Time-Expanded Sampling for Ensemble Kalman Filter: Assimilation Experiments with Simulated Radar Observations

Abstract

Abstract A time-expanded sampling approach is proposed for the ensemble Kalman filter (EnKF). This approach samples a series of perturbed state vectors from each prediction run not only at the analysis time (as the conventional approach does) but also at other time levels in the vicinity of the analysis time. Since all the sampled state vectors are used to construct the ensemble, the number of required prediction runs can be much smaller than the ensemble size and this can reduce the computational cost. Since the sampling time interval can be adjusted to optimize the ensemble spread and enrich the ensemble structures, the proposed approach can improve the EnKF performance even though the number of prediction runs is greatly reduced. The potential merits of the time-expanded sampling approach are demonstrated by assimilation experiments with simulated radar observations for a supercell storm case.