The Lagrangian Submesoscale Experiment (LASER) involved the deployment of ~1000 biodegradable GPS-tracked Consortium for Advanced Research on Transport of Hydrocarbon in the Environment (CARTHE) drifters to measure submesoscale upper-ocean currents and their potential impact on oil spills. The experiment was conducted from January to February 2016 in the Gulf of Mexico (GoM) near the mouth of the Mississippi River, an area characterized by strong submesoscale currents. A Helmholtz-Zentrum Geesthacht (HZG) marine X-band radar (MR) on board the R/V F. G. Walton Smith was used to locate fronts and eddies by their sea surface roughness signatures. The MR data were further processed to yield near-surface current maps at ~500-m resolution up to a maximum range of ~3 km. This study employs the drifter measurements to perform the first comprehensive validation of MR near-surface current maps. For a total of 4130 MR–drifter pairs, the root-mean-square error for the current speed is 4 cm and that for the current direction is 12°. The MR samples currents at a greater effective depth than the CARTHE drifters (1–5 m vs ~0.4 m). The mean MR–drifter differences are consistent with a wave- and wind-driven vertical current profile that weakens with increasing depth and rotates clockwise from the wind direction (by 0.7% of the wind speed and 15°). The technique presented here has great potential in observational oceanography, as it allows research vessels to map the horizontal flow structure, complementing the vertical profiles measured by ADCP.

Video imagery of surface waves recorded from a small, off the shelf quadcopter with a self-stabilizing camera gimbal is analyzed to estimate the surface current field. The nadir looking camera acquires a short image sequence, which is geocoded to Universal Transverse Mercator (UTM) coordinates. The resulting image sequence is used to quantify characteristic parameters (wave length, period and direction) of short (0.1 to 1 m) surface waves in space and time. This opens the opportunity to fit the linear dispersion relation to the data and thus monitor the frequency shift induced by an ambient current. The fitting is performed by applying a spectral energy based maximization technique in the wavenumber-frequency domain. The current field is compared to measurements acquired by an Acoustic Doppler Current Profiler mounted on a small boat, showing an overall good agreement. The root mean square error in current velocity is 0.09 m/s with no bias.

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This study investigates the effects of wind–wave processes in a coupled wave–ocean circulation model on Lagrangian transport simulations. Drifters deployed in the southern North Sea from May to June 2015 are used. The Eulerian currents are obtained by simulation from the coupled circulation model (NEMO) and the wave model (WAM), as well as a stand-alone NEMO circulation model. The wave–current interaction processes are the momentum and energy sea state dependent fluxes, wave-induced mixing and Stokes–Coriolis forcing. The Lagrangian transport model sensitivity to these wave-induced processes in NEMO is quantified using a particle drift model. Wind waves act as a reservoir for energy and momentum. In the coupled wave–ocean circulation model, the momentum that is transferred into the ocean model is considered as a fraction of the total flux that goes directly to the currents plus the momentum lost from wave dissipation. Additional sensitivity studies are performed to assess the potential contribution of windage on the Lagrangian model performance. Wave-induced drift is found to significantly affect the particle transport in the upper ocean. The skill of particle transport simulations depends on wave–ocean circulation interaction processes. The model simulations were assessed using drifter and high-frequency (HF) radar observations. The analysis of the model reveals that Eulerian currents produced by introducing wave-induced parameterization into the ocean model are essential for improving particle transport simulations. The results show that coupled wave–circulation models may improve transport simulations of marine litter, oil spills, larval drift or transport of biological materials.