Multivariate probabilistic forecasting using ensemble Bayesian model averaging and copulas
We propose a method for post-processing an ensemble of multivariate forecasts in order to obtain a joint predictive distribution of weather. Our method utilizes existing univariate post-processing techniques, in this case ensemble Bayesian model averaging (BMA), to obtain estimated marginal distribu...
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| Main Authors: | , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
2013
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| In: |
Quarterly journal of the Royal Meteorological Society
Year: 2012, Volume: 139, Issue: 673, Pages: 982-991 |
| ISSN: | 1477-870X |
| DOI: | https://doi.org/10.1002/qj.2009 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/https://doi.org/10.1002/qj.2009 Verlag, lizenzpflichtig, Volltext: https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.2009 
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| Author Notes: | Annette Möller, Alex Lenkoski and Thordis L. Thorarinsdottir |
MARC
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| 520 | |a We propose a method for post-processing an ensemble of multivariate forecasts in order to obtain a joint predictive distribution of weather. Our method utilizes existing univariate post-processing techniques, in this case ensemble Bayesian model averaging (BMA), to obtain estimated marginal distributions. However, implementing these methods individually offers no information regarding the joint distribution. To correct this, we propose the use of a Gaussian copula, which offers a simple procedure for recovering the dependence that is lost in the estimation of the ensemble BMA marginals. Our method is applied to 48 h forecasts of a set of five weather quantities using the eight-member University of Washington mesoscale ensemble. We show that our method recovers many well-understood dependencies between weather quantities and subsequently improves calibration and sharpness over both the raw ensemble and a method which does not incorporate joint distributional information. Copyright © 2012 Royal Meteorological Society | ||
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