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Volumn 139, Issue 673, 2013, Pages 982-991

Multivariate probabilistic forecasting using ensemble Bayesian model averaging and copulas

Author keywords

Copula methods; Ensemble post processing; Joint predictive distributions

Indexed keywords

BAYESIAN MODEL AVERAGING; COPULA METHODS; DISTRIBUTIONAL INFORMATION; ENSEMBLE POST-PROCESSING; POST-PROCESSING TECHNIQUES; PREDICTIVE DISTRIBUTIONS; PROBABILISTIC FORECASTING; UNIVERSITY OF WASHINGTON;

EID: 84879201434     PISSN: 00359009     EISSN: 1477870X     Source Type: Journal    
DOI: 10.1002/qj.2009     Document Type: Article
Times cited : (117)

References (53)
  • 1
    • 77953137410 scopus 로고    scopus 로고
    • Copula-based uncertainty modelling: application to multisensor precipitation estimates
    • AghaKouchak A, Bárdossy A, Habib E. 2010. Copula-based uncertainty modelling: application to multisensor precipitation estimates. Hydrol. Process. 24: 2111-2124.
    • (2010) Hydrol. Process. , vol.24 , pp. 2111-2124
    • AghaKouchak, A.1    Bárdossy, A.2    Habib, E.3
  • 2
    • 0030438938 scopus 로고    scopus 로고
    • A method for producing and evaluating probabilistic forecasts from ensemble model integrations
    • Anderson JL. 1996. A method for producing and evaluating probabilistic forecasts from ensemble model integrations. J. Climate 9: 1518-1530.
    • (1996) J. Climate , vol.9 , pp. 1518-1530
    • Anderson, J.L.1
  • 3
    • 84879232752 scopus 로고    scopus 로고
    • Observations QC documentation
    • Baars J. 2005. Observations QC documentation. http://www.atmos.washington.edu/mm5rt/qc_obs/qc_doc.html.
    • (2005)
    • Baars, J.1
  • 4
    • 77955580560 scopus 로고    scopus 로고
    • Bias correction and Bayesian model averaging for ensemble forecasts of surface wind direction
    • Bao L, Gneiting T, Grimit EP, Guttorp P, Raftery AE. 2010. Bias correction and Bayesian model averaging for ensemble forecasts of surface wind direction. Mon. Weather Rev. 138: 1811-1821.
    • (2010) Mon. Weather Rev. , vol.138 , pp. 1811-1821
    • Bao, L.1    Gneiting, T.2    Grimit, E.P.3    Guttorp, P.4    Raftery, A.E.5
  • 5
    • 34248349570 scopus 로고    scopus 로고
    • Combining spatial statistical and ensemble information in probabilistic weather forecasts
    • Berrocal VJ, Raftery AE, Gneiting T. 2007. Combining spatial statistical and ensemble information in probabilistic weather forecasts. Mon. Weather Rev. 135: 1386-1402.
    • (2007) Mon. Weather Rev. , vol.135 , pp. 1386-1402
    • Berrocal, V.J.1    Raftery, A.E.2    Gneiting, T.3
  • 6
    • 77950950767 scopus 로고    scopus 로고
    • Probabilistic quantitative precipitation field forecasting using a two-stage spatial model
    • Berrocal VJ, Raftery AE, Gneiting T. 2008. Probabilistic quantitative precipitation field forecasting using a two-stage spatial model. Ann. Appl. Statist. 2: 1170-1193.
    • (2008) Ann. Appl. Statist. , vol.2 , pp. 1170-1193
    • Berrocal, V.J.1    Raftery, A.E.2    Gneiting, T.3
  • 7
    • 79958715223 scopus 로고    scopus 로고
    • Probabilistic visibility forecasting using Bayesian model averaging
    • Chmielecki RM, Raftery AE. 2010. Probabilistic visibility forecasting using Bayesian model averaging. Mon. Weather Rev. 139: 1626-1636.
    • (2010) Mon. Weather Rev. , vol.139 , pp. 1626-1636
    • Chmielecki, R.M.1    Raftery, A.E.2
  • 8
    • 1642284456 scopus 로고    scopus 로고
    • The Schaake Shuffle: a method for reconstructing space-time variability in forecasted precipitation and temperature fields
    • Clark MP, Gangopadhyay S, Hay LE, Rajagopalan B, Wilby RL. 2004. The Schaake Shuffle: a method for reconstructing space-time variability in forecasted precipitation and temperature fields. J. Hydrometeorol. 5: 243-262.
    • (2004) J. Hydrometeorol. , vol.5 , pp. 243-262
    • Clark, M.P.1    Gangopadhyay, S.2    Hay, L.E.3    Rajagopalan, B.4    Wilby, R.L.5
  • 9
    • 34547904994 scopus 로고    scopus 로고
    • Probabilistic aspects of meteorological and ozone regional ensemble forecasts
    • DOI: 10.1029/2005JD006917
    • Delle Monache L, Hacker JP, Zhou Y, Deng X, Stull RB. 2006. Probabilistic aspects of meteorological and ozone regional ensemble forecasts. J. Geophys. Res. 111: D24307, DOI: 10.1029/2005JD006917.
    • (2006) J. Geophys. Res. , vol.111
    • Delle Monache, L.1    Hacker, J.P.2    Zhou, Y.3    Deng, X.4    Stull, R.B.5
  • 10
    • 23144455758 scopus 로고    scopus 로고
    • Aspects of effective mesoscale, short-range ensemble forecasting
    • Eckel FA, Mass CF. 2005. Aspects of effective mesoscale, short-range ensemble forecasting. Weather Forecast. 20: 328-350.
    • (2005) Weather Forecast. , vol.20 , pp. 328-350
    • Eckel, F.A.1    Mass, C.F.2
  • 11
    • 33746239466 scopus 로고    scopus 로고
    • Probabilistic forecasting from ensemble prediction systems: improving upon the best-member method by using a different weight and dressing kernel for each member
    • Fortin V, Favre AC, Saïd M. 2006. Probabilistic forecasting from ensemble prediction systems: improving upon the best-member method by using a different weight and dressing kernel for each member. Q. J. R. Meteorol. Soc. 132: 1349-1369.
    • (2006) Q. J. R. Meteorol. Soc. , vol.132 , pp. 1349-1369
    • Fortin, V.1    Favre, A.C.2    Saïd, M.3
  • 13
    • 34347377353 scopus 로고    scopus 로고
    • Everything you always wanted to know about copula modeling but where afraid to ask
    • Genest C, Favre AE. 2007. Everything you always wanted to know about copula modeling but where afraid to ask. J. Hydrol. Eng. 12: 347-368.
    • (2007) J. Hydrol. Eng. , vol.12 , pp. 347-368
    • Genest, C.1    Favre, A.E.2
  • 14
    • 26844438590 scopus 로고    scopus 로고
    • Weather forecasting with ensemble methods
    • Gneiting T, Raftery AE. 2005. Weather forecasting with ensemble methods. Science 310: 248-249.
    • (2005) Science , vol.310 , pp. 248-249
    • Gneiting, T.1    Raftery, A.E.2
  • 15
    • 33947274775 scopus 로고    scopus 로고
    • Strictly proper scoring rules, prediction, and estimation
    • 2
    • Gneiting T, Raftery AE. 2007. Strictly proper scoring rules, prediction, and estimation. J. Am. Statist. Assoc. 10: 2: 359-378.
    • (2007) J. Am. Statist. Assoc. , vol.10 , pp. 359-378
    • Gneiting, T.1    Raftery, A.E.2
  • 16
    • 20444484849 scopus 로고    scopus 로고
    • Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation
    • Gneiting T, Raftery AE, Westveld AH, Goldman T. 2005. Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Mon. Weather Rev. 133: 1098-1118.
    • (2005) Mon. Weather Rev. , vol.133 , pp. 1098-1118
    • Gneiting, T.1    Raftery, A.E.2    Westveld, A.H.3    Goldman, T.4
  • 17
    • 48349105056 scopus 로고    scopus 로고
    • Assessing probabilistic forecasts of multivariate quantities, with applications to ensemble predictions of surface winds (with discussion and rejoinder)
    • Gneiting T, Stanberry LI, Grimit EP, Held L, Johnson NA. 2008. Assessing probabilistic forecasts of multivariate quantities, with applications to ensemble predictions of surface winds (with discussion and rejoinder). Test 17: 211-264.
    • (2008) Test , vol.17 , pp. 211-264
    • Gneiting, T.1    Stanberry, L.I.2    Grimit, E.P.3    Held, L.4    Johnson, N.A.5
  • 19
    • 84861657566 scopus 로고    scopus 로고
    • Copula-based modeling of stochastic wind power in Europe and implications for the Swiss power grid
    • Hagspiel S, Papaemannouil A, Schmid M, Andersson G. 2011. Copula-based modeling of stochastic wind power in Europe and implications for the Swiss power grid. Appl. Energy 96: 33-44.
    • (2011) Appl. Energy , vol.96 , pp. 33-44
    • Hagspiel, S.1    Papaemannouil, A.2    Schmid, M.3    Andersson, G.4
  • 20
    • 0002897679 scopus 로고    scopus 로고
    • Verification of Eta-RSM short-range ensemble forecasts
    • Hamill TM, Colucci SJ. 1997. Verification of Eta-RSM short-range ensemble forecasts. Mon. Weather Rev. 125: 1312-1327.
    • (1997) Mon. Weather Rev. , vol.125 , pp. 1312-1327
    • Hamill, T.M.1    Colucci, S.J.2
  • 21
    • 0034292468 scopus 로고    scopus 로고
    • Decomposition of the continuous ranked probability score for ensemble prediction systems
    • Hersbach H. 2000. Decomposition of the continuous ranked probability score for ensemble prediction systems. Weather Forecast. 15: 559-570.
    • (2000) Weather Forecast. , vol.15 , pp. 559-570
    • Hersbach, H.1
  • 22
    • 60949106826 scopus 로고    scopus 로고
    • Extending the rank likelihood for semiparametric copula estimation
    • Hoff PD. 2007. Extending the rank likelihood for semiparametric copula estimation. Ann. Appl. Statist. 1: 265-283.
    • (2007) Ann. Appl. Statist. , vol.1 , pp. 265-283
    • Hoff, P.D.1
  • 23
    • 84879238818 scopus 로고    scopus 로고
    • Information bounds for Gaussian copulas
    • Hoff PD, Niu X, Wellner JA. 2011. Information bounds for Gaussian copulas. http://arxiv.org/abs/1110.3572.
    • (2011)
    • Hoff, P.D.1    Niu, X.2    Wellner, J.A.3
  • 24
    • 71849084400 scopus 로고    scopus 로고
    • A copula-based joint deficit index for droughts
    • Kao SC, Govindaraju RS. 2010. A copula-based joint deficit index for droughts. J. Hydrol. 380: 121-134.
    • (2010) J. Hydrol. , vol.380 , pp. 121-134
    • Kao, S.C.1    Govindaraju, R.S.2
  • 25
    • 0032853041 scopus 로고    scopus 로고
    • Bayesian theory of probabilistic forecasting via deterministic hydrologic model
    • Krzysztofowicz R. 1999. Bayesian theory of probabilistic forecasting via deterministic hydrologic model. Water Resour. Res. 35: 2739-2750.
    • (1999) Water Resour. Res. , vol.35 , pp. 2739-2750
    • Krzysztofowicz, R.1
  • 26
    • 0035425631 scopus 로고    scopus 로고
    • Hyrdrologic uncertainty processor for probabilistic river stage forecasting: precipitation-dependent model
    • Krzysztofowicz R, Herr HD. 2001. Hyrdrologic uncertainty processor for probabilistic river stage forecasting: precipitation-dependent model. J. Hydrol. 249: 46-68.
    • (2001) J. Hydrol. , vol.249 , pp. 46-68
    • Krzysztofowicz, R.1    Herr, H.D.2
  • 27
    • 0033735880 scopus 로고    scopus 로고
    • Hydrologic uncertainty processor for probabilistic river stage forecasting
    • Krzysztofowicz R, Kelly KS. 2000. Hydrologic uncertainty processor for probabilistic river stage forecasting. Water Resour. Res. 36: 3265-3277.
    • (2000) Water Resour. Res. , vol.36 , pp. 3265-3277
    • Krzysztofowicz, R.1    Kelly, K.S.2
  • 30
    • 79961050814 scopus 로고    scopus 로고
    • An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach
    • Lindgren F, Rue H, Lindström J. 2011. An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach. J. R. Statist. Soc. B 73: 423-498.
    • (2011) J. R. Statist. Soc. B , vol.73 , pp. 423-498
    • Lindgren, F.1    Rue, H.2    Lindström, J.3
  • 31
    • 0016961866 scopus 로고
    • Scoring rules for continuous probability distributions
    • Matheson JE, Winkler RL. 1976. Scoring rules for continuous probability distributions. Manage. Sci. 22: 1087-1096.
    • (1976) Manage. Sci. , vol.22 , pp. 1087-1096
    • Matheson, J.E.1    Winkler, R.L.2
  • 32
    • 33751542466 scopus 로고    scopus 로고
    • Copulas: tales and facts
    • Mikosch T. 2006. Copulas: tales and facts. Extremes 9: 3-20.
    • (2006) Extremes , vol.9 , pp. 3-20
    • Mikosch, T.1
  • 33
    • 84879240926 scopus 로고    scopus 로고
    • National Weather Service. Automated Surface Observing System (ASOS) User's Guide
    • National Weather Service. 1998. Automated Surface Observing System (ASOS) User's Guide. http://www.weather.gov/asos/aum-toc.pdf.
    • (1998)
  • 35
    • 84864281578 scopus 로고    scopus 로고
    • Adaptive calibration of (u,v)-wind ensemble forecasts
    • DOI: 10.1002/qj.1873.
    • Pinson P. 2012. Adaptive calibration of (u, v)-wind ensemble forecasts. Q. J. R. Meteorol. Soc. 138: 1273-1284, DOI: 10.1002/qj.1873.
    • (2012) Q. J. R. Meteorol. Soc. , vol.138 , pp. 1273-1284
    • Pinson, P.1
  • 36
    • 84861664310 scopus 로고    scopus 로고
    • Evaluating the quality of scenarios of short-term wind power generation
    • Pinson P, Girard R. 2012. Evaluating the quality of scenarios of short-term wind power generation. Appl. Energy 96: 12-20.
    • (2012) Appl. Energy , vol.96 , pp. 12-20
    • Pinson, P.1    Girard, R.2
  • 37
    • 74149093707 scopus 로고    scopus 로고
    • From probabilistic forecasts to statistical scenarios of short-term wind power production
    • Pinson P, Papaefthymiou G, Klockl B, Nielsen HA, Madsen H. 2009. From probabilistic forecasts to statistical scenarios of short-term wind power production. Wind Energy 12: 51-62.
    • (2009) Wind Energy , vol.12 , pp. 51-62
    • Pinson, P.1    Papaefthymiou, G.2    Klockl, B.3    Nielsen, H.A.4    Madsen, H.5
  • 38
    • 84879248616 scopus 로고    scopus 로고
    • R Development Core Team. R: A language and environment for statistical computing
    • R Development Core Team. 2011. R: A language and environment for statistical computing. http://www.R-project.org.
    • (2011)
  • 40
    • 65249103483 scopus 로고    scopus 로고
    • A local ensemble prediction system for fog and low clouds: construction, Bayesian model averaging calibration, and validation
    • Roquelaure S, Bergot T. 2008. A local ensemble prediction system for fog and low clouds: construction, Bayesian model averaging calibration, and validation. J. Appl. Meteorol. Climatol. 47: 3072-3088.
    • (2008) J. Appl. Meteorol. Climatol. , vol.47 , pp. 3072-3088
    • Roquelaure, S.1    Bergot, T.2
  • 41
    • 84879224551 scopus 로고    scopus 로고
    • Ensemble copula coupling. Diploma thesis, Faculty of Mathematics and Informatics, University of Heidelberg.
    • Schefzik R. 2011. Ensemble copula coupling. Diploma thesis, Faculty of Mathematics and Informatics, University of Heidelberg.
    • (2011)
    • Schefzik, R.1
  • 42
    • 54949105089 scopus 로고    scopus 로고
    • Multivariate non-normally distributed random variables in climate research-introduction to the copula approach
    • Schölzel C, Friederichs P. 2008. Multivariate non-normally distributed random variables in climate research-introduction to the copula approach. Nonlinear Proc. Geophys. 15: 761-772.
    • (2008) Nonlinear Proc. Geophys. , vol.15 , pp. 761-772
    • Schölzel, C.1    Friederichs, P.2
  • 43
    • 84879231208 scopus 로고    scopus 로고
    • Ensemble model output statistics for wind vectors. ArXiv:1201.2612.
    • Schuhen N, Thorarinsdottir TL, Gneiting T. 2012. Ensemble model output statistics for wind vectors. ArXiv:1201.2612.
    • (2012)
    • Schuhen, N.1    Thorarinsdottir, T.L.2    Gneiting, T.3
  • 44
    • 34248354794 scopus 로고    scopus 로고
    • Probabilistic quantitative precipitation forecasting using Bayesian model averaging
    • Sloughter JM, Raftery AE, Gneiting T, Fraley C. 2007. Probabilistic quantitative precipitation forecasting using Bayesian model averaging. Mon. Weather Rev. 135: 3209-3220.
    • (2007) Mon. Weather Rev. , vol.135 , pp. 3209-3220
    • Sloughter, J.M.1    Raftery, A.E.2    Gneiting, T.3    Fraley, C.4
  • 45
    • 77952562085 scopus 로고    scopus 로고
    • Probabilistic wind speed forecasting using ensembles and Bayesian model averaging
    • Sloughter JM, Gneiting T, Raftery AE. 2010. Probabilistic wind speed forecasting using ensembles and Bayesian model averaging. J. Am. Statist. Assoc. 105: 25-35.
    • (2010) J. Am. Statist. Assoc. , vol.105 , pp. 25-35
    • Sloughter, J.M.1    Gneiting, T.2    Raftery, A.E.3
  • 46
    • 84879223461 scopus 로고    scopus 로고
    • Probabilistic wind vector forecasting using ensembles and Bayesian model averaging
    • submitted).
    • Sloughter JM, Gneiting T, Raftery AE. 2011. Probabilistic wind vector forecasting using ensembles and Bayesian model averaging. Mon. Weather Rev. (submitted).
    • (2011) Mon. Weather Rev.
    • Sloughter, J.M.1    Gneiting, T.2    Raftery, A.E.3
  • 47
    • 84879238159 scopus 로고    scopus 로고
    • Evaluation of probabilistic prediction systems. In Proceedings of the Workshop on Predictability, European Centre for Medium-Range Weather Forecasts, Reading, UK
    • Talagrand O, Vautard R, Strauss B. 1997. Evaluation of probabilistic prediction systems. In Proceedings of the Workshop on Predictability, European Centre for Medium-Range Weather Forecasts, Reading, UK; 1-25.
    • (1997) , pp. 1-25
    • Talagrand, O.1    Vautard, R.2    Strauss, B.3
  • 48
    • 77950945178 scopus 로고    scopus 로고
    • Probabilistic forecasts of wind speed: ensemble model output statistics using heteroskedastic censored regression
    • Thorarinsdottir TL, Gneiting T. 2010. Probabilistic forecasts of wind speed: ensemble model output statistics using heteroskedastic censored regression. J. R. Statist. Soc. A 173: 371-388.
    • (2010) J. R. Statist. Soc. A , vol.173 , pp. 371-388
    • Thorarinsdottir, T.L.1    Gneiting, T.2
  • 49
    • 84863418569 scopus 로고    scopus 로고
    • Probabilistic wind gust forecasting using non-homogeneous Gaussian regression
    • Thorarinsdottir TL, Johnson MS. 2011. Probabilistic wind gust forecasting using non-homogeneous Gaussian regression. Mon. Weather Rev. 140: 889-897.
    • (2011) Mon. Weather Rev. , vol.140 , pp. 889-897
    • Thorarinsdottir, T.L.1    Johnson, M.S.2
  • 50
    • 0034652134 scopus 로고    scopus 로고
    • The multivariate L1-median and associated data depth
    • Vardi Y, Zhang CH. 2000. The multivariate L1-median and associated data depth. Proc. Natl Acad. Sci. USA 97: 1423-1426.
    • (2000) Proc. Natl Acad. Sci. USA , vol.97 , pp. 1423-1426
    • Vardi, Y.1    Zhang, C.H.2
  • 51
    • 0036821009 scopus 로고    scopus 로고
    • Smoothing forecast ensembles with fitted probability distributions
    • Wilks DS. 2002. Smoothing forecast ensembles with fitted probability distributions. Q. J. R. Meteorol. Soc. 128: 2821-2836.
    • (2002) Q. J. R. Meteorol. Soc. , vol.128 , pp. 2821-2836
    • Wilks, D.S.1
  • 52
    • 34447309549 scopus 로고    scopus 로고
    • Comparison of ensemble-MOS methods using GFS reforecasts
    • Wilks DS, Hamill TM. 2007. Comparison of ensemble-MOS methods using GFS reforecasts. Mon. Weather Rev. 135: 2379-2390.
    • (2007) Mon. Weather Rev. , vol.135 , pp. 2379-2390
    • Wilks, D.S.1    Hamill, T.M.2
  • 53
    • 34248352225 scopus 로고    scopus 로고
    • Calibrated surface temperature forecasts from the Canadian ensemble prediction system using Bayesian model averaging
    • Wilson LJ, Beauregard S, Raftery AE, Verret R. 2007. Calibrated surface temperature forecasts from the Canadian ensemble prediction system using Bayesian model averaging. Mon. Weather Rev. 135: 1364-1385.
    • (2007) Mon. Weather Rev. , vol.135 , pp. 1364-1385
    • Wilson, L.J.1    Beauregard, S.2    Raftery, A.E.3    Verret, R.4


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