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| Titel |
Probabilistic downscaling of precipitation data in a subtropical mountain area: a two-step approach |
| VerfasserIn |
R. Haas, K. Born |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 18, no. 2 ; Nr. 18, no. 2 (2011-03-24), S.223-234 |
| Datensatznummer |
250013896
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| Publikation (Nr.) |
 copernicus.org/npg-18-223-2011.pdf |
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| Zusammenfassung |
| In this study, a two-step probabilistic downscaling approach is introduced
and evaluated. The method is exemplarily applied on precipitation
observations in the subtropical mountain environment of the High Atlas in
Morocco. The challenge is to deal with a complex terrain, heavily skewed
precipitation distributions and a sparse amount of data, both spatial and
temporal. In the first step of the approach, a transfer function between
distributions of large-scale predictors and of local observations is derived.
The aim is to forecast cumulative distribution functions with parameters from
known data. In order to interpolate between sites, the second step applies
multiple linear regression on distribution parameters of observed data using
local topographic information. By combining both steps, a prediction at every
point of the investigation area is achieved. Both steps and their combination
are assessed by cross-validation and by splitting the available dataset into
a trainings- and a validation-subset. Due to the estimated quantiles and
probabilities of zero daily precipitation, this approach is found to be
adequate for application even in areas with difficult topographic
circumstances and low data availability. |
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