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

TL;DR

This paper introduces a bivariate EMOS model for joint forecasting of wind speed and temperature, demonstrating comparable predictive performance to existing methods but with significantly reduced computational time.

Contribution

The paper develops a novel bivariate EMOS model based on a truncated normal distribution for joint weather variable forecasting, offering a computationally efficient alternative to bivariate BMA.

Findings

Bivariate EMOS achieves similar predictive accuracy as bivariate BMA.

The proposed model requires less computation time than bivariate BMA.

Performance is comparable to a multivariate Gaussian copula approach.

Abstract

Forecast ensembles are typically employed to account for prediction uncertainties in numerical weather prediction models. However, ensembles often exhibit biases and dispersion errors, thus they require statistical post-processing to improve their predictive performance. Two popular univariate post-processing models are the Bayesian model averaging (BMA) and the ensemble model output statistics (EMOS). In the last few years increased interest has emerged in developing multivariate post-processing models, incorporating dependencies between weather quantities, such as for example a bivariate distribution for wind vectors or even a more general setting allowing to combine any types of weather variables. In line with a recently proposed approach to model temperature and wind speed jointly by a bivariate BMA model, this paper introduces a bivariate EMOS model for these weather quantities based on a truncated normal distribution. The bivariate EMOS model is applied to temperature and wind speed 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 the performance of the bivariate BMA model and a multivariate Gaussian copula approach, post-processing the margins with univariate EMOS. While the predictive skills of the compared methods are similar, the bivariate EMOS model requires considerably lower computation times than the bivariate BMA method.