Impact of Removing Covariance Localization in an Ensemble Kalman Filter: Experiments with 10 240 Members Using an Intermediate AGCM
Impact of Removing Covariance Localization in an Ensemble Kalman Filter: Experiments with 10 240 Members Using an Intermediate AGCM
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DOI：
10.1175/mwr-d-15-0388.1
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发表时间：
2016-11
影响因子：
3.2
通讯作者：
K. Kondo;T. Miyoshi
中科院分区：
文献类型：
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作者：
K. Kondo;T. Miyoshi
高维地球物理系统的集合卡尔曼滤波器（EnKF）通常使用多达100个集合成员，并需要协方差本地化，以减少远距离位置之间的预测误差协方差的采样误差。作者以前的工作开创了一个EnKF的实施与高达10 240个成员的大型合奏，但这种方法需要应用相对较宽的协方差本地化，以避免内存溢出。这项研究修改了EnKF代码，以节省内存，并首次启用了完全的协方差本地化与中间AGCM的删除。本研究在大样本下，探讨分析与预测的准确度，以及在样本误差较小时，协方差局部化的影响。在理想模式方案下，进行了一系列60天不同局部化尺度的资料同化循环试验，研究了协方差局部化的纯影响。结果显示……
AbstractThe ensemble Kalman filter (EnKF) with high-dimensional geophysical systems usually employs up to 100 ensemble members and requires covariance localization to reduce the sampling error in the forecast error covariance between distant locations. The authors’ previous work pioneered implementation of an EnKF with a large ensemble of up to 10 240 members, but this method required application of a relatively broad covariance localization to avoid memory overflow. This study modified the EnKF code to save memory and enabled for the first time the removal of completely covariance localization with an intermediate AGCM. Using the large sample size, this study aims to investigate the analysis and forecast accuracy, as well as the impact of covariance localization when the sampling error is small. A series of 60-day data assimilation cycle experiments with different localization scales are performed under the perfect model scenario to investigate the pure impact of covariance localization. The results show...