Natural Hazards and Earth System Science www.nat-hazards-earth-syst-sci.net 1561-8633 1684-9981 10 2 2010 10.5194/nhess-10-265-2010 http://www.nat-hazards-earth-syst-sci.net/10/265/2010/ http://www.nat-hazards-earth-syst-sci.net/10/265/2010/nhess-10-265-2010.html http://www.nat-hazards-earth-syst-sci.net/10/265/2010/nhess-10-265-2010.pdf 265 273 2010-02-12 Multimodel SuperEnsemble technique for quantitative precipitation forecasts in Piemonte region D. Cane daniele.cane@arpa.piemonte.it M. Milelli Regional Environmental Protection Agency – Arpa Piemonte, Torino, Italy The Multimodel SuperEnsemble technique is a powerful post-processing method for the estimation of weather forecast parameters reducing direct model output errors. It has been applied to real time NWP, TRMM-SSM/I based multi-analysis, Seasonal Climate Forecasts and Hurricane Forecasts. The novelty of this approach lies in the methodology, which differs from ensemble analysis techniques used elsewhere. <br><br> Several model outputs are put together with adequate weights to obtain a combined estimation of meteorological parameters. Weights are calculated by least-square minimization of the difference between the model and the observed field during a so-called training period. Although it can be applied successfully on the continuous parameters like temperature, humidity, wind speed and mean sea level pressure, the Multimodel SuperEnsemble gives good results also when applied on the precipitation, a parameter quite difficult to handle with standard post-processing methods. Here we present our methodology for the Multimodel precipitation forecasts, involving a new accurate statistical method for bias correction and a wide spectrum of results over Piemonte very dense non-GTS weather station network. Anderson, J. L.: A method for producing and evaluating probabilistic forecasts from ensemble model integrations, J. Climate, 9, 1518–1530, 1996. Cane, D. and Milelli, M.: Use of Multimodel SuperEnsemble Technique for Mountain-area weather forecast in the Olympic Area of Torino 2006, Croat. Met. J., 40, 215–219, 2005. Cane, D. and Milelli, M.: Weather forecasts with Multimodel SuperEnsemble Technique in a complex orography region, Meteorol. Z., 15(2), 1–8, 2006. Hamill, T. M.: Interpretation of rank histograms for verifying ensemble forecasts, Mon. Weather Rev., 129, 550–560, 2001. 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. Krishnamurti, T. N., Kishtawal, C. M., Larow, T. E., Bachiochi, D. R., Zhang, Z., Williford, C. E., Gadgil, S., and Surendran, S.: Multi-model superensemble forecasts for weather and seasonal climate, J. Climate, 13, 4217–4227, 2000. Krishnamurti, T. N., Stefanova, L., Chakraborty, A., Vijaya Kumar, T. S. V., Cocke, S., Bachiochi, D. R., and Mackey, B.: Seasonal Forecasts of precipitation anomalies for North American and Asian Monsoons, J. Meteorol. Soc. Jpn, 80(6), 1415–1426, 2002. Lovejoy, S., Duncan, M. R., and Schertzer, D.: Scalar multi-fractal radar observer's problem, J. Geophys. Res.-Atmos., 101(D21), 26479–26491, 1996. Raftery, A. E., Gneiting, T., Balabdaoui, F., and Polakowski, M.: Using a Bayesian model averaging to calibrate forecast ensembles, Mon. Weather Rev., 133, 1155–1174, 2005. Wilks, D. S.: Statistical Methods in the Atmospheric Sciences. An Introduction, Academic Press, San Diego, 467~pp., 1995. Williford, C. E., Krishnamurti, T. N., Correa-Torres, R. J., Cocke, S., Christidis, Z., and Vijaya Kumar, T. S. V.: Real Time Multianalysis/Multimodel Superensemble Forecasts of Precipitation using TRMM and SSM/I Products, Mon. Weather Rev., 131/8, 1878–1894, 2002.