Estimating the travel time and the most likely path from the Global Drifter Program

We provide a novel method and tool for computing the most likely path taken by drifters between arbitrary fixed locations in the ocean. In addition to this we provide an estimate of the travel time associated with the path. Lagrangian pathways and travel times are of practical value not just in understanding surface currents, but also in modelling the transport of ocean-borne species such as planktonic organisms, and floating debris such as plastics. To demonstrate the capabilities of this method we show emperical results derived from the Global Drifter Program data. We use the drifter data to construct Markov transition matrices and apply Dijkstra's algorithm to find the most likely paths. The novelty is that we apply hexagonal tessellation of the ocean using Uber's H3 index (which we show is far superior to the standard practice of rectangular or lat-lon gridding). Furthermore, we provide techniques for measuring uncertainty by bootstrapping and applying rotations to the hexagonal grid. The methodology is purely data-driven, and requires no simulations of drifter trajectories. The method scales globally and is computationally efficient.