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Probabilistic Downscaling of Precipitation Data in a Subtropical Mountain Area: a Two-step Approach : Volume 18, Issue 2 (24/03/2011)

By Haas, R.

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Book Id: WPLBN0003974954
Format Type: PDF Article :
File Size: Pages 12
Reproduction Date: 2015

Title: Probabilistic Downscaling of Precipitation Data in a Subtropical Mountain Area: a Two-step Approach : Volume 18, Issue 2 (24/03/2011)  
Author: Haas, R.
Volume: Vol. 18, Issue 2
Language: English
Subject: Science, Nonlinear, Processes
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


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Haas, R., & Born, K. (2011). Probabilistic Downscaling of Precipitation Data in a Subtropical Mountain Area: a Two-step Approach : Volume 18, Issue 2 (24/03/2011). Retrieved from

Description: Institute of Geophysics and Meteorology, University of Cologne, Germany. 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.

Probabilistic downscaling of precipitation data in a subtropical mountain area: a two-step approach

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