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Forecast ensembles are typically employed to account for predictionuncertainties in numerical weather prediction models. However, ensembles oftenexhibit biases and dispersion errors, thus they require statisticalpost-processing to improve their predictive performance. Two popular univariatepost-processing models are the Bayesian model averaging (BMA) and the ensemblemodel output statistics (EMOS).  In the last few years increased interest has emerged in developingmultivariate post-processing models, incorporating dependencies between weatherquantities, such as for example a bivariate distribution for wind vectors oreven a more general setting allowing to combine any types of weather variables.  In line with a recently proposed approach to model temperature and wind speedjointly by a bivariate BMA model, this paper introduces a bivariate EMOS modelfor these weather quantities based on a truncated normal distribution.  The bivariate EMOS model is applied to temperature and wind speed forecastsof the eight-member University of Washington mesoscale ensemble and of theeleven-member ALADIN-HUNEPS ensemble of the Hungarian Meteorological Serviceand its predictive performance is compared to the performance of the bivariateBMA model and a multivariate Gaussian copula approach, post-processing themargins with univariate EMOS. While the predictive skills of the comparedmethods are similar, the bivariate EMOS model requires considerably lowercomputation times than the bivariate BMA method.

Bivariate ensemble model output statistics approach for joint forecasting of wind speed and temperature