Advances in Atmospheric Sciences

, Volume 30, Issue 5, pp 1287–1302 | Cite as

Effect of doubling the ensemble size on the performance of ensemble prediction in the warm season using MOGREPS implemented at the KMA

  • Jun Kyung Kay
  • Hyun Mee KimEmail author
  • Young-Youn Park
  • Joohyung Son


Using the Met Office Global and Regional Ensemble Prediction System (MOGREPS) implemented at the Korea Meteorological Administration (KMA), the effect of doubling the ensemble size on the performance of ensemble prediction in the warm season was evaluated. Because a finite ensemble size causes sampling error in the full forecast probability distribution function (PDF), ensemble size is closely related to the efficiency of the ensemble prediction system. Prediction capability according to doubling the ensemble size was evaluated by increasing the number of ensembles from 24 to 48 in MOGREPS implemented at the KMA. The initial analysis perturbations generated by the Ensemble Transform Kalman Filter (ETKF) were integrated for 10 days from 22 May to 23 June 2009. Several statistical verification scores were used to measure the accuracy, reliability, and resolution of ensemble probabilistic forecasts for 24 and 48 ensemble member forecasts. Even though the results were not significant, the accuracy of ensemble prediction improved slightly as ensemble size increased, especially for longer forecast times in the Northern Hemisphere. While increasing the number of ensemble members resulted in a slight improvement in resolution as forecast time increased, inconsistent results were obtained for the scores assessing the reliability of ensemble prediction. The overall performance of ensemble prediction in terms of accuracy, resolution, and reliability increased slightly with ensemble size, especially for longer forecast times.

Key words

ensemble prediction ensemble size ensemble transform Kalman filter 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Anderson, J. L., 1996: A method for producing and evaluating probabilistic forecasts from ensemble model integrations. J. Climate, 9, 1518–1530.CrossRefGoogle Scholar
  2. Anderson, J. L., 2001: An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev., 129, 2884–2903.CrossRefGoogle Scholar
  3. Arribas, A., K. B. Robertson, and K. R. Mylne, 2005: Test of a poor man’s ensemble prediction system for short-range probability forecasting. Mon. Wea. Rev., 133, 1825–1839.CrossRefGoogle Scholar
  4. Atger, F., 1999: The skill of ensemble prediction systems. Mon. Wea. Rev., 127, 1941–1953.CrossRefGoogle Scholar
  5. Atger, F., 2004: Estimation of the reliability of ensemble based probabilistic forecasts. Quart. J. Roy. Meteor. Soc., 130, 627–646.CrossRefGoogle Scholar
  6. Bowler, N. E., A. Arribas, K. R. Mylne., K. B. Robertson, and S. E. Beare, 2008: The MOGREPS short-range ensemble prediction system. Quart. J. Roy. Meteor. Soc., 134, 703–722.CrossRefGoogle Scholar
  7. Bowler, N. E., A. Arribas, S. E. Beare, K. R. Mylne, and G. J. Shutts, 2009: The local ETKF and SKEB: Upgrades to the MOGREPS short-range ensemble prediction system. Quart. J. Roy. Meteor. Soc., 135, 767–776.CrossRefGoogle Scholar
  8. Buizza, F., and T. N. Palmer, 1998: Impact of ensemble size on ensemble prediction. Mon. Wea. Rev., 126, 2503–2518.CrossRefGoogle Scholar
  9. Buizza, R. T. Petroliagis, T. Palmer, J. Barkmeijer, M. Hamrud, A. Hollingsworth, A. Simmons, and N. Wedi, 1998: Impact of model resolution and ensemble size on the performance of an ensemble prediction system. Quart. J. Roy. Meteor. Soc., 124, 1935–1960.CrossRefGoogle Scholar
  10. Candille, G., and O. Talagrand, 2004: On limitations to the objective evaluation of ensemble prediction systems. Workshop on Ensemble Methods, UK Met Offect, Exeter, October 2004.Google Scholar
  11. Candille, G., and O. Talagrand, 2005: Evaluation of probabilistic prediction systems for a scalar variable. Quart. J. Roy. Meteor. Soc., 131, 2131–2150.CrossRefGoogle Scholar
  12. Casella, G., and R. L. Berger, 1990: Statistical Inference. Duxbury Press, 650pp.Google Scholar
  13. Ehrendorfer, M., 1994a: The Liouville equation and its potential usefulness for the prediction of forecast skill. I: Theory. Mon. Wea. Rev., 122, 703–713.CrossRefGoogle Scholar
  14. Ehrendorfer, M., 1994b: The Liouville equation and its potential usefulness for the prediction of forecast skill. I: Applications. Mon. Wea. Rev., 122, 714–728.CrossRefGoogle Scholar
  15. Ehrendorfer, M., 2007: A review of issues in ensemblebased Kalman filtering. Meteor. Z., 16, 795–818.CrossRefGoogle Scholar
  16. Epstein, E. S., 1969: A scoring system for probability forecast of ranked categories. J. Appl. Meteor., 8, 985–987.CrossRefGoogle Scholar
  17. Hacker, J. P., and Coauthors, 2011: The U.S. air force weather agency’s mesoscale ensemble: Scientific description and performance results. Tellus A, 63, 625–641.CrossRefGoogle Scholar
  18. Hamill, T. M., S. L. Mullen, C. Snyder, Z. Toth, and D. P. Baumhefner, 2000: Ensemble forecasting in the short to medium range: Report from a workshop. Bull. Amer. Meteor. Soc., 81, 2653–2664.CrossRefGoogle Scholar
  19. Jolliffe, I. T., and D. B. Stephenson, 2003: Forecast Verification: A Practitioner’S Guide in Atmospheric Science. John Wiley and Sons, Chichester, 292pp.Google Scholar
  20. Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-year reanalysis project. Bull. Amer. Meteor. Soc., 77, 437–471.CrossRefGoogle Scholar
  21. Leith, C. E., 1974: Theoretical skill of Monte Carlo forecasts. Mon. Wea. Rev., 102, 409–418.CrossRefGoogle Scholar
  22. Leutbecher, M., and T. N. Palmer, 2008: Ensemble forecasting. J. Comput. Phys., 227, 3515–3539.CrossRefGoogle Scholar
  23. Lorenz, E. N., 1963: Deterministic nonperiodic flow. J. Atmos. Sci., 20, 130–141.CrossRefGoogle Scholar
  24. Mullen, S. L., and R. Buizza, 2002: The impact of horizontal resolution and ensemble size on probabilistic forecasts of precipitation by the ECMWF Ensemble Prediction System. Wea. Forecasting, 17, 173–191.CrossRefGoogle Scholar
  25. Murphy, A. H., 1971: A note on the ranked probability score. J. Appl. Meteor., 10, 155–156.CrossRefGoogle Scholar
  26. Murphy, A. H., 1973: A new vector partition of the probability score. J. Apply. Meteor., 12, 595–600.CrossRefGoogle Scholar
  27. Palmer, T. N., 1993: Extended-range atmospheric prediction and the Lorenz model. Bull. Amer. Meteor. Soc., 74, 49–65.CrossRefGoogle Scholar
  28. Park, Y. Y., R. Buizza, and M. Leutbecher, 2008: TIGGE: Preliminary results on comparing and combining ensembles. Quart. J. R. Meteorol. Soc., doi: 10.1002/gi.Google Scholar
  29. Richardson, D. S., 2001: Measures of skill and value of ensemble prediction systems, their interrelationship and the effect of ensemble size. Quart. J. Roy. Meteor. Soc., 127, 2473–2489.CrossRefGoogle Scholar
  30. Stanski, H. R., L. J. Wilson, and W. R. Burrows, 1989: Survey of common verification methods in meteo rology. Research Report 89-5, Environment Canada, 114pp.Google Scholar
  31. Talagrand, O., R. Vautard, and B. Strauss, 1997: Evaluation of probabilistic prediction system. Proc. ECMWF Workshop on Predictability, ECMWF, 1–25.Google Scholar
  32. Tippett, M. K., J. L. Anderson, C. H. Bishop, T. M. Hamill, and J. S. Whitaker, 2003: Ensemble square root filters. Mon. Wea. Rev., 131, 1485–1490.CrossRefGoogle Scholar
  33. Wang, X., and C. H. Bishop, 2003: A comparison of breeding and ensemble transform Kalman filter ensemble forecast schemes. J. Atmos. Sci., 60, 1140–1157.CrossRefGoogle Scholar
  34. Wei, M., Z. Toth, R. Wobus, and Y. Zhu, 2008: Initial perturbations based on the ensemble transform (ET) technique in the NCEP global operational forecast system. Tellus A, 60, 62–79.Google Scholar
  35. Whitaker, J. S., and A. F. Loughe, 1998: The relationship between ensemble spread and ensemble mean skill. Mon. Wea. Rev., 126, 3292–3302.CrossRefGoogle Scholar
  36. Wilks, D. S., 2002: Smoothing ensembles with fitted probability distribution. Quart. J. Roy. Meteor. Soc., 128, 2821–2836.CrossRefGoogle Scholar
  37. Wilks, D. S., 2006: Statistical Methods in the Atmospheric Sciences. 2nd ed. Academic Press, 627pp.Google Scholar

Copyright information

© Chinese National Committee for International Association of Meteorology and Atmospheric Sciences, Institute of Atmospheric Physics, Science Press and Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jun Kyung Kay
    • 1
  • Hyun Mee Kim
    • 1
    Email author
  • Young-Youn Park
    • 2
  • Joohyung Son
    • 2
  1. 1.Department of Atmospheric SciencesYonsei UniversitySeoulRepublic of Korea
  2. 2.Korea Meteorological AdministrationSeoulRepublic of Korea

Personalised recommendations