Lagrangian Tracking in Stochastic Fields with Application to an Ensemble of Velocity Fields in the Red Sea
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ArticleKAUST Department
Applied Mathematics and Computational Science ProgramComputer, 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
2018-08-21Online Publication Date
2018-08-21Print Publication Date
2018-11Permanent link to this record
http://hdl.handle.net/10754/628468
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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 BVJournal
Ocean ModellingAdditional Links
https://www.sciencedirect.com/science/article/pii/S1463500318300490ae974a485f413a2113503eed53cd6c53
10.1016/j.ocemod.2018.08.008