Do Multi-Model Ensemble Forecasts Yield Added Value?

The THORPEX goal of improving weather forecasts from one day to two weeks suggests the combination of multi-model and multi-initial-condition ensembles of simulations into a probabilistic forecast of some kind. This contribution presents a simple methodology for combining forecasts (be they high resolution or ensemble forecasts) into a predictive distribution function of a chosen target variable(s). While the identification of the most useful combination is a question of scoring distribution functions, the emphasis here will be on alternative mathematical methods with which to combine the variety of potential inputs. The aim is not to determine which of two operational forecast systems is “better” but rather which combination of all available forecast products is most useful. From a user's point of view, the ideal measure of forecast skill is made in terms of the task at hand, but proof-of-value studies can be expensive and tend to yield domain-specific results. Rather than adopt a particular user's cost function, general measures of skill are employed to distinguish the performance of various combinations (i.e. different interpretations of the information at hand). The methodology is illustrated using a combination of ECMWF and NCEP forecasts, and demonstrates that the distinctions introduced in this work can have a significant impact on the utility of the forecast in application.