Enhancing Lagrangian particle tracking using objective Eulerian coherent structures

Forecasting the drift of floating particles in the ocean is crucial for pollution control but it is often hindered by uncertainties in surface velocity fields and numerical particle initialization, leading to significant trajectory errors. This study addresses these challenges by integrating Objective Eulerian Coherent Structures (OECS) into Lagrangian particle-transport simulations. OECS identify hyperbolic regions in ocean flows that redirect trajectories, thereby enabling a more accurate depiction of particle trajectories. Within this framework, we developed and tested a correction algorithm that incorporates attractive and repulsive OECS into particle tracking simulations. The algorithm demonstrated significant reduction on the trajectory errors when applied to a dataset of drifters in the Caribbean Sea. Over a five-day period, the corrected median errors remained around 50 km or less, while uncorrected errors were approximately 70 km. The 90th percentile error of uncorrected trajectories was comparable to the 75th percentile of corrected trajectories. Moreover, the larger outlier in the corrected trajectories was nearly 150 km closer to the observed trajectory than its uncorrected counterpart. In nearly 29 % of all cases, the correction resulted in larger error, suggesting future research. Our results demonstrate that incorporating OECS into Lagrangian simulations improves the particle tracking accuracy, offering a promising methodology for marine debris management including sargassum and plastics forecasting, oil spill tracking, and even search and rescue operations. By reducing trajectory uncertainty, this approach provides a critical advance in addressing the ecological challenges in ocean and coastal systems, paving the way for more effective environmental management strategies.