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The Ensemble Kalman Filter (EnKF) has achieved great successes in data assimilation in atmospheric and oceanic sciences, but it fails in converging to the correct filtering distribution which precludes its use for uncertainty quantification of dynamic systems. We reformulate EnKF under the framework of Langevin dynamics, which leads to a new particle filtering algorithm, the so-called Langevinized EnKF (LEnKF). LEnKF inherits the forecast-analysis procedure from EnKF and the use of mini-batchdoi:10.5705/ss.202022.0172 fatcat:dla3zdorrrbbdfcmqzkbiabyz4