalisation bias
correction method was adapted to the use of station, alleviating the problems
associated with relative low-resolution observational grids. The wet-day
frequency condition might not be satisfied for extremely dry biases, but the
proposed approach substantially increases the applicability of bias
correction to high-resolution models. The method is efficient at bias
correcting both seasonal and daily characteristic of precipitation, providing
more accurate information that is crucial for impact assessment studies. |
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