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    A new deterministic Ensemble Kalman Filter with one-step-ahead smoothing for storm surge forecasting

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    Naila_Raboudi_thesis.pdf
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    Description:
    Raboudi, Naila Thesis
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    Type
    Thesis
    Authors
    Raboudi, Naila Mohammed Fathi cc
    Advisors
    Hoteit, Ibrahim cc
    Committee members
    Knio, Omar cc
    Sun, Shuyu cc
    Ait-El-Fquih, Boujemaa
    Program
    Earth Science and Engineering
    KAUST Department
    Physical Science and Engineering (PSE) Division
    Date
    2016-11
    Embargo End Date
    2017-12-07
    Permanent link to this record
    http://hdl.handle.net/10754/621969
    
    Metadata
    Show full item record
    Access Restrictions
    At the time of archiving, the student author of this thesis opted to temporarily restrict access to it. The full text of this thesis became available to the public after the expiration of the embargo on 2017-12-07.
    Abstract
    The Ensemble Kalman Filter (EnKF) is a popular data assimilation method for state-parameter estimation. Following a sequential assimilation strategy, it breaks the problem into alternating cycles of forecast and analysis steps. In the forecast step, the dynamical model is used to integrate a stochastic sample approximating the state analysis distribution (called analysis ensemble) to obtain a forecast ensemble. In the analysis step, the forecast ensemble is updated with the incoming observation using a Kalman-like correction, which is then used for the next forecast step. In realistic large-scale applications, EnKFs are implemented with limited ensembles, and often poorly known model errors statistics, leading to a crude approximation of the forecast covariance. This strongly limits the filter performance. Recently, a new EnKF was proposed in [1] following a one-step-ahead smoothing strategy (EnKF-OSA), which involves an OSA smoothing of the state between two successive analysis. At each time step, EnKF-OSA exploits the observation twice. The incoming observation is first used to smooth the ensemble at the previous time step. The resulting smoothed ensemble is then integrated forward to compute a "pseudo forecast" ensemble, which is again updated with the same observation. The idea of constraining the state with future observations is to add more information in the estimation process in order to mitigate for the sub-optimal character of EnKF-like methods. The second EnKF-OSA "forecast" is computed from the smoothed ensemble and should therefore provide an improved background. In this work, we propose a deterministic variant of the EnKF-OSA, based on the Singular Evolutive Interpolated Ensemble Kalman (SEIK) filter. The motivation behind this is to avoid the observations perturbations of the EnKF in order to improve the scheme's behavior when assimilating big data sets with small ensembles. The new SEIK-OSA scheme is implemented and its efficiency is demonstrated by performing assimilation experiments with the highly nonlinear Lorenz model and a realistic setting of the Advanced Circulation (ADCIRC) model configured for storm surge forecasting in the Gulf of Mexico during Hurricane Ike.
    Citation
    Raboudi, N. M. F. (2016). A new deterministic Ensemble Kalman Filter with one-step-ahead smoothing for storm surge forecasting. KAUST Research Repository. https://doi.org/10.25781/KAUST-XA185
    DOI
    10.25781/KAUST-XA185
    ae974a485f413a2113503eed53cd6c53
    10.25781/KAUST-XA185
    Scopus Count
    Collections
    MS Theses; Physical Science and Engineering (PSE) Division; Earth Science and Engineering Program

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