Traditional objective verification of deterministic model output fails to demonstrate the added value of high-resolution forecasts made by such models. It is generally accepted from subjective verification that these models nevertheless have a predictive potential for small-scale weather phenomena and extreme weather events.
In this poster we argue that the evaluation of the information in mesoscale forecasts should be essentially connected to the method that is used to extract this information from the direct model output. This could be, given the probabilistic nature of small-scale weather, preferentially a form of statistical postprocessing. Using Model Output Statistics (MOS) and traditional verification scores the potential of this approach is demonstrated. The MOS approach presented here incorporates concepts from fuzzy verification. It objectively weighs different forecast quality measures and as such it is an essential extension of fuzzy methods. Plans are being developed to apply the method in an experiment to compare the predictive potentials of the high resolution ensemble GLAMEPS (Grand Limited Area Model Ensemble Prediction System) and the ECMWF EPS.