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    Ensemble Kalman filtering with one-step-ahead smoothing

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    mwr-d-17-0175.1.pdf
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    Type
    Article
    Authors
    Raboudi, Naila Mohammed Fathi cc
    Ait-El-Fquih, Boujemaa
    Hoteit, Ibrahim cc
    KAUST Department
    Earth Fluid Modeling and Prediction Group
    Earth Science and Engineering Program
    Physical Science and Engineering (PSE) Division
    KAUST Grant Number
    CRG3-2156
    Date
    2018-01-11
    Online Publication Date
    2018-01-11
    Print Publication Date
    2018-02
    Permanent link to this record
    http://hdl.handle.net/10754/627130
    
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    Abstract
    The ensemble Kalman filter (EnKF) is widely used for sequential data assimilation. It operates as a succession of forecast and analysis steps. In realistic large-scale applications, EnKFs are implemented with small ensembles and poorly known model error statistics. This limits their representativeness of the background error covariances and, thus, their performance. This work explores the efficiency of the one-step-ahead (OSA) smoothing formulation of the Bayesian filtering problem to enhance the data assimilation performance of EnKFs. Filtering with OSA smoothing introduces an updated step with future observations, conditioning the ensemble sampling with more information. This should provide an improved background ensemble in the analysis step, which may help to mitigate the suboptimal character of EnKF-based methods. Here, the authors demonstrate the efficiency of a stochastic EnKF with OSA smoothing for state estimation. They then introduce a deterministic-like EnKF-OSA based on the singular evolutive interpolated ensemble Kalman (SEIK) filter. The authors show that the proposed SEIK-OSA outperforms both SEIK, as it efficiently exploits the data twice, and the stochastic EnKF-OSA, as it avoids observational error undersampling. They present extensive assimilation results from numerical experiments conducted with the Lorenz-96 model to demonstrate SEIK-OSA’s capabilities.
    Citation
    Raboudi NF, Ait-El-Fquih B, Hoteit I (2018) Ensemble Kalman Filtering with One-Step-Ahead Smoothing. Monthly Weather Review 146: 561–581. Available: http://dx.doi.org/10.1175/mwr-d-17-0175.1.
    Sponsors
    This work is supported by King Abdullah University of Science and Technology Award CRG3-2156.
    Publisher
    American Meteorological Society
    Journal
    Monthly Weather Review
    DOI
    10.1175/mwr-d-17-0175.1
    Additional Links
    https://journals.ametsoc.org/doi/10.1175/MWR-D-17-0175.1
    ae974a485f413a2113503eed53cd6c53
    10.1175/mwr-d-17-0175.1
    Scopus Count
    Collections
    Articles; Physical Science and Engineering (PSE) Division; Earth Science and Engineering Program

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