Localizing the Error Covariance by Physical Distances within a Local Ensemble Transform Kalman Filter (LETKF)
Localizing the Error Covariance by Physical Distances within a Local Ensemble Transform Kalman Filter (LETKF)
复制标题
DOI：
10.2151/sola.2007-023
复制
发表时间：
2007-01-01
期刊：
影响因子：
1.9
通讯作者：
Enomoto, Takeshi
中科院分区：
文献类型：
--
作者：
Miyoshi, Takemasa;Yamane, Shozo;Enomoto, Takeshi
介绍了一种利用物理距离进行误差协方差定位的局部集成变换卡尔曼滤波算法(LETKF)，并对其性能进行了评估。该方法不是使用模型网格空间中均匀分布的局部面片来定位误差协方差，而是通过计算精确的物理距离来定位，从而解决了极地区域分析不连续的问题。用实际观测资料进行了资料同化循环试验，结果表明极地地区的不连续性较小。此外，对于不同的局部化尺度，计算时间更短，鲁棒性更强。因此，本研究中介绍的实现是未来LETKF系统的一个有前途的选择。
An efficient implementation of the local ensemble transform Kalman filter (LETKF) with the error covariance localization by physical distances is introduced and assessed in this study. Instead of using local patches uniform in the model grid space to localize the error covariance, accurate physical distances are computed and used for the localization, so that the problem of analysis discontinuities in the Polar Regions is solved. Data assimilation cycle experiments with real observations are performed, which indicate less discontinuity in the Polar Regions. Moreover, the computational time is shorter and more robust for various localization scales. Thus, the implementation introduced in this study is a promising choice of future LETKF systems.