  A new probabilistic post-processing method for wind vectors is presented in a
distributional regression framework employing the bivariate Gaussian
distribution. In contrast to previous studies all parameters of the
distribution are simultaneously modeled, namely the means and variances for
both wind components and also the correlation coefficient between them
employing flexible regression splines. To capture a possible mismatch between
the predicted and observed wind direction, ensemble forecasts of both wind
components are included using flexible two-dimensional smooth functions. This
encompasses a smooth rotation of the wind direction conditional on the season
and the forecasted ensemble wind direction.
  The performance of the new method is tested for stations located in plains,
mountain foreland, and within an alpine valley employing ECMWF ensemble
forecasts as explanatory variables for all distribution parameters. The
rotation-allowing model shows distinct improvements in terms of predictive
skill for all sites compared to a baseline model that post-processes each wind
component separately. Moreover, different correlation specifications are tested
and small improvements compared to the model setup with no estimated
correlation could be found for stations located in alpine valleys.
