  Contemporary weather forecasts are typically based on ensemble prediction
systems, which consist of multiple runs of numerical weather prediction models
that vary with respect to in the initial conditions and/or the the
parameterization of the atmosphere. Ensemble forecasts are frequently biased
and show dispersion errors and thus need to be statistically postprocessed.
However, current postprocessing approaches are often univariate and apply to a
single weather quantity at a single location and for a single prediction
horizon only, thereby failing to account for potentially crucial dependence
structures. Non-parametric multivariate postprocessing methods based on
empirical copulas, such as ensemble copula coupling or the Schaake shuffle, can
address this shortcoming. A specific implementation of the Schaake shuffle,
called the SimSchaake approach, is introduced. The SimSchaake method aggregates
univariately postprocessed ensemble forecasts using dependence patterns from
past observations. Specifically, the observations are taken from historical
dates at which the ensemble forecasts resembled the current ensemble prediction
with respect to a specific similarity criterion. The SimSchaake ensemble
outperforms all reference ensembles in an application to ensemble forecasts for
surface temperature from the European Centre for Medium-Range Weather
Forecasts.
