Extended Abstract

Can Multi-model ensemble forecasts improve probability ofprecipitation forecasts compared to single model ensembleforecasts?1. IntroductionLaurence J. Wilson 1 and Anna Ghelli 21 Environment Canada2 European Center for Medium Range Weather Forecastslawrence.wilson@ec.gc.caThe TIGGE (Thorpex Interactive Grand Global Ensemble) project was designed toencourage research to determine the advantages of merging ensemble forecasts fromdifferent centers. Most verification studies using the TIGGE database have so farfocused on the evaluation of upper air variables with respect to gridded analyses. Forexample Park et al (2007) looked at the performance of the models in terms ofgeopotential height at 500hPa and temperature at 850hPa. Johnson and Swinbank(2009) discussed the advantages of model combinations using geopotential height at500hPa and mean sea level pressure. They also considered 2m Temperature in theirevaluation, but still with respect to analysis data. Yi He et al (2008) studied the benefitsof multimodel ensembles in a hydrological context and more recently Froude (2010)compared the performance of the ensemble systems when used for tropical cycloneforecasts. In general, these studies show that improvements in probabilistic forecastscan be obtained from combined ensembles compared to single model ensembles,especially if the models used in the combination are of relatively high quality, but themagnitude of the improvement has not been found to be large.We have been using the TIGGE database to try to answer the question of the benefitsof combined ensembles for a long-period sample of precipitation forecasts. The maingoal of this work is to try to answer the TIGGE question posed above in a more userfriendlyway, which is by comparing directly with surface observations rather thananalysed data. This approach has the advantage that no model dependence isintroduced into the verification dataset. This is particularly important when comparingthe quality of several different systems since none of the systems is given an unfairadvantage or disadvantage compared to the others by use of truth data which has beenprocessed using one of the candidate models. Furthermore, exactly the same data isused for the verification of all the ensembles. We also wish to obtain as representativea result as possible, by using a long time period for the model evaluations.We emphasize at the outset that this is a work in process. The results presented beloware samples recently obtained, and we continue to analyse the results and to carry outfurther experiments using different verification measures, and for different regions.Most of the work has so far been done for Europe where a dense network of rain gaugeobservations is available, but efforts are underway for Canadian stations, and otherregions will need to be evaluated before the TIGGE question is fully answered. Themethodology and data are described in the next section, followed by sample results,-357-