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    Adaptive ensemble optimal interpolation for efficient data assimilation in the red sea

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    Name:
    dictionary_based_paper.pdf
    Size:
    2.535Mb
    Format:
    PDF
    Description:
    Accepted manuscript
    Embargo End Date:
    2023-02-09
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    Type
    Article
    Authors
    Toye, Habib cc
    Zhan, Peng cc
    Sana, Furrukh cc
    Sanikommu, Siva Reddy cc
    Raboudi, Naila Mohammed Fathi cc
    Hoteit, Ibrahim cc
    KAUST Department
    Applied Mathematics and Computational Science Program
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Earth Fluid Modeling and Prediction Group
    Earth Science and Engineering
    Earth Science and Engineering Program
    Electrical and Computer Engineering Program
    Physical Science and Engineering (PSE) Division
    KAUST Grant Number
    REP/1/3268-01-01
    Date
    2021-02-06
    Online Publication Date
    2021-02-06
    Print Publication Date
    2021-04
    Embargo End Date
    2023-02-09
    Submitted Date
    2020-02-28
    Permanent link to this record
    http://hdl.handle.net/10754/667512
    
    Metadata
    Show full item record
    Abstract
    Ensemble optimal interpolation (EnOI) is a variant of the ensemble Kalman filter (EnKF) that operates with a static ensemble to drastically reduce its computational cost. The idea is to use a pre-selected ensemble to parameterize the background covariance matrix, which avoids the costly integration of the ensemble members with the dynamical model during the forecast step of the filtering process. To better represent the pronounced time-varying circulation of the Red Sea, we propose a new adaptive EnOI approach in which the ensemble members are adaptively selected at every assimilation cycle from a large dictionary of ocean states describing the Red Sea variability. We implement and test different schemes to select the ensemble members (i) based on the similarity to the forecast state according to some criteria, or (ii) in term of best representation of the forecast in an ensemble subspace using an Orthogonal Matching Pursuit (OMP) algorithm. The relevance of the schemes is first demonstrated with the Lorenz 63 and Lorenz 96 models. Then results of numerical experiments assimilating real remote sensing data into a high resolution MIT general circulation model (MITgcm) of the Red Sea using the Data Assimilation Research Testbed (DART) system are presented and discussed.
    Citation
    Toye, H., Zhan, P., Sana, F., Sanikommu, S., Raboudi, N., & Hoteit, I. (2021). Adaptive ensemble optimal interpolation for efficient data assimilation in the red sea. Journal of Computational Science, 51, 101317. doi:10.1016/j.jocs.2021.101317
    Sponsors
    This work was funded by the Office of Sponsored Research (OSR) at King Abdullah University of Science and Technology (KAUST), Saudi Arabia under the Virtual Red Sea Initiative (Grant #REP/1/3268-01-01) and the KAUST Center for Marine Environmental Observations (SAKMEO), Saudi Arabia. The research made use of the KAUST supercomputing facilities.
    Publisher
    Elsevier BV
    Journal
    Journal of Computational Science
    DOI
    10.1016/j.jocs.2021.101317
    Additional Links
    https://linkinghub.elsevier.com/retrieve/pii/S187775032100017X
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.jocs.2021.101317
    Scopus Count
    Collections
    Articles; Applied Mathematics and Computational Science Program; Physical Science and Engineering (PSE) Division; Electrical and Computer Engineering Program; Earth Science and Engineering Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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