An efficient Markovian algorithm for the analysis of ocean currents

An efficient Markovian algorithm for the analysis of ocean currents We propose a method for analyzing ocean currents using a statistical approach. The proposed technique is useful for analyzing global velocity fields and producing indices to describe the probable trajectories and destinations of particles embedded in such fields. Short-term Lagrangian integration of the velocities was used to generate transition matrices that define the system locally. A reshuffling algorithm, based on standard Markov Chain theory, was implemented to mix and synthesize the information involved in the global analysis. Iterative methods were then used to solve the resulting large and sparse linear systems. The method efficiently used local information (short-term Lagrangian integration) to infer global characteristics of the system. Two case studies were presented to emphasize the merits of the described scheme: one using modeled data from the Gulf of California, and another from the Gulf of Mexico. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Environmental Modelling & Software Elsevier

An efficient Markovian algorithm for the analysis of ocean currents

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Publisher
Elsevier
Copyright
Copyright © 2018 Elsevier Ltd
ISSN
1364-8152
eISSN
1873-6726
D.O.I.
10.1016/j.envsoft.2018.02.014
Publisher site
See Article on Publisher Site

Abstract

We propose a method for analyzing ocean currents using a statistical approach. The proposed technique is useful for analyzing global velocity fields and producing indices to describe the probable trajectories and destinations of particles embedded in such fields. Short-term Lagrangian integration of the velocities was used to generate transition matrices that define the system locally. A reshuffling algorithm, based on standard Markov Chain theory, was implemented to mix and synthesize the information involved in the global analysis. Iterative methods were then used to solve the resulting large and sparse linear systems. The method efficiently used local information (short-term Lagrangian integration) to infer global characteristics of the system. Two case studies were presented to emphasize the merits of the described scheme: one using modeled data from the Gulf of California, and another from the Gulf of Mexico.

Journal

Environmental Modelling & SoftwareElsevier

Published: May 1, 2018

References

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