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| Titel |
Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations – a comparison of methods |
| VerfasserIn |
L. Gudmundsson, J. B. Bremnes, J. E. Haugen, T. Engen-Skaugen |
| 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 ; 16, no. 9 ; Nr. 16, no. 9 (2012-09-21), S.3383-3390 |
| Datensatznummer |
250013477
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| Publikation (Nr.) |
 copernicus.org/hess-16-3383-2012.pdf |
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| Zusammenfassung |
| The impact of climate change on water resources is usually assessed at
the local scale. However, regional climate models (RCMs) are known to
exhibit systematic biases in precipitation. Hence, RCM simulations
need to be post-processed in order to produce reliable estimates of
local scale climate. Popular post-processing approaches are based on
statistical transformations, which attempt to adjust the
distribution of modelled data such that it closely resembles the
observed climatology. However, the diversity of suggested methods
renders the selection of optimal techniques difficult and therefore
there is a need for clarification. In this paper, statistical
transformations for post-processing RCM output are reviewed and
classified into (1) distribution derived transformations, (2) parametric
transformations and (3) nonparametric transformations, each differing
with respect to their underlying assumptions. A real world
application, using observations of 82 precipitation stations in
Norway, showed that nonparametric transformations have the highest
skill in systematically reducing biases in RCM precipitation. |
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