Parameter Sensitivities of the Dual-Localization Approach in the Local Ensemble Transform Kalman Filter
Parameter Sensitivities of the Dual-Localization Approach in the Local Ensemble Transform Kalman Filter
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DOI：
10.2151/sola.2013-039
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发表时间：
2013-11
期刊：
影响因子：
1.9
通讯作者：
K. Kondo;T. Miyoshi;Hiroshi L. Tanaka
中科院分区：
文献类型：
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作者：
K. Kondo;T. Miyoshi;Hiroshi L. Tanaka
在集成卡尔曼滤波器中，协方差定位在处理远距离位置之间基于集成的误差协方差中的采样误差方面起着至关重要的作用。我们可能会过度限制观测的影响，特别是当模型分辨率非常高时，因为由于高分辨率模型的严格定位，比定位尺度更大的结构被移除。为了在有限的整体尺寸下保留较大尺度的结构，提出了同时考虑两个单独的定位尺度的双定位方法。双定位方法使用空间平滑和两个定位尺度分别分析小尺度和大尺度分析增量。这些是双定位方法的控制参数，本研究旨在通过使用称为 SPEEDY 模型的中间 AGCM 进行大量观测系统模拟实验来研究参数敏感性。对球谐谱截断和 Lanczos 滤波器这两个平滑函数进行了测试，结果表明没有显着差异。此外，还研究了对两个定位参数的敏感性，结果表明，双定位方法优于传统的单定位，两个定位尺度的选择相对较宽，范围约为 400 公里。这表明我们可以避免对两个定位参数进行微调。 （引用：Kondo, K.、T. Miyoshi 和 H. L. Tanaka，2013 年：局部集成变换卡尔曼滤波器中双定位方法的参数敏感性。SOLA , 9 , 174−178, doi: 10.2151/sola.2013-039。）
In the ensemble Kalman filter, covariance localization plays an essential role in treating sampling errors in the ensemble-based error covariance between distant locations. We may limit the influence of observations excessively, particularly when the model resolution is very high, since larger-scale structures than the localization scale are removed due to tight localization for the high-resolution model. To retain the larger-scale structures with a limited ensemble size, the dual-localization approach, which considers two separate localization scales simultaneously, has been proposed. The dual-localization method analyzes small-scale and large-scale analysis increments separately using spatial smoothing and two localization scales. These are the control parameters of the dual-localization method, and this study aims to investigate the parameter sensitivities by performing a number of observing system simulation experiments using an intermediate AGCM known as the SPEEDY model. Two smoothing functions, the spherical harmonics spectral truncation and the Lanczos filter, are tested, and the results indicate no significant difference. Also, sensitivity to the two localization parameters is investigated, and the results show that the dual-localization approach outperforms traditional single localization with relatively wide choices of the two localization scales by about 400-km ranges. This suggests that we could avoid fine tuning of the two localization parameters. ( Citation: Kondo, K., T. Miyoshi, and H. L. Tanaka, 2013: Parameter sensitivities of the dual-localization approach in the local ensemble transform Kalman filter. SOLA , 9 , 174−178, doi: 10.2151/sola.2013-039.)