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Article ? AI-assigned paper type based on the abstract. Classification may not be perfect — flag errors using the feedback button. Tier 2 ? Original research — experimental, observational, or case-control study. Direct primary evidence. Environmental Sources Marine & Wildlife Sign in to save

Wind‐Based Estimations of Ocean Surface Currents From Massive Clusters of Drifters in the Gulf of Mexico

Journal of Geophysical Research Oceans 2019 19 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 30 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Angelique C. Haza, Nathan Paldor, Tamay M. Özgökmen Tamay M. Özgökmen Tamay M. Özgökmen Milan Curcic, Shengli Chen, Shengli Chen, Tamay M. Özgökmen Shengli Chen, Gregg Jacobs, Tamay M. Özgökmen

Summary

Researchers used ocean surface drifter data from the Gulf of Mexico to develop models estimating surface currents from wind measurements. This type of modeling can be applied to predict how microplastic debris disperses across ocean surfaces after entering from river or coastal sources.

Study Type Environmental

Abstract During the Lagrangian submesoscale experiment (LASER), 1,000 drifters were launched to sample the surface ocean flow in the northern Gulf of Mexico. Due to half a dozen strong winter storms, about 40% of the drifters lost their drogue. This unintended situation facilitated documentation of both near‐surface (5 cm) and deeper (60 cm) flows. These depths are relevant to transport of oil spills, as well as marine debris, such as microplastics, a rapidly growing environmental problem. Here, we improve the surface Lagrangian current prediction by combining a state‐of‐the‐art ocean forecast model with wind and wave data. The ocean surface velocities are obtained from the Navy Coordinate Ocean Model at 1‐km horizontal resolution, while the wind and wave fields are from the Unified Wave INterface Coupled Model coupled atmosphere‐wave‐ocean model. Two Lagrangian parameterizations are tested: one is based on Ekman dynamics, and the other directly on the surface winds. LASER data set is then used to assess the performance of these formulations, as a function of wind/wave conditions, as well as geographic region. It is found that incorporation of wind and wave data into the ocean circulation model can lead to major prediction improvement, by reducing the average 2‐day separation from the modeled and real LASER trajectories by a factor ranging from 1.4 to 4.9. This is a significant improvement for applications, where a rapid deployment of assets is needed, such as oil spill response, or other tracking problems.

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