A comparison of breeding and ensemble transform vectors for global ensemble generation

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• 作者：Guo Deng ; Hua Tian ; Xiaoli Li ; Jing Chen …
• 关键词：breeding ; ensemble transform ; ensemble prediction system
• 刊名：Journal of Meteorological Research
• 出版年：2012
• 出版时间：February 2012
• 年：2012
• 卷：26
• 期：1
• 页码：52-61
• 全文大小：2,214 KB
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• 作者单位：Guo Deng (1)
  Hua Tian (1)
  Xiaoli Li (1)
  Jing Chen (1)
  Jiandong Gong (1)
  Meiyan Jiao (2)
  
  1. National Meteorological Center, China Meteorological Administration, Beijing, 100081, China
  2. China Meteorological Administration, Beijing, 100081, China
  
• 刊物类别：Atmospheric Sciences; Meteorology; Geophysics and Environmental Physics; Atmospheric Protection/Air
• 刊物主题：Atmospheric Sciences; Meteorology; Geophysics and Environmental Physics; Atmospheric Protection/Air Quality Control/Air Pollution;
• 出版者：The Chinese Meteorological Society
• ISSN：2198-0934

文摘

To compare the initial perturbation techniques using breeding vectors and ensemble transform vectors, three ensemble prediction systems using both initial perturbation methods but with different ensemble member sizes based on the spectral model T213/L31 are constructed at the National Meteorological Center, China Meteorological Administration (NMC/CMA). A series of ensemble verification scores such as forecast skill of the ensemble mean, ensemble resolution, and ensemble reliability are introduced to identify the most important attributes of ensemble forecast systems. The results indicate that the ensemble transform technique is superior to the breeding vector method in light of the evaluation of anomaly correlation coefficient (ACC), which is a deterministic character of the ensemble mean, the root-mean-square error (RMSE) and spread, which are of probabilistic attributes, and the continuous ranked probability score (CRPS) and its decomposition. The advantage of the ensemble transform approach is attributed to its orthogonality among ensemble perturbations as well as its consistence with the data assimilation system. Therefore, this study may serve as a reference for configuration of the best ensemble prediction system to be used in operation. Key words breeding ensemble transform ensemble prediction system