Advances in Atmospheric Sciences

, Volume 29, Issue 4, pp 782–794 | Cite as

An effective configuration of ensemble size and horizontal resolution for the NCEP GEFS

  • Juhui Ma (麻巨慧)
  • Yuejian Zhu
  • Richard Wobus
  • Panxing Wang


Two important questions are addressed in this paper using the Global Ensemble Forecast System (GEFS) from the National Centers for Environmental Prediction (NCEP): (1) How many ensemble members are needed to better represent forecast uncertainties with limited computational resources? (2) What is the relative impact on forecast skill of increasing model resolution and ensemble size? Two-month experiments at T126L28 resolution were used to test the impact of varying the ensemble size from 5 to 80 members at the 500-hPa geopotential height. Results indicate that increasing the ensemble size leads to significant improvements in the performance for all forecast ranges when measured by probabilistic metrics, but these improvements are not significant beyond 20 members for long forecast ranges when measured by deterministic metrics. An ensemble of 20 to 30 members is the most effective configuration of ensemble sizes by quantifying the tradeoff between ensemble performance and the cost of computational resources. Two representative configurations of the GEFS—the T126L28 model with 70 members and the T190L28 model with 20 members, which have equivalent computing costs—were compared. Results confirm that, for the NCEP GEFS, increasing the model resolution is more (less) beneficial than increasing the ensemble size for a short (long) forecast range.

Key words

NCEP operational GEFS ensemble size horizontal resolution ensemble mean forecast probabilistic forecast 


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  1. Buizza, R., 2010: Horizontal resolution impact on short and long range forecast error. Quart. J. Roy. Meteor. Soc., 136, 1020–1035.CrossRefGoogle Scholar
  2. Buizza, R., and T. Palmer, 1998: Impact of ensemble size on ensemble prediction. Mon. Wea. Rev., 126, 2503–2518.CrossRefGoogle Scholar
  3. Buizza, R., T. Petroliagis, T. N. 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
  4. Buizza, R., A. Hollingsworth, F. Lalaurette, and A. Ghelli, 1999: Probabilistic predictions of precipitation using the ECMWF Ensemble Prediction System. Wea. Forecasting, 14, 168–189.CrossRefGoogle Scholar
  5. Buizza, R., J.-R. Bidlot, N. Wedi, M. Fuentes, M. Hamrud, G. Holt, and F. Vitart, 2007: The new ECMWF VAREPS (Variable Resolution Ensemble Prediction System). Quart. J. Roy. Meteor. Soc., 133, 681–695.CrossRefGoogle Scholar
  6. Du, J., S. L. Mullen, and F. Sanders, 1997: Short-range ensemble forecasting of quantitative precipitation. Mon. Wea. Rev., 125, 2427–2459.CrossRefGoogle Scholar
  7. Leith, C. E., 1974: Theoretical skill of Monte Carlo forecasts. Mon. Wea. Rev., 102, 409–418.CrossRefGoogle Scholar
  8. 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
  9. Muller, W. A., C. Appenzeller, F. J. Doblas-Reyes, and M. A. Liniger, 2005: A debiased ranked probability skill score to evaluate probabilistic ensemble forecasts with small ensemble sizes. J. Climate, 18, 1513–1523.CrossRefGoogle Scholar
  10. Murphy, A. H., 1973: A new vector partition of the probability score. J. Appl. Meteor., 12, 595–600.CrossRefGoogle Scholar
  11. Reynolds, C. A., J. G. McLay, J. S. Goerss, E. A. Serra, D. Hodyss, and C. R. Sampson, 2011: Impact of resolution and design on the U.S. navy global ensemble performance in the tropics. Mon. Wea. Rev., 139, 2145–2155.CrossRefGoogle Scholar
  12. 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
  13. Szunyogh, I., and Z. Toth, 2002: The effect of increased horizontal resolution on the NCEP global ensemble mean forecasts. Mon. Wea. Rev., 130, 1125–1143.CrossRefGoogle Scholar
  14. Toth, Z., and E. Kalnay, 1997: Ensemble forecasting at NCEP and the breeding method. Mon. Wea. Rev., 125, 3297–3319.CrossRefGoogle Scholar
  15. Toth, Z., O. Talagrand, G. Candille, and Y. Zhu, 2003: Probability and Ensemble Forecasts. Forecast Verification: A practitioner’s Guide in Atmospheric Science, Jolliffe and Stephenson, Wiley, 137–163.Google Scholar
  16. Wei, M., Z. Toth, R. Wobus, Y. Zhu, C. H. Bishop, and X. Wang, 2006: Ensemble transform Kalman filter based ensemble perturbations in an operational global prediction system at NCEP. Tellus A, 58, 28–44.CrossRefGoogle Scholar
  17. 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
  18. Wilks, D. S., 2006: Statistical Methods in the Atmospheric Sciences. 2nd ed., Academic Press, 627pp.Google Scholar
  19. Zhu, Y., 2004: Probabilistic forecasts and evaluations based on global ensemble forecast system. Vol. 3, World Scientific Series on Meteorology of East Asia, 277–287.Google Scholar
  20. Zhu, Y., 2005: Ensemble forecast: A new approach to uncertainty and predictability. Adv. Atmos. Sci., 22, 781–788.CrossRefGoogle Scholar
  21. Zhu, Y., and Z. Toth, 2008: Ensemble based probabilistic forecast verification. Preprints, 19th Conference on Probability and Statistics, Amer. Meteor. Soc., New Orleans, Louisiana, 1–6.Google Scholar
  22. Zhu, Y., and J. Ma, 2010: Predictability, probabilistic forecasting and ensemble prediction system. Lecture Notes on Numerical Weather Prediction, WMO Re gional Training Center, NUIST, China, 43–60.Google Scholar
  23. Zhu, Y., G. Iyengar, Z. Toth, S. Tracton, and T. Marchok, 1996: Objective evaluation of the NCEP Global Ensemble Forecasting System. Preprints, 15th Conference on Weather Analysis and Forecasting, Amer. Meteor. Soc., Norfolk, Virginia, 1–4.Google Scholar
  24. Zhu, Y., Z. Toth, R. Wobus, D. Richardson, and K. Mylne, 2002: The economic value of ensemble-based weather forecasts. Bull. Amer. Meteror. Soc., 83, 73–83.CrossRefGoogle 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 2012

Authors and Affiliations

  • Juhui Ma (麻巨慧)
    • 1
    • 2
    • 3
  • Yuejian Zhu
    • 2
  • Richard Wobus
    • 4
  • Panxing Wang
    • 1
  1. 1.Key Laboratory of Meteorological Disaster of Ministry of EducationNanjing University of Information Science & TechnologyNanjingChina
  2. 2.Environmental Modeling Center/NCEP/NOAACamp SpringsUSA
  3. 3.UCARBoulderUSA
  4. 4.I.M. Systems Group, Inc. (IMSG) at the Environmental Modeling Center/NCEP/NOAACamp SpringsUSA

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