Sparse drifter data and mathematical approach map ocean eddies
Sparse drifter data and mathematical approach map ocean eddies lead image
Mesoscale ocean vortices, which range from 100 to 200 kilometers across, transport large amounts of water in the ocean. These eddies are essential for large-scale ocean mixing, and smaller versions are also important during sub-mesoscale mixing events like oil spills. However, finding the structures is difficult: satellites often miss them, and drifting data collection buoys – or drifters – are spread thin in the water.
Encinas-Bartos et al. developed a mathematical tool to identify ocean vortices using the data from a single drifter. Previous methods relied on measurements from other nearby drifters, which isn’t possible when the buoys are often hundreds or thousands of miles away from each other.
“Our motivation was to utilize drifter data directly to identify eddies in the ocean in a way that is mathematically justified,” said group leader and author George Haller. “Eddies are often called the ‘weather of the ocean’. We wanted to extract their coherently rotating water masses based only on intrinsic rotation measures that can be computed from single drifter trajectories, without any reliance on other drifters.”
The team used their recently developed diagnostic, the Trajectory Rotation Average (TRA), to accomplish this task. This scalar field identifies an intrinsic material rotation of a drifter that is found to be the same by all geophysically relevant observers. Regions with distinctly higher TRA indicate an eddy.
“Drifters are all over the ocean and have a great potential to reveal eddies, as long as they are interrogated via a reliable, mathematically supported method,” said Haller.
The researchers plan to develop an open-source software tool to provide real-time TRA-based identification of eddies based on drifter data from the Global Drifter Program.
Source: “Quasi-objective eddy visualization from sparse drifter data,” by Alex P. Encinas-Bartos, Nikolas O. Aksamit, and George Haller, Chaos (2022). The article can be accessed at https://doi.org/10.1063/5.0099859 .
