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首页> 外文期刊>ournal of the Meteorological Society of Japan >Comparison of the Extended Kalman Filter and the Ensemble Kalman Filter Using the Barotropic General Circulation Model
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Comparison of the Extended Kalman Filter and the Ensemble Kalman Filter Using the Barotropic General Circulation Model

机译:利用正压总循环模型比较扩展卡尔曼滤波器和集合卡尔曼滤波器

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In this study, we compare assimilation techniques of the full-rank extended Kalman filter (EKF) and the ensemble Kalman filter (EnKF), using a barotropic general circulation model, called barotropic S-model, under the perfect model configuration. We investigate the accuracy of the EnKF in reference to the direct computation of the EKF and examine the influence of the localization for EnKF. The barotropic S-model is based on the primitive equations and predicts the vertical mean state of the atmosphere. Although it has the predictability comparable to the operational prediction models, the direct computation of the EKF is possible. Therefore, we can assess the accuracy of the EnKF as a function of the ensemble members. In this study, the convergence of the EnKF to the EKF is examined using various ensemble members of 20, 50, 100, 410, and 1000. The EKF and EnKF directly assimilate the observation in the spectral space, and the observational elements are model variables.According to the result of the root mean square error (RMSE), the EnKF converges to the full-rank EKF filter when the ensemble member is increased to more than 50. It is demonstrated that the 20 ensemble members are insufficient with respect to the convergence. An empirical orthogonal function (EOF) analysis is conducted using the covariance matrices of analysis error for both filters. The structure of the first EOF (EOF-1) indicates the characteristics of the baroclinic instability waves in mid-latitudes in both filters, showing the same geographical distributions when it has converged. Interestingly, another large analysis error is detected in the Arctic region. Furthermore, the influence of the localization is examined by introducing the local ensemble transform Kalman filter (LETKF), which assimilates the observations in the physical space. The observations which are assimilated by the LETKF are retrieved from the spectral space to the physical space. It is found that the analysis error of the non-localized EnKF in the spectral space is smaller than that of the LETKF in the physical space.It is concluded from the comparison of the RMSE that more than 50 ensemble members are required for the non-localized EnKF to converge to the full-rank EKF for the practical assimilation in the spectral space under the perfect model configuration of the barotropic general circulation model of the atmosphere.
机译:在这项研究中,我们在理想的模型配置下,使用称为正压S模型的正压通用循环模型,比较了全秩扩展卡尔曼滤波器(EKF)和集合卡尔曼滤波器(EnKF)的同化技术。我们参考EKF的直接计算来研究EnKF的准确性,并检查EnKF的本地化的影响。正压S模型基于原始方程,可预测大气的垂直平均状态。尽管它的可预测性与操作预测模型相当,但可以直接计算EKF。因此,我们可以评估EnKF作为集合成员函数的准确性。在这项研究中,使用20、50、100、410和1000的各种集合成员检查了EnKF到EKF的收敛性。EKF和EnKF直接吸收了光谱空间中的观测值,并且观测元素是模型变量。根据均方根误差(RMSE)的结果,当将合奏成员增加到50个以上时,EnKF收敛到满秩EKF滤波器。证明了20个合奏成员相对于收敛。使用两个滤波器的分析误差的协方差矩阵进行经验正交函数(EOF)分析。第一个EOF(EOF-1)的结构表示两个过滤器中纬度的斜压不稳定波的特征,收敛后显示相同的地理分布。有趣的是,在北极地区发现了另一个较大的分析错误。此外,通过引入局部集合变换卡尔曼滤波器(LETKF)来检查局部化的影响,该滤波可同化物理空间中的观测结果。由LETKF吸收的观测值从光谱空间检索到物理空间。发现在频谱空间中非局部EnKF的分析误差小于在物理空间中LETKF的分析误差。通过对RMSE的比较得出结论,非局部EnKF的分析需要50个以上的集合成员。在大气正压总环流模型的理想模型配置下,将局部EnKF收敛到满级EKF,以便在光谱空间中进行实际吸收。

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