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

Meteorology and Atmospheric Physics volume 129, pages 99–112 (2017)Cite this article

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 an EMOS model for these weather quantities based on a bivariate truncated normal distribution. The bivariate EMOS model is applied to temperature and wind speed forecasts of the 8-member University of Washington mesoscale ensemble and the 11-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.

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Notes

  1. PEARP: Prévision d’Ensemble ARPege.

  2. COSMO: Consortium for Small scale Modeling.

  3. ALADIN: Aire Limitée Adaptation dynamique Development International.

  4. ARPEGE: Action de Recherche Petite Echelle Grande Echelle.

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Acknowledgments

An essential part of this work was made during the visit of Sándor Baran at the Heidelberg Institute for Theoretical Studies. The research stay in Heidelberg was funded by the DAAD program “Research Stays for University Academics and Scientists, 2015”. Sándor Baran was also supported by the János Bolyai Research Scholarship of the Hungarian Academy of Sciences. The authors are indebted to Tilmann Gneiting for his useful suggestions and remarks, the University of Washington MURI group for providing the UWME data, Mihály Szűcs from the HMS for the ALADIN-HUNEPS data and Thordis Thorarinsdottir and Alex Lenkoski for their help with the R codes for the copula method. Last but not least, the authors are very grateful to the unknown reviewer for his/her valuable comments and suggestions.

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Authors and Affiliations

  1. Faculty of Informatics, University of Debrecen, Kassai út 26, Debrecen, 4028, Hungary

    Sándor Baran

  2. Department of Animal Sciences, University of Göttingen, Göttingen, Germany

    Annette Möller

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  1. Sándor Baran
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  2. Annette Möller
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Correspondence to Sándor Baran.

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Baran, S., Möller, A. Bivariate ensemble model output statistics approach for joint forecasting of wind speed and temperature. Meteorol Atmos Phys 129, 99–112 (2017). https://doi.org/10.1007/s00703-016-0467-8

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Keywords

  • Wind Speed
  • Ensemble Member
  • Ensemble Forecast
  • Bayesian Model Average
  • Bayesian Model Average