Journal of Meteorological Research

, Volume 31, Issue 5, pp 955–964 | Cite as

A convection-allowing ensemble forecast based on the breeding growth mode and associated optimization of precipitation forecast

  • Xiang Li
  • Hongrang He
  • Chaohui Chen
  • Ziqing Miao
  • Shigang Bai
Regular Article
  • 8 Downloads

Abstract

A convection-allowing ensemble forecast experiment on a squall line was conducted based on the breeding growth mode (BGM). Meanwhile, the probability matched mean (PMM) and neighborhood ensemble probability (NEP) methods were used to optimize the associated precipitation forecast. The ensemble forecast predicted the precipitation tendency accurately, which was closer to the observation than in the control forecast. For heavy rainfall, the precipitation center produced by the ensemble forecast was also better. The Fractions Skill Score (FSS) results indicated that the ensemble mean was skillful in light rainfall, while the PMM produced better probability distribution of precipitation for heavy rainfall. Preliminary results demonstrated that convection-allowing ensemble forecast could improve precipitation forecast skill through providing valuable probability forecasts. It is necessary to employ new methods, such as the PMM and NEP, to generate precipitation probability forecasts. Nonetheless, the lack of spread and the overprediction of precipitation by the ensemble members are still problems that need to be solved.

Key words

convection-allowing ensemble forecast breeding growth mode (BGM) precipitation optimization probability matched mean (PMM) neighborhood ensemble probability (NEP) Fractions Skill Score (FSS) 

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References

  1. Bentzien, S., and P. Friederichs, 2012: Generating and calibrating probabilistic quantitative precipitation forecasts from the high-resolution NWP model COSMO-DE. Wea. Forecasting, 27, 988–1002, doi: 10.1175/WAF-D-11-00101.1.CrossRefGoogle Scholar
  2. Bishop, C. H., and Z. Toth, 1999: Ensemble transformation and adaptive observations. J. Atmos. Sci., 56, 1748–1765, doi: 10.1175/1520-0469(1999)056<1748:ETAAO>2.0.CO;2.CrossRefGoogle Scholar
  3. Bishop, C. H., B. J. Etherton, and S. J. Majumdar, 2001: Adaptive sampling with the ensemble transform Kalman filter. Part I: Theoretical aspects. Mon. Wea. Rev., 129, 420–436, doi: 10.1175/1520-0493(2001)129<0420:ASWTET>2.0.CO;2.CrossRefGoogle Scholar
  4. Chen, C. H., T. Wang, Y. K. Tan, et al., 2009: Research of multimodel short-range ensemble forecasting techniques in forecasting rainy season over Changjiang–Huaihe basin in 2003. J. Trop. Meteor., 25, 449–457, doi: 10.3969/j.issn.1004-4965.2009.04.010. (in Chinese)Google Scholar
  5. Chen, J., J. S. Xue, and H. Yan, 2005: A new initial perturbation method of ensemble mesoscale heavy rain prediction. Chinese J. Atmos. Sci., 29, 717–726, doi: 10.3878/j.issn.1006-9895.2005.05.05. (in Chinese)Google Scholar
  6. Clark, A. J., S. J. Weiss, J. S. Kain, et al., 2012: An overview of the 2010 hazardous weather testbed experimental forecast program spring experiment. Bull. Amer. Meteor. Soc., 93, 55–74, doi: 10.1175/BAMS-D-11-00040.1.CrossRefGoogle Scholar
  7. Ebert, E. E., 2001: Ability of a poor man’s ensemble to predict the probability and distribution of precipitation. Mon. Wea. Rev., 129, 2461–2480, doi: 10.1175/1520-0493(2001)129<2461:AOAPMS>2.0.CO;2.CrossRefGoogle Scholar
  8. Ebert, E. E., 2009: Neighborhood verification: A strategy for rewarding close forecasts. Wea. Forecasting, 24, 1498–1510, doi: 10.1175/2009WAF2222251.1.CrossRefGoogle Scholar
  9. Johnson, A., X. G. Wang, M. Xue, et al., 2011: Hierarchical cluster analysis of a convection-allowing ensemble during the hazardous weather testbed 2009 spring experiment. Part II: Ensemble clustering over the whole experiment period. Mon. Wea. Rev., 139, 3694–3710, doi: 10.1175/mwr-d-11-00016.1.CrossRefGoogle Scholar
  10. Jones, T. A., D. Stensrud, L. Wicker, et al., 2015: Simultaneous radar and satellite data storm-scale assimilation using an ensemble Kalman filter approach for 24 May 2011. Mon. Wea. Rev., 143, 165–194, doi: 10.1175/MWR-D-14-00180.1.CrossRefGoogle Scholar
  11. Kong, F., M. Xue, K. W. Thomas, et al., 2008: Real-time stormscale ensemble forecast 2008 spring experiment. Proceedings of the 24th Conference on Severe Local Storms, Savannah, Amer. Metor. Soc., 27–31.Google Scholar
  12. Kong, F., M. Xue, K. Thomas, et al., 2009: A real-time storm-scale ensemble forecast system: 2009 spring experiment. 23rd Conference on Weather Analysis and Forecasting/19th Conference Numerical Weather Prediction Omaha, USA, Amer. Meteor. Soc., 16A.3.Google Scholar
  13. Kühnlein, C., C. Keil, G. C. Craig, et al., 2014: The impact of downscaled initial condition perturbations on convectivescale ensemble forecasts of precipitation. Quart. J. Roy. Meteor. Soc., 140, 1552–1562, doi: 10.1002/qj.2238.CrossRefGoogle Scholar
  14. Li, J., J. Du, M. H. Wang, et al., 2010: Precipitation verifications of an ensemble prediction system using two initial perturbation schemes based on AREM. J. Trop. Meteor., 26, 733–742, doi: 10.3969/j.issn.1004-4965.2010.06.012. (in Chinese)Google Scholar
  15. Lorenz, E. N., 1963: Deterministic nonperiodic flow. J. Atmos. Sci., 20, 130–141, doi: 10.1175/1520-0469(1963)020<0130:dnf>2.0.co;2.CrossRefGoogle Scholar
  16. Roberts, N. M., 2005: An Investigation of the Aability of a Storm Scale Configuration of the Met Office NWP Model to Predict Flood-producing rainfall. UK Met Office Technology Report No. 455, 80 pp.Google Scholar
  17. Roberts, N. M., and H. W. Lean, 2008: Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events. Mon. Wea. Rev., 136, 78–97, doi: 10.1175/2007MWR2123.1.CrossRefGoogle Scholar
  18. Romine, G. S., C. S. Schwartz, J. Berner, et al., 2014: Representing forecast error in a convection-permitting ensemble system. Mon. Wea. Rev., 142, 4519–4541, doi: 10.1175/mwr-d-14-00100.1.CrossRefGoogle Scholar
  19. Schumacher, R. S., and A. J. Clark, 2014: Evaluation of ensemble configurations for the analysis and prediction of heavy-rainproducing mesoscale convective systems. Mon. Wea. Rev., 142, 4108–4138, doi: 10.1175/MWR-D-13-00357.1.CrossRefGoogle Scholar
  20. Schwartz, C. S., J. S. Kain, S. J. Weiss, et al., 2010: Toward improved convection-allowing ensembles: Model physics sensitivities and optimizing probabilistic guidance with small ensemble membership. Wea. Forecasting, 25, 263–280, doi: 10.1175/2009WAF2222267.1.CrossRefGoogle Scholar
  21. Schwartz, C. S., G. S. Romine, K. R. Smith, et al., 2014: Characterizing and optimizing precipitation forecasts from a convection- permitting ensemble initialized by a mesoscale ensemble Kalman filter. Wea. Forecasting, 29, 1295–1318, doi: 10.1175/WAF-D-13-00145.1.CrossRefGoogle Scholar
  22. Schwartz, C. S., G. S. Romine, R. A. Sobash, et al., 2015: NCAR’s experimental real-time convection-allowing ensemble prediction system. Wea. Forecasting, 30, 1645–1654, doi: 10.1175/WAFD-15-0103.1.CrossRefGoogle Scholar
  23. Tennant, W., 2015: Improving initial condition perturbations for MOGREPS-UK. Quart. J. Roy. Meteor. Soc., 141, 2324–2336, doi: 10.1002/qj.2524.CrossRefGoogle Scholar
  24. Toth, Z., and E. Kalnay, 1993: Ensemble forecasting at NMC: The generation of perturbations. Bull. Amer. Meteor. Soc., 74, 2317–2330, doi: 10.1175/1520-0477(1993)074<2317:efantg>2.0.co;2.CrossRefGoogle Scholar
  25. Weisman, M. L., R. J Trapp, G. S. Romine, et al., 2015: The mesoscale predictability experiment (MPEX). Bull. Amer. Meteor. Soc., 96, 2127–2149, doi: 10.1175/BAMS-D-13-00281.1.CrossRefGoogle Scholar
  26. Xue, M., F. Kong, D. Weber, et al., 2007: CAPS real-time stormscale ensemble and high-resolution forecasts as part of the NOAA hazardous weather testbed 2007 spring experiment. Preprints, the 22nd Conf. Wea. Anal. Forecasting/18th Conf. Num. Wea. Pred., Salt Lake City, Amer. Meteor. Soc., 3B.Google Scholar
  27. Zhang, H. B., J. Chen, X. F. Zhi, et al., 2014: Study on the application of GRAPES regional ensemble prediction system. Meteor. Mon., 40, 1076–1087, doi: 10.7519/j.issn.1000-0526.2014.09.005. (in Chinese)Google Scholar

Copyright information

© The Chinese Meteorological Society and Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  • Xiang Li
    • 1
    • 2
  • Hongrang He
    • 1
    • 2
  • Chaohui Chen
    • 1
    • 2
  • Ziqing Miao
    • 3
  • Shigang Bai
    • 4
  1. 1.College of Meteorology and OceanographyPLA University of Science and TechnologyNanjingChina
  2. 2.Nanjing Joint Center of Atmospheric ResearchNanjingChina
  3. 3.PLA Troop 96219KunmingChina
  4. 4.PLA Troop 96319PuningChina

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