• 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 LibguidePlumX LibguideSubmit an Item

    Statistics

    Display statistics

    Lagrangian Tracking in Stochastic Fields with Application to an Ensemble of Velocity Fields in the Red Sea

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    1-s2.0-S1463500318300490-main.pdf
    Size:
    17.14Mb
    Format:
    PDF
    Description:
    Accepted Manuscript
    Download
    Type
    Article
    Authors
    Mohtar, Samah El
    Hoteit, Ibrahim cc
    Knio, Omar cc
    Issa, Leila
    Lakkis, Issam
    KAUST Department
    Applied Mathematics and Computational Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Earth Fluid Modeling and Prediction Group
    Earth Science and Engineering Program
    Physical Science and Engineering (PSE) Division
    Date
    2018-08-21
    Online Publication Date
    2018-08-21
    Print Publication Date
    2018-11
    Permanent link to this record
    http://hdl.handle.net/10754/628468
    
    Metadata
    Show full item record
    Abstract
    Lagrangian tracking of passive tracers in a stochastic velocity field within a sequential ensemble data assimilation framework is challenging due to the exponential growth in the number of particles. This growth arises from describing the behavior of velocity over time as a set of possible combinations of the different realizations, before and after each assimilation cycle. This paper addresses the problem of efficiently advecting particles in stochastic flow fields, whose statistics are prescribed by an underlying ensemble, in a parallel computational framework (openMP). To this end, an efficient algorithm for forward and backward tracking of passive particles in stochastic flow-fields is presented. The algorithm, which employs higher order particle advection schemes, presents a mechanism for controlling the growth in the number of particles. The mechanism uses an adaptive binning procedure, while conserving the zeroth, first and second moments of probability (total probability, mean position, and variance). The adaptive binning process offers a tradeoff between speed and accuracy by limiting the number of particles to a desired maximum. To validate our method, we conducted various forward and backward particles tracking experiments within a realistic high-resolution ensemble assimilation setting of the Red Sea, focusing on the effect of the maximum number of particles, the time step, the variance of the ensemble, the travel time, the source location, and history of transport.
    Citation
    Mohtar SE, Hoteit I, Knio O, Issa L, Lakkis I (2018) Lagrangian tracking in stochastic fields with application to an ensemble of velocity fields in the Red Sea. Ocean Modelling 131: 1–14. Available: http://dx.doi.org/10.1016/j.ocemod.2018.08.008.
    Sponsors
    This work is partially supported by the University Research Board of the American University of Beirut, and by King Abdullah University of Science and Technology (KAUST).
    Publisher
    Elsevier BV
    Journal
    Ocean Modelling
    DOI
    10.1016/j.ocemod.2018.08.008
    Additional Links
    https://www.sciencedirect.com/science/article/pii/S1463500318300490
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.ocemod.2018.08.008
    Scopus Count
    Collections
    Articles; Applied Mathematics and Computational Science Program; Physical Science and Engineering (PSE) Division; Earth Science and Engineering Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

    entitlement

     
    DSpace software copyright © 2002-2021  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.