  State-of-the-art weather forecasts usually rely on ensemble prediction
systems, accounting for the different sources of uncertainty. As ensembles are
typically uncalibrated, they should get statistically postprocessed. Several
multivariate ensemble postprocessing techniques, which additionally consider
spatial, inter-variable and/or temporal dependencies, have been developed.
These can be roughly divided into two groups. The first group comprises
parametric, mainly low-dimensional approaches that are tailored to specific
settings. The second group involves non-parametric reordering methods that
impose a specific dependence template on univariately postprocesed forecasts
and are suitable in any dimension. In this paper, these different strategies
are combined, with the aim to exploit the benefits of both concepts.
Specifically, a high-dimensional postprocessing problem is divided into
multiple low-dimensional instances, each of which is postprocessed via a
suitable multivariate parametric method. From each postprocessed
low-dimensional distribution, a sample is drawn, which is then reordered
according to the corresponding multidimensional rank structure of an
appropriately chosen dependence template. In this context, different ranking
concepts for multivariate settings are discussed. Finally, all reordered
samples are aggregated to obtain the overall postprocessed ensemble. The new
approach is applied to ensemble forecasts for temperature and wind speed at
several locations from the European Centre for Medium-Range Weather Forecasts,
using a recent bivariate ensemble model output statistics postprocessing
technique and a reordering based on the raw ensemble forecasts similar to the
ensemble copula coupling method. It shows good predictive skill and outperforms
reference ensembles.
