Fast lagrangian particle tracking in unstructured ocean model grids

• Published in: Ocean dynamics Vol. 71; no. 4; pp. 423 - 437
• Main Authors: Vennell, Ross, Scheel, Max, Weppe, Simon, Knight, Ben, Smeaton, Malcolm
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2021; Springer Nature B.V
• Subjects: Atmospheric Sciences; Coastal zone; Earth and Environmental Science; Earth Sciences; Fluid- and Aerodynamics; Geophysics/Geodesy; Hydrodynamic models; Hydrodynamics; Interpolation; Invasive species; Microprocessors; Monitoring/Environmental Analysis; Ocean currents; Ocean models; Oceanography; Oceans; Parameter sensitivity; Particle tracking; Probability theory; Statistical methods; Unstructured grids (mathematics); Biophysical; Lagrangian; Particle tracking; Ocean transport
• ISSN: 1616-7341; 1616-7228
• DOI: 10.1007/s10236-020-01436-7

Lagrangian particle tracking, based on currents derived from hydrodynamic models, is an important tool in quantifying bio-physical transports in the ocean. Particle tracking in the unstructured grids typically used in coastal regions is computationally slow, limiting the number of particles and ranges of behaviours that can be modeled. Techniques used in a new offline particle tracker “OceanTracker” are shown to be two orders of magnitude faster than those used in an existing ocean particle tracker for unstructured grids when run on a single computer core. More significantly, its computational speed can exceed that achieved when particle tracking on a regular grid. The techniques for unstructured grids make it possible to routinely calculate the trajectories of millions of particles. This large number of particles allows much better estimates of dispersion and transport statistics, particularly when the probability of connection is low but the consequences are significant, e.g. the spread of invasive species. It also enables wider exploration of parameter sensitivity and particles’ bio-physical behaviours to provide more robust results. The speed increases result largely from exploiting history and reuse within the spatial interpolation of the hydrodynamic model’s output. Using multiple computer cores further increased the speed to track a given number of particles.