Localization is an essential element of ensemble-based Kalman filters in large-scale systems. Two localization methods are commonly used: Covariance localization and domain localization. The former applies a localizing weight to the forecast covariance matrix while the latter splits the assimilation into local regions in which independent assimilation updates are performed.The domain localization is usually combined with a weighting of the observation error covariance matrix, resulting in a similar localization effect to that of covariance localized filters. In order to improve the performance of domain localization with weighting of the observation errors, a regulated localization scheme is introduced. Twin experiments with the Lorenz-96 model demonstrate that the regulated localization can lead to a significant reduction of the estimation errors as well as increased stability of the assimilation process. In addition, the numerical experiments point out that the combination of covariance localization with a serial processing of observations during the analysis step can destabilize the assimilation process.