Journal of Arid Meteorology

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Research on an Embedded Dynamic-statistics Correction Method of Numerical Model

JIN Shuanglong1, HUA Shenbing1, ZENG Xiaoqing2, ZHOU Lilong2   

  1. 1. State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems, China Electric Power Research Institute, Beijing 100192, China;
    2. National Meteorological Center, Beijing 100081, China
  • Online:2017-12-29 Published:2017-12-29



  1. 1.中国电力科学研究院,新能源与储能运行控制国家重点实验室,北京 100192;
    2.国家气象中心,北京 100081
  • 通讯作者: 曾晓青,。
  • 作者简介:靳双龙(1984— ),男,甘肃静宁人,博士,高级工程师,主要从事数值模拟、电力气象技术研究.。
  • 基金资助:



Currently, the improvement of accuracy of numerical weather prediction depended on modified initial fields and the statistics correction of model output. Although the accurate initial fields could promote the precision of prediction to a certain extent, the systematic errors of predicted output exist. Similarly, the statistics correction to model output can eliminate partly the uncertainty of model in terms of the systematic error, but it cannot make predicted variables to integrate each other. For the reasons above, the embedded model processing correction (EMPC) method was brought up. In this research, the temperature fields in June and July in China were simulated by WRF model and EMPC method. Combined with the corresponding period FNL data, ground observation at 943 weather stations and sounding data at 9 radiosonde stations in China, the model outputs were tested. The results showed that the model output by using EMPC method was better than the directly predicted output by WRF in a short time. However, the conservation of model might be broken due to considering poorly the coherence of physical quantities in initial field, which might make the model instability. Nevertheless, this study may provide a new thought to model revision techniques.

Key words: numerical model error correction, model output statistics, dynamic-statistics method



关键词: 模式误差订正, 模式输出统计, 动力统计技术

CLC Number: