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Peter Vogel, Peter Knippertz, Andreas H. Fink, Andreas Schlueter, and Tilmann Gneiting

probability distributions allows a point mass for zero precipitation and flexible modeling in positive precipitation accumulations, depending on the specifics of the ensemble forecast at hand. For mathematical details we refer to the original paper by Scheuerer (2014) . Postprocessing techniques rely on statistical parameters that need to be estimated from training data, comprising forecast–observation-pairs from the TRMM pixel at hand and typically from a rolling training period consisting of the n

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Peter Vogel, Peter Knippertz, Andreas H. Fink, Andreas Schlueter, and Tilmann Gneiting

dispersion errors and biases. Statistical postprocessing addresses these deficiencies and realizes the full potential of ensemble forecasts ( Gneiting and Raftery 2005 ). Additionally, it performs implicit downscaling from the model grid resolution to finer resolutions or station locations. The correction of systematic forecast errors is based on (distributional) regression techniques and, depending on the need of the user, several approaches are at hand ( Schefzik et al. 2013 ; Gneiting 2014 ). Hamill

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