The authors are grateful to Jeff Baars, Tom M. Hamill, Clifford F. Mass, Adrian E. Raftery, and J. McLean Sloughter for discussions and for providing data. Tom Hamill communicated to us the idea that underlies ensemble copula coupling (ECC), well before we noticed the independent discussions in the work of Bremnes (2007) and Krzysztofowicz and Toth (2008), and well before the term ECC was coined in Schefzik (2011).
Anderson, J. L., 1996: A method for producing and evaluating probabilistic forecasts from ensemble model integrations. J. Climate, 9, 1518–1530.
Baars, J., cited 2005: Observations QC documentation. [Available online at http://www.atmos.washington.edu/mm5rt/qc_obs/qc_doc.html.]
Bao, L., T. Gneiting, E. P. Grimit, P. Guttorp, and A. E. Raftery, 2010: Bias correction and Bayesian model averaging for ensemble forecasts of surface wind direction. Mon. Wea. Rev., 138, 1811–1821.
Bertsekas, D. P., 1999: Nonlinear Programming. 2nd ed. Athena Scientific, 780 pp.
Bremnes, J. B., 2007: Improved calibration of precipitation forecasts using ensemble techniques. Part 2: Statistical calibration methods. Norwegian Meteorological Institute, Tech. Rep. 04/2007, 38 pp. [Available online at http://met.no/Forskning/Publikasjoner/Publikasjoner_2007/filestore/report04_2007.pdf.]
Delle Monache, L., J. P. Hacker, Y. Zhou, X. Deng, and R. B. Stull, 2006: Probabilistic aspects of meteorological and ozone regional ensemble forecasts. J. Geophys. Res., 111, D24307, doi:10.1029/2005JD006917.
Eckel, F. A., and C. F. Mass, 2005: Aspects of effective mesoscale, short-range ensemble forecasting. Wea. Forecasting, 20, 328–350.
Gel, Y., A. E. Raftery, and T. Gneiting, 2004: Calibrated probabilistic mesoscale weather field forecasting: The geostatistical output perturbation (GOP) method (with discussion). J. Amer. Stat. Assoc., 99, 575–587.
Gneiting, T., and A. E. Raftery, 2007: Strictly proper scoring rules, prediction, and estimation. J. Amer. Stat. Assoc., 102, 359–378.
Gneiting, T., A. E. Raftery, A. H. Westveld, and T. Goldman, 2005: Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Mon. Wea. Rev., 133, 1098–1118.
Gneiting, T., F. Balabdaoui, and A. E. Raftery, 2007: Probabilistic forecasts, calibration and sharpness. J. Roy. Stat. Soc., 69B, 243–268.
Gneiting, T., L. I. Stanberry, E. P. Grimit, L. Held, and N. A. Johnson, 2008: Assessing probabilistic forecasts of multivariate quantities, with an application to ensemble predictions of surface winds. TEST, 17, 211–235.
Grimit, E., and C. Mass, 2002: Initial results of a mesoscale short-range ensemble forecasting system over the Pacific Northwest. Wea. Forecasting, 17, 192–205.
Hagedorn, R., T. M. Hamill, and J. S. Whitaker, 2008: Probabilistic forecast calibration using ECMWF and GFS ensemble reforecasts. Part I: Temperature. Mon. Wea. Rev., 136, 2608–2619.
Kann, A., C. Wittmann, Y. Wang, and X. Ma, 2009: Calibrating 2-m temperature of limited area ensemble forecasts using high-resolution analysis. Mon. Wea. Rev., 137, 3373–3387.
Kleiber, W., A. E. Raftery, J. Baars, T. Gneiting, C. F. Mass, and E. Grimit, 2011: Locally calibrated probabilistic temperature forecasting using geostatistical model averaging and local Bayesian model averaging. Mon. Wea. Rev., 139, 2630–2649.
Kolczynski, W. C., D. R. Stauffer, S. E. Haupt, N. S. Altman, and A. Deng, 2011: Investigation of ensemble variance as a measure of true forecast variance. Mon. Wea. Rev., 139, 3954–3963.
Krzysztofowicz, R., and Z. Toth, 2008: Bayesian processor of ensemble (BPE): Concept and implementation. Fourth NCEP/NWS Ensemble User Workshop, Laurel, MD, NCEP/NWS. [Available online at http://www.emc.ncep.noaa.gov/gmb/ens/ens2008/Krzysztofowicz_Presentation_Web.pdf.]
Marquis, M., J. Wilczak, M. Ahlstrom, J. Sharp, A. Stern, C. J. Smith, and S. Calvert, 2011: Forecasting the wind to reach significant penetration levels of wind energy. Bull. Amer. Meteor. Soc., 92, 1159–1171.
Marzban, C., R. Wang, F. Kong, and S. Leyton, 2011: On the effect of correlations on rank histograms: Reliability of temperature and wind speed forecasts from finescale ensemble reforecasts. Mon. Wea. Rev., 139, 295–310.
National Weather Service, 1998: Automated Surface Observing System (ASOS) user’s guide. NWS, 72 pp. [Available online at http://www.weather.gov/asos/aum-toc.pdf.]
Pinson, P., and R. Hagedorn, 2012: Verification of the ECMWF ensemble forecasts of wind speed against analyses and observations. Meteor. Appl., in press.
Raftery, A. E., T. Gneiting, F. Balabdaoui, and M. Polakowski, 2005: Using Bayesian model averaging to calibrate forecast ensembles. Mon. Wea. Rev., 133, 1155–1174.
R Development Core Team, cited 2011: R: A Language and Environment for Statistical Computing. Vienna, Austria, R Foundation for Statistical Computing. [Available online at http://www.R-project.org/.]
Schefzik, R., 2011: Ensemble copula coupling. M.S. thesis, Faculty of Mathematics and Informatics, University of Heidelberg, Heidelberg, Germany, 149 pp. [Available online at http://www.rzuser.uni-heidelberg.de/~gb4/files/Schefzik2011.pdf.]
Schölzel, C., and P. Friederichs, 2008: Multivariate non-normally distributed random variables in climate research – Introduction to copulas. Nonlinear Processes Geophys., 15, 761–772.
Schuhen, N., 2011: Ensemble model output statistics for wind vectors. M.S. thesis, Faculty of Mathematics and Informatics, University of Heidelberg, Heidelberg, Germany, 131 pp. [Available online at http://www.rzuser.uni-heidelberg.de/~gb4/files/Schuhen2011.pdf.]
Sloughter, J. M., 2009: Probabilistic weather forecasting using Bayesian model averaging. Ph.D. thesis, Department of Statistics, University of Washington, Seattle, WA, 81 pp.
Sloughter, J. M., T. Gneiting, and A. E. Raftery, 2010: Probabilistic wind speed forecasting using ensembles and Bayesian model averaging. J. Amer. Stat. Assoc., 105, 25–35.
Sloughter, J. M., T. Gneiting, and A. E. Raftery, 2012: Probabilistic wind vector forecasting using ensembles and Bayesian model averaging. Mon. Wea. Rev., in press.
Talagrand, O., R. Vautard, and B. Strauss, 1997: Evaluation of probabilistic prediction systems. Proc. Workshop on Predictability, Reading, United Kingdom, European Centre for Medium-Range Weather Forecasts, 1–25.
Thorarinsdottir, T. L., and T. Gneiting, 2010: Probabilistic forecasts of wind speed: Ensemble model output statistics by using heteroscedastic censored regression. J. Roy. Stat. Soc., 173A, 371–388.
Thorarinsdottir, T. L., and M. S. Johnson, 2012: Probabilistic wind gust forecasting using nonhomogeneous Gaussian regression. Mon. Wea. Rev., 140, 889–897.
Vardi, Y., and C.-H. Zhang, 2000: The multivariate LI-median and associated data depth. Proc. Natl. Acad. Sci. USA, 97, 1423–1426.
Whitaker, J. S., and A. F. Loughe, 1998: The relationship between ensemble spread and ensemble mean skill. Mon. Wea. Rev., 126, 3292–3302.
Wilks, D. S., 2009: Extending logistic regression to provide full-probability-distribution MOS forecasts. Meteor. Appl., 16, 361–368.
Wilks, D. S., 2011b: Statistical Methods in the Atmospheric Sciences. 3rd ed. Academic Press, 704 pp.