• Login
    View Item 
    •   Home
    • Research
    • Articles
    • View Item
    •   Home
    • Research
    • Articles
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of KAUSTCommunitiesIssue DateSubmit DateThis CollectionIssue DateSubmit Date

    My Account

    Login

    Quick Links

    Open Access PolicyORCID LibguideTheses and Dissertations LibguideSubmit an Item

    Statistics

    Display statistics

    Enhancing Ensemble Data Assimilation into One-Way-Coupled Models with One-Step-Ahead-Smoothing

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    enhancing.pdf
    Size:
    4.434Mb
    Format:
    PDF
    Description:
    Accepted Article
    Download
    Type
    Article
    Authors
    Raboudi, Naila Mohammed Fathi cc
    Ait-El-Fquih, Boujemaa cc
    Subramanian, Aneesh C. cc
    Hoteit, Ibrahim cc
    KAUST Department
    Earth Fluid Modeling and Prediction Group
    Earth Science and Engineering
    Earth Science and Engineering Program
    Physical Science and Engineering (PSE) Division
    KAUST Grant Number
    REP/1/3268-01-01
    Date
    2020-10-14
    Online Publication Date
    2020-10-14
    Print Publication Date
    2021-01
    Embargo End Date
    2021-09-30
    Submitted Date
    2019-08-28
    Permanent link to this record
    http://hdl.handle.net/10754/665399
    
    Metadata
    Show full item record
    Abstract
    This study investigates the filtering problem with one-way coupled (OWC) state-space systems, for which the joint ensemble Kalman filter (EnKF) is the standard solution. In this approach, the states of the two coupled sub-systems are jointly updated with all incoming observations. This enables transferring the information across the subsystems, which should provide coupled-state estimates in better agreement with the observations. The state estimates of the joint EnKF highly depend on the relevance of the joint ensembles’ cross-covariances between the sub-systems’ variables. In this work, we propose a new joint EnKF scheme based on the One-Step-Ahead (OSA) smoothing formulation of the filtering problem for efficient assimilation into OWC systems. The scheme introduces an extra smoothing step for both states sub-systems with the future observations, followed by an analysis step for each sub-system state using only its own observation, all within a Bayesian consistent framework. The extra OSA-smoothing step enables to more efficiently exploit the observations, to enhance the representativeness of the EnKF covariances, and to mitigate for reported inconsistencies in the joint EnKF analysis step.We demonstrate the relevance of the proposed approach by presenting and analyzing results of various numerical experiments conducted with a OWC Lorenz-96 model.
    Citation
    Raboudi, N. F., Ait-El-Fquih, B., Subramanian, A. C., & Hoteit, I. (2020). Enhancing Ensemble Data Assimilation into One-Way-Coupled Models with One-Step-Ahead-Smoothing. Quarterly Journal of the Royal Meteorological Society. doi:10.1002/qj.3916
    Sponsors
    This work was supported by the Office of Sponsored Research (OSR) at King Abdullah University of Science and Technology (KAUST) under the Virtual Red Sea Initiative (Grant #REP/1/3268-01-01). The research made use of the KAUST supercomputing facilities.
    Publisher
    Wiley
    Journal
    Quarterly Journal of the Royal Meteorological Society
    DOI
    10.1002/qj.3916
    Additional Links
    https://onlinelibrary.wiley.com/doi/10.1002/qj.3916
    ae974a485f413a2113503eed53cd6c53
    10.1002/qj.3916
    Scopus Count
    Collections
    Articles; Physical Science and Engineering (PSE) Division; Earth Science and Engineering Program

    entitlement

     
    DSpace software copyright © 2002-2023  DuraSpace
    Quick Guide | Contact Us | KAUST University Library
    Open Repository is a service hosted by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items. For anonymous users the allowed maximum amount is 50 search results.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.