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Can a Multimodel Superensemble Technique Be Used for Precipitation Forecasts? : Volume 25, Issue 25 (08/03/2010)

By Cane, D.

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Book Id: WPLBN0003998940
Format Type: PDF Article :
File Size: Pages 6
Reproduction Date: 2015

Title: Can a Multimodel Superensemble Technique Be Used for Precipitation Forecasts? : Volume 25, Issue 25 (08/03/2010)  
Author: Cane, D.
Volume: Vol. 25, Issue 25
Language: English
Subject: Science, Advances, Geosciences
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Historic
Publication Date:
2010
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

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Cane, D., & Milelli, M. (2010). Can a Multimodel Superensemble Technique Be Used for Precipitation Forecasts? : Volume 25, Issue 25 (08/03/2010). Retrieved from http://www.ebooklibrary.org/


Description
Description: Regional Environmental Protection Agency – Arpa Piemonte, Torino, Italy. The Multimodel SuperEnsemble technique is a postprocessing method for the estimation of weather forecast parameters reducing direct model output errors. It differs from other ensemble analysis techniques by the use of an adequate weighting of the input forecast models in order to obtain a combined estimation of meteorological parameters. Weights are calculated by least-square minimization of the differences between the model and the observed field during a so-called training period.

Although it can be applied successfully on continuous parameters like temperature, relative humidity, wind speed and mean sea level pressure, the Multimodel SuperEnsemble also gives good results when applied on the precipitation, a parameter quite difficult to handle with standard post-processing methods. Here we present a methodology for the Multimodel precipitation forecasts with a careful ensemble dressing via the precipitation PDF estimation.


Summary
Can a Multimodel SuperEnsemble technique be used for precipitation forecasts?

Excerpt
Stefanova L. and Krishnamurti T. N.: Interpretation of Seasonal Climate Forecast Using Brier Skill Score, The Florida State University Superensemble, and the AMIP-I Dataset, J. Climate, 15, 537–544, 2002.; WMO: Recommendations for the Verification and Intercomparison of QPFs and PQPFs from Operational NWP Models, published by WMO, 2008.; Cane, D. and Milelli, M.: Weather forecasts with Multimodel SuperEnsemble Technique in a complex orography region, Meteorol. Z., 15(2), 1–8, 2006.; Cane, D. and Milelli, M.: Multimodel SuperEnsemble technique for quantitative precipitation forecasts in Piemonte region, Nat. Hazards Earth Syst. Sci., 10, 265–273, 2010.; Krishnamurti T. N., Kishtawal, C. M., Larow, T. E., Bachiochi, D. R., Zhang, Z., Williford, C. E., Gadgil, S., and Surendran, S.: Improved weather and seasonal climate forecasts from Multimodel Superensemble, Science, 285, 1548–1550, 1999.; Hamill, T. M.: Hypothesis tests for evaluating numerical precipitation forecasts, Wea. Forecasting, 14, 155–167, 1999.; McLean Sloughter J., Raftery A. E. and Gneiting T.: Probabilistic Quantitative Precipitation Forecasting Using Bayesian Model Averaging, Tech. Rep no 496, Department of Statistics, University of Washington, 2006.; Raftery, A. E., Gneiting, T., Balabdaoui, F., and Polakowski, M.: Using Bayesian model averaging to calibrate forecast ensembles, Mon. Weather Rev. 133, 1155–1174, 2005.; Weibull, W.: A statistical distribution function of wide applicability, J. Appl. Mech.-Trans. ASME 18(3), 293–297, 1951.

 

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