An effective configuration of ensemble size and horizontal resolution for the NCEP GEFS
详细信息 查看全文
- 作者：Juhui Ma (1) (2) (3)
Yuejian Zhu (2)
Richard Wobus (4)
Panxing Wang (1) - 关键词：NCEP operational GEFS ; ensemble size ; horizontal resolution ; ensemble mean forecast ; probabilistic forecast
- 刊名：Advances in Atmospheric Sciences
- 出版年：2012
- 出版时间：July 2012
- 年：2012
- 卷：29
- 期：4
- 页码：782-794
- 全文大小：481KB
- 参考文献：1. Buizza, R., 2010: Horizontal resolution impact on short and long range forecast error. / Quart. J. Roy. Meteor. Soc., 136, 1020-035. CrossRef
2. Buizza, R., and T. Palmer, 1998: Impact of ensemble size on ensemble prediction. / Mon. Wea. Rev., 126, 2503-518. CrossRef
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, 1935-960. CrossRef
4. Buizza, R., A. Hollingsworth, F. Lalaurette, and A. Ghelli, 1999: Probabilistic predictions of precipitation using the ECMWF Ensemble Prediction System. / Wea. Forecasting, 14, 168-89. CrossRef
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, 681-95. CrossRef
6. Du, J., S. L. Mullen, and F. Sanders, 1997: Short-range ensemble forecasting of quantitative precipitation. / Mon. Wea. Rev., 125, 2427-459. CrossRef
7. Leith, C. E., 1974: Theoretical skill of Monte Carlo forecasts. / Mon. Wea. Rev., 102, 409-18. CrossRef
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, 173-91. CrossRef
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, 1513-523. CrossRef
10. Murphy, A. H., 1973: A new vector partition of the probability score. / J. Appl. Meteor., 12, 595-00. CrossRef
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, 2145-155. CrossRef
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, 2473-489. CrossRef
13. Szunyogh, I., and Z. Toth, 2002: The effect of increased horizontal resolution on the NCEP global ensemble mean forecasts. / Mon. Wea. Rev., 130, 1125-143. CrossRef
14. Toth, Z., and E. Kalnay, 1997: Ensemble forecasting at NCEP and the breeding method. / Mon. Wea. Rev., 125, 3297-319. CrossRef
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, 137-63.
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, 28-4. CrossRef
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, 62-9.
18. Wilks, D. S., 2006: / Statistical Methods in the Atmospheric Sciences. 2nd ed., Academic Press, 627pp.
19. Zhu, Y., 2004: Probabilistic forecasts and evaluations based on global ensemble forecast system. Vol. 3, / World Scientific Series on Meteorology of East Asia, 277-87.
20. Zhu, Y., 2005: Ensemble forecast: A new approach to uncertainty and predictability. / Adv. Atmos. Sci., 22, 781-88. CrossRef
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-.
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, 43-0.
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-.
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, 73-3. CrossRef - 作者单位：Juhui Ma (1) (2) (3)
Yuejian Zhu (2)
Richard Wobus (4)
Panxing Wang (1)
1. Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science & Technology, Nanjing, 210044, China
2. Environmental Modeling Center/NCEP/NOAA, Camp Springs, MD, 20746, USA
3. UCAR, Boulder, CO, 80307, USA
4. I.M. Systems Group, Inc. (IMSG) at the Environmental Modeling Center/NCEP/NOAA, Camp Springs, MD, 20746, USA - ISSN：1861-9533
文摘
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.