%0 Journal Article
%A Bardsley, Johnathan M.
%A Solonen, Antti
%A Parker, Albert
%A Haario, Heikki
%A Howard, Marylesa
%D 2013
%I Begell House
%K ensemble Kalman filter, data assimilation, conjugate gradient iteration, conjugate gradient sampler
%N 4
%P 357-370
%R 10.1615/Int.J.UncertaintyQuantification.2012003889
%T AN ENSEMBLE KALMAN FILTER USING THE CONJUGATE GRADIENT SAMPLER
%U http://dl.begellhouse.com/journals/52034eb04b657aea,7115c9f91645289d,21c8819768da3b59.html
%V 3
%X The ensemble Kalman filter (EnKF) is a technique for dynamic state estimation. EnKF approximates the standard extended Kalman filter (EKF) by creating an ensemble of model states whose mean and empirical covariance are then used within the EKF formulas. The technique has a number of advantages for large-scale, nonlinear problems. First, large-scale covariance matrices required within EKF are replaced by low-rank and low-storage approximations, making implementation of EnKF more efficient. Moreover, for a nonlinear state space model, implementation of EKF requires the associated tangent linear and adjoint codes, while implementation of EnKF does not. However, for EnKF to be effective, the choice of the ensemble members is extremely important. In this paper, we show how to use the conjugate gradient (CG) method, and the recently introduced CG sampler, to create the ensemble members at each filtering step. This requires the use of a variational formulation of EKF. The effectiveness of the method is demonstrated on both a large-scale linear, and a small-scale, nonlinear, chaotic problem. In our examples, the CG-EnKF performs better than the standard EnKF, especially when the ensemble size is small.
%8 2013-03-12