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    An Adjoint-Based Adaptive Ensemble Kalman Filter

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    mwr-d-12-002441.pdf
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    Type
    Article
    Authors
    Song, Hajoon
    Hoteit, Ibrahim cc
    Cornuelle, Bruce D.
    Luo, Xiaodong
    Subramanian, Aneesh C.
    KAUST Department
    Earth Fluid Modeling and Prediction Group
    Earth Science and Engineering Program
    Physical Science and Engineering (PSE) Division
    Date
    2013-10
    Permanent link to this record
    http://hdl.handle.net/10754/552769
    
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    Abstract
    A new hybrid ensemble Kalman filter/four-dimensional variational data assimilation (EnKF/4D-VAR) approach is introduced to mitigate background covariance limitations in the EnKF. The work is based on the adaptive EnKF (AEnKF) method, which bears a strong resemblance to the hybrid EnKF/three-dimensional variational data assimilation (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 optimal interpolation (OI)] scheme with a preselected background covariance matrix. The idea here is to reformulate the transformation of the residuals as a 4D-VAR problem, constraining the new member with model dynamics and the 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 done 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 EnKF and the hybrid EnKF/3D-VAR. The new method leads to the least root-mean-square 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.
    Citation
    An Adjoint-Based Adaptive Ensemble Kalman Filter 2013, 141 (10):3343 Monthly Weather Review
    Publisher
    American Meteorological Society
    Journal
    Monthly Weather Review
    DOI
    10.1175/MWR-D-12-00244.1
    Additional Links
    http://journals.ametsoc.org/doi/abs/10.1175/MWR-D-12-00244.1
    ae974a485f413a2113503eed53cd6c53
    10.1175/MWR-D-12-00244.1
    Scopus Count
    Collections
    Articles; Physical Science and Engineering (PSE) Division; Earth Science and Engineering Program

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