  While probabilistic forecast verification for categorical forecasts is well
established, some of the existing concepts and methods have not found their
equivalent for the case of continuous variables. New tools dedicated to the
assessment of forecast discrimination ability and forecast value are introduced
here, based on quantile forecasts being the base product for the continuous
case (hence in a nonparametric framework). The relative user characteristic
(RUC) curve and the quantile value plot allow analysing the performance of a
forecast for a specific user in a decision-making framework. The RUC curve is
designed as a user-based discrimination tool and the quantile value plot
translates forecast discrimination ability in terms of economic value. The
relationship between the overall value of a quantile forecast and the
respective quantile skill score is also discussed. The application of these new
verification approaches and tools is illustrated based on synthetic datasets,
as well as for the case of global radiation forecasts from the high resolution
ensemble COSMO-DE-EPS of the German Weather Service.
