Ensemble regression

Monthly Weather Review

Volumn 137, Issue 7, 2009, Pages 2365-2379

  Unger, David A ^a   van den Dool, Huug ^a   O'Lenic, Edward ^a   Collins, Dan ^a  

^a CLIMATE PREDICTION CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BEST FIT; CLIMATE FORECASTS; CONTINUOUS RANKED PROBABILITY SCORES; ENSEMBLE FORECASTS; ENSEMBLE MEMBERS; ENSEMBLE PREDICTION; ERROR DISTRIBUTIONS; ERROR VARIANCE; NATIONAL CENTERS FOR ENVIRONMENTAL PREDICTIONS; REGRESSION EQUATION; REGRESSION MODEL; REGRESSION RELATIONSHIP;

FORECASTING; NICKEL COMPOUNDS; PROBABILITY DENSITY FUNCTION; PROBABILITY DISTRIBUTIONS;

REGRESSION ANALYSIS;

CLIMATE PREDICTION; ENSEMBLE FORECASTING; NUMERICAL MODEL; PROBABILITY DENSITY FUNCTION; REGRESSION ANALYSIS; WEATHER FORECASTING;

EID: 70350302248     PISSN: 00270644     EISSN: None     Source Type: Journal    
DOI: 10.1175/2008MWR2605.1     Document Type: Article

References (48)

1. Anderson, J. L., 1996: A method for producing and evaluating probabilistic forecasts from ensemble model integrations. J. Climate, 9, 1518-1530.
2. Barnett, T. P., 1995: Monte Carlo climate forecasting. J. Climate, 8, 1005-1022.
3. Barnston, A. G., and Coauthors, 1994: Long lead seasonal forecasts - Where do we stand? Bull. Amer. Meteor. Soc., 75, 2097-2114.
4. Barnston, A. G., M. Chelliah, and S. B. Goldberg, 1997: Documentation of a highly ENSO-related SST region in the equatorial Pacific. Atmos.-Ocean, 35, 367-383.
5. Barnston, A. G., Y. He, and D. A. Unger, 2000: A forecast product that maximizes utility for state-of-the-art seasonal climate prediction. Bull. Amer. Meteor. Soc., 81, 1271-1279.
6. Benzi, R., and A. Speranza, 1989: Statistical properties of low frequency variability in the Northern Hemisphere. J. Climate, 2, 367-379.
7. Draper, N., and H. Smith, 1981: Applied Regression Analysis. John Wiley and Sons, 709 pp.
8. Eckel, F. A., and M. K. Walters, 1998: Calibrated probabilistic quantitative precipitation forecasts based on the MRF ensemble. Wea. Forecasting, 13, 1132-1147.
9. Epstein, E., 1969a: Stochastic dynamic prediction. Tellus, 21, 739-759.
10. Epstein, E., 1969b: A scoring system for probability forecasts of ranked categories. J. Appl. Meteor., 8, 985-987.
11. Fortin, V., A. C. Favre, and M. Said, 2006: Probabilistic forecasting from ensemble prediction systems: Improving on the best member method by using a different weight and dressing kernel for each member. Quart. J. Roy. Meteor. Soc., 132, 1349-1369.
12. Glahn, B., and Coauthors, 2009: MOS uncertainty estimates in an ensemble framework. Mon. Wea. Rev., 137, 246-268.
13. Glahn, H. R., and D. Lowry, 1972: The use of model output statistics (MOS) in objective weather forecasts. J. Appl. Meteor., 11, 1203-1211.
14. Gneiting, T., A. Raftery, A. H. Westveld III, and T. Goldman, 2005: Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Mon. Wea. Rev., 133, 1098-1118.
15. Hamill, T. M., 2000: Interpretation of rank histograms for verifying ensemble forecasts. Mon. Wea. Rev., 129, 550-560.
16. Hamill, T. M., and S. J. Colucci, 1997: Verification of Eta-RSM short-range ensemble forecasts. Mon. Wea. Rev., 125, 1312-1327.
17. Hamill, T. M., and S. J. Colucci, 1998: Evaluation of Eta-RSM ensemble probabilistic precipitation forecasts. Mon. Wea. Rev., 126, 711-724.
18. Hamill, T. M., and J. S. Whitaker, 2006: Quantitative precipitation forecasts based on reforecast analogs: Theory and applications. Mon. Wea. Rev., 134, 3209-3229.
19. Hamill, T. M., J. S. Whitaker, and X. Wei, 2004: Ensemble reforecasting: Improving medium-range forecasting using retrospective forecasts. Mon. Wea. Rev., 132, 1434-1447.
20. Hersbach, H., 2000: Decomposition of the continuous ranked probability score for ensemble prediction systems. Wea. Forecasting, 15, 559-570.
21. Hoffman, R. N., and E. Kalnay, 1983: Lagged average forecasting, an alternative to Monte-Carlo forecasting. Tellus, 35A, 100-118.
22. Klein, W. H., F. Lewis, and I. Enger, 1959: Objective prediction of 5-day mean temperature during winter. J. Meteor., 16, 672-682.
23. Kumar, A., and M. P. Hoerling, 1995: Prospects and limitations of seasonal atmospheric GCM predictions. Bull. Amer. Meteor. Soc., 76, 335-345.
24. Leith, C. E., and Coauthors, 1974: Theoretical skill of Monte Carlo forecasts. Mon. Wea. Rev., 102, 409-418.
25. Livezey, R. E., 1990: Variability of skill of long range forecasts and implications for their use and value. Bull. Amer. Meteor. Soc., 71, 300-309.
26. Livezey, R. E., and M. M. Timofeyeva, 2008: The first decade of long-lead U.S. seasonal forecasts: Insights from a skill analysis. Bull. Amer. Meteor. Soc., 89, 843-855.
27. Matheson, J. E., and R. L. Winkler, 1976: Scoring rules for continuous probability distributions. Manage. Sci., 22, 1087-1096.
28. Michaelsen, J., 1987: Cross-validation in statistical climate forecast models. J. Climate Appl. Meteor., 26, 1589-1600.
29. Murphy, A. H., 1970: The ranked probability score and the probability score: A comparison. Mon. Wea. Rev., 98, 917-924.
30. O'Lenic, E. A., 2008: Developments in operational long-range climate prediction at CPC. Wea. Forecasting, 23, 496-515.
31. Phelps, M. W., A. Kumar, and J. J. O'Brien, 2004: Potential predictability in the NCEP CPC seasonal forecast system. J. Climate, 17, 3775-3785.
32. Raftery, A. E., and Coauthors, 2005: Using Bayesian model averaging to calibrate forecast model ensembles. Mon. Wea. Rev., 133, 1155-1174.
33. Roads, J. O., 1988: Lagged averaged predictions in a predictability experiment. J. Atmos. Sci., 45, 147-162.
34. Roulston, M. S., and L. A. Smith, 2003: Combining dynamic and statistical ensembles. Tellus, 55A, 16-30.
35. Rowell, D. P., 1998: Assessing seasonal predictability with an ensemble of multidecadal GCM simulations. J. Climate, 11, 109-120.
36. Saha, S., and Coauthors, 2006: The NCEP Climate Forecast System. J. Climate, 19, 3483-3517.
37. Silverman, B. W., 1986: Density Estimation for Statistics and Data Analysis. Chapman and Hall, 175 pp.
38. Sivillo, J. K., J. E. Ahlquist, and Z. Toth, 1997: An ensemble forecasting primer. Wea. Forecasting, 12, 809-818.
39. Stern, W., and K. Miyakoda, 1995: Feasibility of seasonal forecasts inferred from multiple GCM simulations. J. Climate, 8, 1071-1085.
40. Talagrand, O., R. Vauland, and B. Strauss, 1997: Evaluation of probabilistic predictions systems. Proc. Workshop on Predictions, Reading, United Kingdom, ECMWF, 1-25. [Available from ECMWF, Shinfield Park, Reading, Berkshire RG29AX, United Kingdom.]
41. Thiébaux, H. J., and F. W. Zwiers, 1984: The interpretation and estimation of effective sample size. J. Climate Appl. Meteor., 23, 800-811.
42. Toth, Z., and E. Kalnay, 1993: Ensemble forecasting at NMC: The generation of perturbations. Bull. Amer. Meteor. Soc., 74, 2317-2330.
43. van den Dool, H. M., 1994: Searching for analogues, how long must we wait? Tellus, 46A, 314-324.
44. Vialard, J., and Coauthors, 2005: An ensemble generation technique for seasonal forecasting with an ocean-atmosphere coupled model. Mon. Wea. Rev., 133, 441-453.
45. Wang, X., and C. H. Bishop, 2005: Improvements of ensemble reliability using a new dressing kernel. Quart. J. Roy. Meteor. Soc., 131, 965-986.
46. Wilks, D. S., 2006: Comparison of ensemble MOS methods in the Lorenz 96 setting. Meteor. Appl., 13, 243-256.
47. Wilks, D. S., and T. M. Hamill, 2007: Comparison of MOS-ensemble methods using GFS reforecasts. Mon. Wea. Rev., 135, 2379-2390.
48. Wilson, L. J., and Coauthors, 2007: Calibrated surface temperature forecasts fromthe Canadian Ensemble Prediction System using Bayesian model averaging. Mon. Wea. Rev., 135, 1365-1385.