Meteorology and Atmospheric Physics

, Volume 113, Issue 1–2, pp 27–38 | Cite as

Comparison of ensemble methods for summer-time numerical weather prediction over East Asia

  • S.-D. Kang
  • D. W. ShinEmail author
  • Steve Cocke
  • H.-D. Kim
  • W.-S. Jung
Original Paper


Summer-time short- to medium-range predictability of precipitation, 500-hPa geopotential height, and wind fields over East Asia are investigated by comparing three ensemble forecast configurations: multi-analysis, multi-convection, and multi-model. These three systems are used in this study in order to assess initial condition uncertainties, model uncertainties, and a combination of initial condition and model uncertainties in an ensemble forecast approach. Each system has a set of six members. Ensemble forecast skill is verified in both deterministic and probabilistic senses using the European Center for Medium-range Weather Forecasting analyses and the Tropical Rainfall Measuring Mission Microwave Imager 2A12 rain estimates. The multi-model configuration, which considers both the initial condition and model uncertainties to predict weather phenomena over East Asia, is an optimal set of ensemble members. The bias-corrected ensemble and the superensemble (SE) show similar predictability, but slightly better skill is obtained from the SE forecasts.


Ensemble Member Numerical Weather Prediction Ensemble Forecast Ensemble Prediction System Brier Skill Score 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was funded by Korea Meteorological Administration Research and Development Program under grant CATER (Center for Atmospheric Sciences and Earthquake Research) 2010-75.


  1. Arribas A, Robertson KB, Mylne KR (2005) Test of a poor man’s ensemble prediction system for short-range probability forecasting. Mon Weather Rev 133:1825–1839CrossRefGoogle Scholar
  2. Bougeault P et al (2010) The THORPEX interactive grand global ensemble. Bull Am Meteor Soc 91:1059–1072CrossRefGoogle Scholar
  3. Cocke S, LaRow TE (2000) Seasonal predictions using a regional spectral model embedded within a coupled ocean-atmosphere model. Mon Weather Rev 128:689–708CrossRefGoogle Scholar
  4. Emanuel KA, Zivkovic-Rothman M (1999) Development and evaluation of a convection scheme for use in climate models. J Atm Sci 56:1766–1782CrossRefGoogle Scholar
  5. Ferraro RR, Marks GF (1995) The development of SSM/I rain-rate retrieval algorithms using ground-based radar measurements. J Atm Ocean Technol 12:755–770CrossRefGoogle Scholar
  6. Hamill TM, Juras J (2006) Measuring forecast skill: is it real skill or is it the varying climatology? Q J Roy Meteor Soc 132:2905–2923CrossRefGoogle Scholar
  7. Johnson C, Swinbank R (2009) Medium-range multimodel ensemble combination and calibration. Q J R Meteorol Soc 135:777–794CrossRefGoogle Scholar
  8. Krishnamurti TN, Low-Nam S, Pasch R (1983) Cumulus parameterization and rainfall rates II. Mon Weather Rev 111:815–828CrossRefGoogle Scholar
  9. Krishnamurti TN et al (2003) Improved skill for the anomaly correlation of geopotential heights at 500 hPa. Mon Weather Rev 131:1082–1102CrossRefGoogle Scholar
  10. Kummerow C, Olson WS, Giglio L (1996) A simplified sche\me for obtaining precipitation and vertical hydrometeor profiles from passive microwave sensors. IEEE Trans Geosci Remote Sens 34:1213–1232CrossRefGoogle Scholar
  11. Moorthi S, Suarez MJ (1992) Relaxed Arakawa-Schubert: a parameterization of moist convection for general circulation models. Mon Weather Rev 120:978–1002CrossRefGoogle Scholar
  12. Mutemi JN, Ogallo LA, Krishnamurti TN, Mishra AK, Kumar TSVV (2007) Multimodel based superensemble forecasts for short and medium range NWP over various regions of Africa. Meteorol Atm Phys 95:87–113CrossRefGoogle Scholar
  13. Olson WS, LaFontaine FJ, Smith WL, Achtor RH (1990) Recommended algorithms for the retrieval of rainfall rates in the tropics using SSM/I (DMSP-F8). (Available at University of Wisconsin, 10 pp)Google Scholar
  14. Palmer TN, Molteni F, Mureau R, Buizza R, Chapelet P, Tribbia J (1992) Ensemble prediction. ECMWF Tech Memo 188Google Scholar
  15. Pan HL, Wu WS (1994) Implementing a mass flux convection parameterization scheme for the NMC Medium-Range Forecast model. In: Tenth Conference on Numerical Weather Prediction, American Meteorological Society, Portland, OR, pp 96–98Google Scholar
  16. Rosmond TE (1992) The design and testing of the Navy operational global atmospheric prediction system. Weather Forecast 7:262–272CrossRefGoogle Scholar
  17. Shin DW, Krishnamurti TN (2003a) Short- to medium-range superensemble precipitation forecasts using satellite products. Part I: deterministic forecasting. J Geophys Res 108:8383. doi: 10.1029/201JD001510 Google Scholar
  18. Shin DW, Krishnamurti TN (2003b) Short- to medium-range superensemble precipitation forecasts using satellite products. Part II: probabilistic forecasting. J Geophys Res 108:8384. doi: 10.1029/201JD001511 Google Scholar
  19. Shin DW, Cocke S, LaRow TE (2003) Ensemble configurations for typhoon precipitation forecasts. J Meteor Soc Jpn 81:679–696CrossRefGoogle Scholar
  20. Stensrud DJ, Brooks HE, Du J, Tracton MS, Rogers E (1999) Using ensembles for short-range forecasting. Mon Weather Rev 127:433–446CrossRefGoogle Scholar
  21. Toth Z, Kalnay E (1997) Ensemble forecasting at NCEP and the breeding method. Mon Weather Rev 125:3297–3319CrossRefGoogle Scholar
  22. Toth Z, Zhu YJ, Marchok T (2001) The use of ensembles to identify forecasts with small and large uncertainty. Weather Forecast 16:463–477CrossRefGoogle Scholar
  23. Turk FJ, Qui S, Hawkins J, Smith EA, Marzano FS, Mugnai A (2001) Blending coincident SSM/I, TRMM and Infrared geostationary satellite data for an operational rainfall analysis, Part I: technique description. (Draft)Google Scholar
  24. Zhang Z, Krishnamurti TN (1997) Ensemble forecasting on hurricane tracks. Bull Am Meteorol Soc 78:2785–2795CrossRefGoogle Scholar
  25. Zhang GJ, McFarlane NA (1995) Sensitivity of climate simulations to the parameterization of cumulus convection in the Canadian Climate Centre general circulation model. Atm Ocean 33:407–446Google Scholar
  26. Zhou BB, Du J (2010) Fog prediction from a multimodel mesoscale ensemble prediction system. Weather Forecast 25:303–322CrossRefGoogle Scholar
  27. Ziehmann C (2000) Comparison of a single-model EPS with a multi-model ensemble consisting of a few operational models. Tellus 52:280–299CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • S.-D. Kang
    • 1
  • D. W. Shin
    • 2
    Email author
  • Steve Cocke
    • 2
  • H.-D. Kim
    • 3
  • W.-S. Jung
    • 4
  1. 1.Green Simulation KoreaBusanRepublic of Korea
  2. 2.Center for Ocean-Atmospheric Prediction StudiesFlorida State UniversityTallahasseeUSA
  3. 3.Department of Environmental ConservationKeimyung UniversityDaeguRepublic of Korea
  4. 4.Department of Atmospheric Environmental Information Engineering, Information Research CenterINJE UniversityGimhaeRepublic of Korea

Personalised recommendations