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dc.contributor.authorMohtar, Samah El
dc.contributor.authorHoteit, Ibrahim
dc.contributor.authorKnio, Omar
dc.contributor.authorIssa, Leila
dc.contributor.authorLakkis, Issam
dc.date.accessioned2018-09-03T13:26:00Z
dc.date.available2018-09-03T13:26:00Z
dc.date.issued2018-08-21
dc.identifier.citationMohtar 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.
dc.identifier.issn1463-5003
dc.identifier.doi10.1016/j.ocemod.2018.08.008
dc.identifier.urihttp://hdl.handle.net/10754/628468
dc.description.abstractLagrangian 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.
dc.description.sponsorshipThis 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).
dc.publisherElsevier BV
dc.relation.urlhttps://www.sciencedirect.com/science/article/pii/S1463500318300490
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Ocean Modelling. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Ocean Modelling, [, , (2018-08-21)] DOI: 10.1016/j.ocemod.2018.08.008 . © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectStochastic flow fields
dc.subjectRed Sea
dc.subjectLagrangian Tracking
dc.titleLagrangian Tracking in Stochastic Fields with Application to an Ensemble of Velocity Fields in the Red Sea
dc.typeArticle
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentEarth Fluid Modeling and Prediction Group
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.identifier.journalOcean Modelling
dc.eprint.versionPost-print
dc.contributor.institutionDepartment of Mechanical Engineering, American University of Beirut, Beirut, Lebanon
dc.contributor.institutionDuke University, Durham, NC 27708, USA
dc.contributor.institutionDepartment of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon
kaust.personMohtar, Samah El
kaust.personHoteit, Ibrahim
kaust.personKnio, Omar
refterms.dateFOA2018-09-04T13:07:01Z
dc.date.published-online2018-08-21
dc.date.published-print2018-11


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