Estimating the travel time and the most likely path from Lagrangian drifters

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  • 1 Lancaster University, UK.
  • 2 Génomique Métabolique, Genoscope, Institut F. Jacob, CEA, CNRS, Univ Evry, Univ Paris-Saclay, Evry, France
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Abstract

We provide a novel methodology for computing the most likely path taken by drifters between arbitrary fixed locations in the ocean. We also provide an estimate of the travel time associated with this path. Lagrangian pathways and travel times are of practical value not just in understanding surface velocities, but also in modelling the transport of ocean-borne species such as planktonic organisms, and oating debris such as plastics. In particular, the estimated travel time can be used to compute an estimated Lagrangian distance, which is often more informative than Euclidean distance in understanding connectivity between locations. Our methodology is purely data-driven, and requires no simulations of drifter trajectories, in contrast to existing approaches. Our method scales globally and can simultaneously handle multiple locations in the ocean. Furthermore, we provide estimates of the error and uncertainty associated with both the most likely path and the associated travel time.

Corresponding author: Michael O’Malley, m.omalley2@lancaster.ac.uk

Abstract

We provide a novel methodology for computing the most likely path taken by drifters between arbitrary fixed locations in the ocean. We also provide an estimate of the travel time associated with this path. Lagrangian pathways and travel times are of practical value not just in understanding surface velocities, but also in modelling the transport of ocean-borne species such as planktonic organisms, and oating debris such as plastics. In particular, the estimated travel time can be used to compute an estimated Lagrangian distance, which is often more informative than Euclidean distance in understanding connectivity between locations. Our methodology is purely data-driven, and requires no simulations of drifter trajectories, in contrast to existing approaches. Our method scales globally and can simultaneously handle multiple locations in the ocean. Furthermore, we provide estimates of the error and uncertainty associated with both the most likely path and the associated travel time.

Corresponding author: Michael O’Malley, m.omalley2@lancaster.ac.uk
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