  Statistical post-processing of dynamical forecast ensembles is an essential
component of weather forecasting. In this article, we present a post-processing
method that generates full predictive probability distributions for
precipitation accumulations based on ensemble model output statistics (EMOS).
We model precipitation amounts by a generalized extreme value distribution that
is left-censored at zero. This distribution permits modelling precipitation on
the original scale without prior transformation of the data. A closed form
expression for its continuous rank probability score can be derived and permits
computationally efficient model fitting. We discuss an extension of our
approach that incorporates further statistics characterizing the spatial
variability of precipitation amounts in the vicinity of the location of
interest. The proposed EMOS method is applied to daily 18-h forecasts of 6-h
accumulated precipitation over Germany in 2011 using the COSMO-DE ensemble
prediction system operated by the German Meteorological Service. It yields
calibrated and sharp predictive distributions and compares favourably with
extended logistic regression and Bayesian model averaging which are state of
the art approaches for precipitation post-processing. The incorporation of
neighbourhood information further improves predictive performance and turns out
to be a useful strategy to account for displacement errors of the dynamical
forecasts in a probabilistic forecasting framework.
