Advances in Geosciences www.adv-geosci.net 1680-7340 1680-7359 25 Precipitation: Measurement, Climatology, Remote Sensing, and Modeling (EGU Session 2009) 2010 10.5194/adgeo-25-17-2010 http://www.adv-geosci.net/25/17/2010/ http://www.adv-geosci.net/25/17/2010/adgeo-25-17-2010.html http://www.adv-geosci.net/25/17/2010/adgeo-25-17-2010.pdf 17 22 2010-03-08 Can a Multimodel SuperEnsemble technique be used for precipitation forecasts? D. Cane daniele.cane@arpa.piemonte.it M. Milelli 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. <br><br> 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. 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. 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. Weibull, W.: A statistical distribution function of wide applicability, J. Appl. Mech.-Trans. ASME 18(3), 293–297, 1951. WMO: Recommendations for the Verification and Intercomparison of QPFs and PQPFs from Operational NWP Models, published by WMO, 2008.