Bivariate ensemble model output statistics approach for joint forecasting of wind speed and temperature
Baran S, Möller AC (2017)
Meteorology and Atmospheric Physics 129(1): 99-112.
Zeitschriftenaufsatz
| Veröffentlicht | Englisch
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Autor*in
Baran, Sándor;
Möller, Annette ChristineUniBi ![](https://pub.uni-bielefeld.de/images/icon_orcid.png)
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Abstract / Bemerkung
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.
Erscheinungsjahr
2017
Zeitschriftentitel
Meteorology and Atmospheric Physics
Band
129
Ausgabe
1
Seite(n)
99-112
ISSN
0177-7971
eISSN
1436-5065
Page URI
https://pub.uni-bielefeld.de/record/2960066
Zitieren
Baran S, Möller AC. Bivariate ensemble model output statistics approach for joint forecasting of wind speed and temperature. Meteorology and Atmospheric Physics. 2017;129(1):99-112.
Baran, S., & Möller, A. C. (2017). Bivariate ensemble model output statistics approach for joint forecasting of wind speed and temperature. Meteorology and Atmospheric Physics, 129(1), 99-112. https://doi.org/10.1007/s00703-016-0467-8
Baran, Sándor, and Möller, Annette Christine. 2017. “Bivariate ensemble model output statistics approach for joint forecasting of wind speed and temperature”. Meteorology and Atmospheric Physics 129 (1): 99-112.
Baran, S., and Möller, A. C. (2017). Bivariate ensemble model output statistics approach for joint forecasting of wind speed and temperature. Meteorology and Atmospheric Physics 129, 99-112.
Baran, S., & Möller, A.C., 2017. Bivariate ensemble model output statistics approach for joint forecasting of wind speed and temperature. Meteorology and Atmospheric Physics, 129(1), p 99-112.
S. Baran and A.C. Möller, “Bivariate ensemble model output statistics approach for joint forecasting of wind speed and temperature”, Meteorology and Atmospheric Physics, vol. 129, 2017, pp. 99-112.
Baran, S., Möller, A.C.: Bivariate ensemble model output statistics approach for joint forecasting of wind speed and temperature. Meteorology and Atmospheric Physics. 129, 99-112 (2017).
Baran, Sándor, and Möller, Annette Christine. “Bivariate ensemble model output statistics approach for joint forecasting of wind speed and temperature”. Meteorology and Atmospheric Physics 129.1 (2017): 99-112.
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