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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
Historic
Publication Date:
2007
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 http://www.ebooklibrary.org/


Description
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.

Summary
On evaluation of ensemble precipitation forecasts with observation-based ensembles

Excerpt
Ahrens, B.: Evaluation of precipitation forecasting with the limited area model ALADIN in an Alpine watershed, Meteorol. Z., 12, 245–255, 2003.; Ahrens, B. and Beck, A.: On upscaling of rain–gauge data for evaluating numerical weather forecasts, Meteorol. Atmos. Phys., in print, 2007.; Atkinson, P. and Lloyd, C.: Mapping precipitation in Switzerland with ordinary and indicator Kriging, J. Geographic Information and Dec. Analysis, 2, 65–76, 1998.; Beck, A. and Ahrens, B.: Multiresolution evaluation of precipitation forecasts over the European Alps, Meteorol. Z., 13, 55–62, 2004.; Chilès, J.-P.: Geostatistics: modeling spatial uncertainty, John Wiley & Sons, New York, 1999.; Creutin, J. and Obled, C.: Objective analyses and mapping techniques for rainfall fields: an objective comparison, Water Resour. Res., 18, 413–431, 1982.; Ehrendorfer, M.: Predicting the uncertainty of numerical weather forecasts: a review, Meteorol. Z., 6, 147–183, 1997.; Grasso, L D.: The differentiation between grid spacing and resolution and their application to numerical modeling, Bull. Amer. Meteorol. Soc., 81, 579–580, 2000.; Johnson, M.: Multivariate Statistical Simulation, Wiley, New York, 1987.; Journel, A.: Geostatistics for conditional simulation of ore bodies, Econom. Geol., 69, 673–687, 1974.; Marsigli, C., Boccanera, F., Montani, A., and Paccagnella, T.: The COSMO-LEPS mesoscale ensemble system: validation of the methodology and verification, Nonlin. Processes Geophys., 12, 527–536, 2005.; Molteni, F., Buizza, R., Marsigli, C., Montani, A., Nerozzi, F., and Paccagnella, T.: A strategy for high-resolution ensemble prediction. I: Definition of representative members and global-model experiments, Quart. J. Roy. Meteorol. Soc., 127, 2069–2094, 2001.; Montani, A., Capaldo, M., Cesari, D., Marsigli, C., Modigliani, U., Nerozzi, F., Paccagnella, T., Patruno, P., and Tibaldi, S.: Operational limited-area ensemble forecasts based on the Lokal Modell, ECMWF Newsletter, 98, 2–7, 2003.; Müller, W., Appenzeller, C., Doblas-Reyes, F., and Liniger, M.: A debiased ranked probability skill score to evaluate probabilistic ensemble forecasts with small ensemble sizes, J. Climate, 18, 1513–1523, 2005.; Palmer, T N.: Predicting uncertainty forecasts of weather and climate, Rep. Prog. Phys., 63, 71–116, 2000.; Pebesma, E.: Multivariable geostatistics in S: the gstat package, Comp. & Geosci., 30, 683–691, 2004.; Stanski, H., Wilson, L., and Burrows, W.: Survey of common verification methods in meteorology, Tech. Rep. WMO/TD No 358, WMO World Weather Watch, 1989.; Weigel, A., Liniger, M., and Appenzeller, C.: The discrete Brier and ranked probability skill scores, Mon. Wea. Rev., 135, 118–124, 2007.; Wilks, D S.: Statistical Methods in the Atmospheric Sciences, Academic Press, San Diego, 2006.; Zhu, Y., Toth, Z., Wobus, R., Richardson, D., and Mylne, K.: The economic value of ensemble–based weather forecasts, Bull. Amer. Meteorol. Soc., 83, 73–83, 2002.

 

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