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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

Parameter Sensitivities of the Dual-Localization Approach in the Local Ensemble Transform Kalman Filter