Multifidelity Ensemble Kalman Filtering

Adrian Sandu, Virginia Tech

Data assimilation aims to improve forecasting by fusing information from mathematical models and observations from nature. Ensemble Kalman filters (EnKF) have gained widespread popularity for large-scale data assimilation. They use a Monte Carlo approach to propagate covariance information, and take advantage of ensemble forecasting to remove the linear model assumption in conventional Kalman filtering.

This talk discusses a new multifidelity ensemble Kalman filter (MFEnKF) algorithm based on linear control variate framework.  The approach allows for rigorous multifidelity extensions of the EnKF, where the uncertainty in coarser fidelities in the hierarchy of models represent control variates for the uncertainty in finer fidelities.  Small ensembles of high-fidelity model runs are complemented by larger ensembles of cheaper, lower fidelity runs, to obtain much improved analyses at only small additional computational costs.

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