| 摘要: |
| 在同化系统中使用更合理的背景误差协方差对于得到更良好的同化效果至关重要。首先采用NMC方法针对中国区域构建更适合WRF-ARW区域预报系统的B矩阵，并对比分析了其与GSI同化系统预设的NCEP预报系统的B矩阵在分析变量间的平衡关系、分析控制变量的标准差、水平和垂直特征尺度等方面的特征差异。参照这些特征差异设计单点观测试验、背景误差协方差调优参数敏感性试验，确定针对中国区域构建B矩阵的最佳调优参数。并讨论其对一次季风低压强降水天气过程的循环同化和预报效果的影响。结果表明，采用最佳调优参数使用针对中国区域构建B矩阵的试验(Sen6)对V风分量场和相对湿度场的预报性能改进显著，同时也引出了GSI同化系统背景误差协方差参数调优(尤其是水平特征尺度参数调整)的两难问题。在此基础上，采用Hybrid同化方法使用针对中国区域构建B矩阵的循环同化试验(Hyb3)可以进一步改善预报效果，并在一定程度上修正个例模拟雨带的位置。 |
| 关键词: 资料同化 GSI同化系统 背景误差协方差 |
| DOI：10.16032/j.issn.1004-4965.2020.039 |
| 分类号: |
| 基金项目: |
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| BACKGROUND ERROR COVARIANCE CHARACTERISTICS BASED ON GSI ASSIMILATION SYSTEM AND ITS EFFECT ON PREDICTION RESULTS |
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YAO Le-bao1,2, WANG Dong-hai3, ZHANG Yu4, SHEN Dan5, LI Guo-ping6
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1. School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, China;2.5. Genhe Meteorological Bureau, HulunBuir 022350, China;3.School of Atmospheric Sciences/ Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies/ Southern Marine Science and Engineering Guangdong Laboratory, Sun Yat-sen University, Zhuhai, 519082, China;4.School of Atmospheric Sciences, Guangdong Ocean University, Zhanjiang, 524094, China;5.Inner Mongolia Autonomous Region Meteorological Centre, Hohhot 010051, China;6.School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, China
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| Abstract: |
| To improve assimilation results, a more reasonable background error covariance is crucial for the assimilation system. In this paper, first, the NMC method is adopted to construct a B matrix that is more suitable for the WRF-ARW regional prediction system for the Chinese region. Then the characteristics of the B matrix and the NCEP prediction system preset by the GSI assimilation system are compared and analyzed. The single-point observation test and background error covariance tuning parameter sensitivity test are designed with reference to the differences in their characteristics to determine the optimal tuning parameters for the development of B matrix for the Chinese region. The results show that the Sen6 experiment with the optimal tuning parameters and the B matrix constructed for the Chinese region has the best prediction effect. In particular, the prediction performance of V wind component field and relative humidity field has been improved significantly. At the same time, the dilemma of background error covariance parameter adjustment (especially horizontal length scale parameter adjustment) in the GSI assimilation system is also introduced. On this basis, the Hyb3 cyclic assimilation experiment with hybrid assimilation method and B matrix constructed for the Chinese region can further improve prediction results, and to some extent, the location of the simulated rain belt is modified. |
| Key words: data assimilation GSI assimilation system background error covariance |
