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On Evaluation of Ensemble Precipitation Forecasts with Observation-based Ensembles : Volume 10, Issue 10 (26/04/2007)

By Ahrens, B.

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Book Id: WPLBN0003998610
Format Type: PDF Article :
File Size: Pages 6
Reproduction Date: 2015

Title: On Evaluation of Ensemble Precipitation Forecasts with Observation-based Ensembles : Volume 10, Issue 10 (26/04/2007)  
Author: Ahrens, B.
Volume: Vol. 10, Issue 10
Language: English
Subject: Science, Advances, Geosciences
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


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Jaun, S., & Ahrens, B. (2007). On Evaluation of Ensemble Precipitation Forecasts with Observation-based Ensembles : Volume 10, Issue 10 (26/04/2007). Retrieved from

Description: Institute for Atmosphere and Environment, University of Frankfurt, Germany. Spatial interpolation of precipitation data is uncertain. How important is this uncertainty and how can it be considered in evaluation of high-resolution probabilistic precipitation forecasts? These questions are discussed by experimental evaluation of the COSMO consortium's limited-area ensemble prediction system COSMO-LEPS. The applied performance measure is the often used Brier skill score (BSS). The observational references in the evaluation are (a) analyzed rain gauge data by ordinary Kriging and (b) ensembles of interpolated rain gauge data by stochastic simulation. This permits the consideration of either a deterministic reference (the event is observed or not with 100% certainty) or a probabilistic reference that makes allowance for uncertainties in spatial averaging. The evaluation experiments show that the evaluation uncertainties are substantial even for the large area (41 300 km2) of Switzerland with a mean rain gauge distance as good as 7 km: the one- to three-day precipitation forecasts have skill decreasing with forecast lead time but the one- and two-day forecast performances differ not significantly.

On evaluation of ensemble precipitation forecasts with observation-based ensembles

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