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CapsuleState-of-the-Art statistical postprocessing techniques for ensemble forecasts are reviewed, together with the challenges posed by a demand for timely, high-resolution and reliable probabilistic information. Possible research avenues are also discussed.

[1] This study describes the experimental setup of an atmospheric ensemble prediction system (EPS) based on a high resolution numerical weather prediction model developed at Deutscher Wetterdienst (DWD). The focus is set on uncertainties of initial conditions, and how to combine them with perturbations of the boundary conditions and model physics variations, discussed in a previous investigation. Two ensemble setups are constructed: one with model physics diversity and variations of boundaries (BP ensemble) and one which also includes perturbations of initial conditions (IBP ensemble). Experimental ensemble forecasts of precipitation with a lead time of 24 hours, for a period of 29 days in the summer of 2009, are generated. Deterministic verification shows that the inclusion of the initial perturbations does not decrease the quality of the forecast, with individual members of the IBP and BP ensembles being statistically similar. The probabilistic scores of the IBP ensemble are better than those of the BP ensemble during the first 12 hours of the forecast, and afterwards both have similar performances. Similarly, the IBP ensemble provides more spread during the first 12 hours of the forecast, decreasing to levels comparable to the BP ensemble afterwards.

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Spatial techniques have been developed to quantify the performance of a system beyond the classical pointto-point comparison with observations. Including spatial neighbourhood information in the verification process, the quality of a forecast can be better characterized. Guidance for the interpretation of deterministic forecasts can also be delivered. This paper investigates the application of spatial techniques to ensemble forecasts. The aim is to assess ensemble forecast skills better and to provide improved guidance to the forecasters in the form of refined probabilistic products. Two spatial techniques are applied to precipitation forecasts derived from an ensemble system at the convective scale (COSMO-DE-EPS). The first technique is a smoothing method which enlarges the ensemble sample size by neighbouring forecasts. The resulting forecasts are called fuzzy probabilistic forecasts. The second method is an upscaling procedure which modifies the reference area of the probabilities. Fuzzy and upscaled probabilistic forecasts are assessed over a 3 month period covering summer 2011. The impact of smoothing and upscaling is investigated for a range of neighbourhood sizes and spatial scales respectively. Based on the verification results, recommendations are drawn how to use these techniques in optimally presenting COSMO-DE-EPS probabilistic products to forecasters who issue weather warnings.