  Ensembles of forecasts are typically employed to account for the forecast
uncertainties inherent in predictions of future weather states. However, biases
and dispersion errors often present in forecast ensembles require statistical
post-processing. Univariate post-processing models such as Bayesian model
averaging (BMA) have been successfully applied for various weather quantities.
Nonetheless, BMA and many other standard post-processing procedures are
designed for a single weather variable, thus ignoring possible dependencies
among weather quantities. In line with recently upcoming research to develop
multivariate post-processing procedures, e.g., BMA for bivariate wind vectors,
or flexible procedures applicable for multiple weather quantities of different
types, a bivariate BMA model for joint calibration of wind speed and
temperature forecasts is proposed based on the bivariate truncated normal
distribution. It extends the univariate truncated normal BMA model designed for
post-processing ensemble forecast of wind speed by adding a normally
distributed temperature component with a covariance structure representing the
dependency among the two weather quantities.
  The method is applied to wind speed and temperature forecasts of the
eight-member University of Washington mesoscale ensemble and of the
eleven-member ALADIN-HUNEPS ensemble of the Hungarian Meteorological Service
and its predictive performance is compared to that of the general Gaussian
copula method. The results indicate improved calibration of probabilistic and
accuracy of point forecasts in comparison to the raw ensemble and the overall
performance of this model is able to keep up with that of the Gaussian copula
method.
