Title: An adjoint-based adaptive ensemble Kalman filter

Authors: Hajoon Song (Scripps Institution of Oceanography, La Jolla, CA, USA)
Ibrahim Hoteit (King Abdullah University of Sciences and Technology (KAUST), Thuwal, KSA)
Bruce Cornuelle (Scripps Institution of Oceanography, La Jolla, CA, USA)
Aneesh Subramanian (Scripps Institution of Oceanography, La Jolla, CA, USA)

This contribution presents a new hybrid EnKF (ensemble Kalman filter)/4D-VAR (four dimensional variational) approach to mitigate background covariance limitations in the EnKF. The work is based on the AEnKF (adaptive EnKF) method, which bears a strong resemblance to the hybrid EnKF/3D-VAR method. In the AEnKF, the representativeness of the EnKF ensemble is regularly enhanced with new members generated after back projection of the EnKF analysis residuals to state space using a 3D-VAR (or OI - optimal interpolation) scheme with a preselected background covariance matrix. The idea here is to reformulate the transformation of the residuals as a 4D-VAR problem while constraining the new member with model dynamics and previous observations. This should provide more information for the estimation of the new member and reduce dependence of the AEnKF on the assumed stationary background covariance matrix. This is achieved by integrating the analysis residuals backward in time with the adjoint model.

Numerical experiments are performed with the Lorenz-96 model under different scenarios to test the new approach and to evaluate its performance with respect to the AEnKF and the hybrid EnKF/3D-VAR. The new method leads to the least root-squared-mean estimation errors as long as the linear assumption guaranteeing the stability of the adjoint model holds. It is also found to be less sensitive to choices of the assimilation system inputs and parameters than the other methods tested.

GMAO Head: Michele Rienecker Global Modeling and Assimilation Office NASA Goddard Space Flight Center

Curator: Nikki Privé Last Updated: May 27 2011