  Today weather forecasting is conducted using numerical weather prediction
(NWP) models, consisting of a set of differential equations describing the
dynamics of the atmosphere. The output of such NWP models are single
deterministic forecasts of future atmospheric states. To assess uncertainty in
NWP forecasts so-called forecast ensembles are utilized. They are generated by
employing a NWP model for distinct variants. However, as forecast ensembles are
not able to capture the full amount of uncertainty in an NWP model, they often
exhibit biases and dispersion errors. Therefore it has become common practise
to employ statistical post processing models which correct for biases and
improve calibration. We propose a novel post processing approach based on
D-vine copulas, representing the predictive distribution by its quantiles.
These models allow for much more general dependence structures than the
state-of-the-art EMOS model and is highly data adapted. Our D-vine quantile
regression approach shows excellent predictive performance in comparative
studies of temperature forecasts over Europe with different forecast horizons
based on the 52-member ensemble of the European Centre for Medium-Range Weather
Forecasting (ECMWF). Specifically for larger forecast horizons the method
clearly improves over the benchmark EMOS model.
