Lagrangian tracking in stochastic fields with application to an ensemble of velocity fields in the Red Sea

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dc.contributor.author Issa, Leila
dc.contributor.author El Mohtar, Samah
dc.contributor.author Hoteit, Ibrahim
dc.contributor.author Knio, Omar
dc.contributor.author Lakkis, Issam
dc.date.accessioned 2018-11-21T12:47:49Z
dc.date.available 2018-11-21T12:47:49Z
dc.date.copyright 2018 en_US
dc.date.issued 2018-11-21
dc.identifier.issn 1463-5003 en_US
dc.identifier.uri http://hdl.handle.net/10725/9772
dc.description.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. en_US
dc.language.iso en en_US
dc.title Lagrangian tracking in stochastic fields with application to an ensemble of velocity fields in the Red Sea en_US
dc.type Article en_US
dc.description.version Published en_US
dc.author.school SAS en_US
dc.author.idnumber 201105273 en_US
dc.author.department Computer Science And Mathematics en_US
dc.description.embargo N/A en_US
dc.relation.journal Ocean Modelling en_US
dc.journal.volume 131 en_US
dc.article.pages 1-14 en_US
dc.keywords Stochastic flow fields en_US
dc.keywords Red sea en_US
dc.keywords Lagrangian tracking en_US
dc.identifier.doi https://doi.org/10.1016/j.ocemod.2018.08.008 en_US
dc.identifier.ctation El Mohtar, S., 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. en_US
dc.author.email leila.issa@lau.edu.lb en_US
dc.identifier.tou http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php en_US
dc.identifier.url https://reader.elsevier.com/reader/sd/pii/S1463500318300490?token=6F0656218BB945B3E165629578FBED63CDAA30A5249B88F917C8533F69A5B9EE73F3094BA75AF8FBD29637EB8F9A74FE en_US
dc.orcid.id https://orcid.org/0000-0002-7417-560X en_US
dc.author.affiliation Lebanese American University en_US

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