GRAPES全球变分同化背景误差协方差的改进及对分析预报的影响:背景误差协方差三维结构的估计

An improvement of background error covariance in the global GRAPES variational data assimilation and its impact on the analysis and prediction:Statistics of the three-dimensional structure of background error covariance

  • 摘要: 回顾并详细推导了估计背景误差协方差统计特征的美国国家气象中心(NMC)方法及其优缺点;采用NMC方法系统地估计了新版GRAPES全球模式的背景误差方差、水平相关特征尺度和垂直相关结构,并与欧洲中心模式结果进行了比较。结果表明,目前GRAPES全球模式的背景误差方差比以前有了显著减小;水平相关特征尺度随纬度和高度有显著变化;背景误差垂直相关结构与欧洲中心模式结果非常一致,相比经验公式结果更具物理意义,同时,单点试验结果也表明,更新后的垂直相关结构产生的分析增量更合理。通过与欧洲中心模式背景误差协方差三维结构的对比,分析了不同模式间背景误差协方差的异同及GRAPES全球同化分析系统目前存在的一些不足及可能原因。为新版GRAPES全球模式的三维变分系统提供了基本的背景误差协方差的三维结构。

     

    Abstract: The background error variance, horizontal correlation length and vertical correlation structure of the latest GRAPES version global model are estimated using the NMC method. The results show that the background error variance of the latest version GRAPES is much smaller than the previous version. The horizontal correlation length of the background error varies dramatically with the latitude and pressure levels. The statistical vertical correlation structures are more appropriate, especially for the stream function and the velocity potential. A single-point experiment for the statistical vertical correlation structure is performed and its result illustrates that the analysis increment distribution is more reasonable than using the vertical correlation structure produced by experienced formula.

     

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