Science China Earth Sciences

, Volume 61, Issue 4, pp 462–472 | Cite as

Algorithm based on local breeding of growing modes for convection-allowing ensemble forecasting

  • Chaohui Chen
  • Xiang Li
  • Hongrang He
  • Jie Xiang
  • Shenjia Ma
Research Paper


We propose a method based on the local breeding of growing modes (LBGM) considering strong local weather characteristics for convection-allowing ensemble forecasting. The impact radius was introduced in the breeding of growing modes to develop the LBGM method. In the local breeding process, the ratio between the root mean square error (RMSE) of local space forecast at each grid point and that of the initial full-field forecast is computed to rescale perturbations. Preliminary evaluations of the method based on a nature run were performed in terms of three aspects: perturbation structure, spread, and the RMSE of the forecast. The experimental results confirm that the local adaptability of perturbation schemes improves after rescaling by the LBGM method. For perturbation physical variables and some near-surface meteorological elements, the LBGM method could increase the spread and reduce the RMSE of forecast, improving the performance of the ensemble forecast system. In addition, different from those existing methods of global orthogonalization approach, this new initial-condition perturbation method takes into full consideration the local characteristics of the convective-scale weather system, thus making convectionallowing ensemble forecast more accurate.


Convection-allowing ensemble forecasting Local breeding of growing modes Perturbation structure Spread Root mean square error of forecast 


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The authors thank anonymous reviewers for their constructive comments and suggestions. This work was supported by the Natural Science Foundation of Nanjing Joint Center of Atmospheric Research (Grant Nos. NJCAR2016MS02 and NJCAR2016ZD04), the National Natural Science Foundation of China (Grant Nos. 41205073 and 41675007), and the National Key Research and Development Program of China (Grant No. 2017YFC1501800).


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Copyright information

© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Chaohui Chen
    • 1
  • Xiang Li
    • 1
  • Hongrang He
    • 1
  • Jie Xiang
    • 1
  • Shenjia Ma
    • 1
  1. 1.College of Meteorology and OceanographyNational University of Defense TechnologyNanjingChina

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