  We propose a method for post-processing an ensemble of multivariate forecasts
in order to obtain a joint predictive distribution of weather. Our method
utilizes existing univariate post-processing techniques, in this case ensemble
Bayesian model averaging (BMA), to obtain estimated marginal distributions.
However, implementing these methods individually offers no information
regarding the joint distribution. To correct this, we propose the use of a
Gaussian copula, which offers a simple procedure for recovering the dependence
that is lost in the estimation of the ensemble BMA marginals. Our method is
applied to 48-h forecasts of a set of five weather quantities using the
8-member University of Washington mesoscale ensemble. We show that our method
recovers many well-understood dependencies between weather quantities and
subsequently improves calibration and sharpness over both the raw ensemble and
a method which does not incorporate joint distributional information.
