The individual members of a (Monte Carlo) ensemble forecasts are often interpreted as single deterministic forecasts; a more coherent interpretation involves dressing these delta function forecasts before the prediction system evaluated (against a dressed `best guess' forecast), or deployed for commercial use. Good probability distributions of future outcomes provide a crucial tool in risk management. The recent trend towards probabilistic weather forecasts instead of traditional `best guess' forecasts should enable businesses with weather risk exposure to assess and manage this risk with greater rigour. Quantifying the skill of ensemble weather forecasts as weather forecasts requires dressing the ensembles before comparison with the observations. The ideal forecast product for an end-user, however, is not a probabilistic weather forecast at all but a probabilistic forecast of a weather dependent economic quantity (e.g. soft drink sales, electricity demand, wind energy production). In both applications, the first challenge is to convert the ensemble of deterministic forecasts into a bona fide probabilistic forecast. This requires knowledge of the error statistics associated with each ensemble member. Extracting such statistics is not as straightforward as with a single deterministic forecasts. In the second application, the probabilistic forecast must be transformed into a function of the economic quantity. The dependence of economic quantities of interest on the weather is generally nonlinear. This means that the expected value of the economic quantity is not the value associated with the expected weather.

We present several examples to illustrate this `end-to-end' forecasting approach. Moving away from a simple `cost-lost' scenario, several examples, including (i) U.K. electricity demand, and (ii) Wind energy production, are employed to demonstrate how the ECMWF ensemble forecast product can be translated into probability functions familiar to risk management professionals.