Multilevel ensemble Kalman filtering algorithms
Hakon Hoel, RWTH Aachen 184.108.40.2060:15 - 11:15
The ensemble Kalman filter (EnKF) is a Monte-Carlo-based sequential filtering
method that is often both robust and efficient, but its performance may suffer
in settings where the computational cost of accurate simulations of ensemble
members/particles is high. I will present recent results [1, 2, 3] on marrying the
multilevel Monte Carlo method with EnKF to obtain the multilevel ensemble
Kalman filter (MLEnKF). The new method can be applied in the following
(I) finite-dimensional state space and discrete-time observations,
(II) infinite-dimensional state space and discrete-time, finite-dimensional ob-
Theoretical results and numerical evidence of the performance gain of MLEnKF
over EnKF will be presented.
 H. Hoel, K. Law, and R. Tempone, Multilevel ensemble Kalman filtering,
SIAM J. Numer. Anal. 54(3), 18131839, 2016.
 A. Chernov, H. Hoel, K. Law, F. Nobile, and R. Tempone, Multilevel ensem-
ble Kalman filtering for spatio-temporal processes, arXiv:1710.07282, 2017.
 H. Hoel, G. Shaimerdenova, and R. Tempone, Multilevel Ensemble Kalman
Filtering with local-level Kalman gains, arXiv:2002.00480, 2020.