CFD application to oceanic mixed layer sampling with Lagrangian platforms

Özgökmen, TamayM.; Fischer, P. F.

doi:10.1080/10618562.2012.668888

Cited by 16 publications

(5 citation statements)

References 64 publications

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“…Satellite chlorophyll and sea surface color images (Lévy et al, 2018; Shulman et al, 2015), as well as synthetic aperture radar images (Karimova & Gade, 2016; Munk et al, 2000), routinely show the strong filamentation produced by submesoscale currents. Direct, in situ observations, however, are less prevalent, as submesoscales are too large and evolve too rapidly to be easily mapped by single ship surveys (Özgökmen & Fischer, 2012). Consequently, much of what is known about the kinematic properties of near‐surface submesoscale currents has been derived from simulations.…”

Section: Introductionmentioning

confidence: 99%

Submesoscale Kinematic Properties in Summer and Winter Surface Flows in the Northern Gulf of Mexico

et al. 2020

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Statistical properties of near-surface horizontal velocity gradients are obtained from four drifter experiments conducted in the Gulf of Mexico during Summer 2012 and Winter 2016. The data density provided by the near-simultaneous deployments of 90-326 surface drifters in each allows direct, drifter-based estimates of the scale dependence of velocity gradients at separation scales ranging from 200 m to 5 km. The robustness of these estimates, derived from uniquley sampled, nearly equilateral triplets, is confirmed by comparisons with estimates produced from larger drifter clusters, and with estimates based on concurrent Eulerian X-band radar observations. The winter launches were deployed above a ∼80 m deep mixed layer, one in a region with nearly homogeneous horizontal density structure, the other in a region of strong surface density gradients associated with filaments of fresh Mississippi River water. The summer launches occurred in a shallow (10m) mixed layer, one launched across a mesoscale frontal jet separating regions of horizontally homogeneous density and the other, similar to the corresponding winter launch, also in a region filamented by shallow lenses of cold, fresh water. Seasonal differences are observed, with larger velocity fluctuations and greater variance in divergence and vorticity, especially at the smallest scales, in winter. Differences between same-season launches are, however, as large as seasonal differences. In both seasons, observations sampling regions directly impacted by fresh water fluxes show strongly skewed vorticity distributions, with cyclonic vorticity dominating strain. For the other launches, one in each season, strain dominated minimally skewed vorticity. Plain Language Summary Variations in the near-surface currents act to redistribute floating material in the ocean. While much is known about variations at large scales, 50-100 km, the statistics of surface currents at smaller scales, 100m-10km, are not nearly as well documented. Here observations provided by hundreds of satellite-tracked drifting instruments (drifters) are used to measure such statistics in four distinct experiments, two each in summer and winter. The large number of drifters in each case allows us to determine, with a new level of statistical certainty, how surface current properties vary on scales ranging from 200 m to 5 km and provides benchmark observations of seasonal and spatial variability. Supporting observations of the upper-ocean density structure allows investigation of this impact on small-scale currents. Higher variability occurs in the more energetic winter flows, as expected. However, differences between two launches within a single season are equally large. Similarities between two flows, occurring in different seasons, can be traced to the presence of fresh, Mississippi River water in each. The drifter estimates were found to be consistent with measurements derived from a completely independent set of observations (surface radar images) indicating the potential for measuring near-surface c...

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Section: Introductionmentioning

confidence: 99%

Submesoscale Kinematic Properties in Summer and Winter Surface Flows in the Northern Gulf of Mexico

et al. 2020

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“…Instantaneous measurement of all representative spatiotemporal scales of the ocean state is notoriously difficult [36] . As previously reviewed [41] , traditional observing systems are not ideal for synoptic sampling of near-surface flows at the submesoscale. Owing to the large spacing between ground tracks [10] and along-track signal contamination from high-frequency motions [6] , gridded altimeter-derived sea level anomalies only resolve the largest submesoscale motions.…”

Section: Introductionmentioning

confidence: 99%

Submesoscale dispersion in the vicinity of the Deepwater Horizon spill

Poje

Özgökmen

Lipphardt

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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254

259

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Reliable forecasts for the dispersion of oceanic contamination are important for coastal ecosystems, society, and the economy as evidenced by the Deepwater Horizon oil spill in the Gulf of Mexico in 2010 and the Fukushima nuclear plant incident in the Pacific Ocean in 2011. Accurate prediction of pollutant pathways and concentrations at the ocean surface requires understanding ocean dynamics over a broad range of spatial scales. Fundamental questions concerning the structure of the velocity field at the submesoscales (100 m to tens of kilometers, hours to days) remain unresolved due to a lack of synoptic measurements at these scales. Using high-frequency position data provided by the near-simultaneous release of hundreds of accurately tracked surface drifters, we study the structure of submesoscale surface velocity fluctuations in the Northern Gulf of Mexico. Observed two-point statistics confirm the accuracy of classic turbulence scaling laws at 200-m to 50-km scales and clearly indicate that dispersion at the submesoscales is local, driven predominantly by energetic submesoscale fluctuations. The results demonstrate the feasibility and utility of deploying large clusters of drifting instruments to provide synoptic observations of spatial variability of the ocean surface velocity field. Our findings allow quantification of the submesoscale-driven dispersion missing in current operational circulation models and satellite altimeter-derived velocity fields.T he Deepwater Horizon (DwH) incident was the largest accidental oil spill into marine waters in history with some 4.4 million barrels released into the DeSoto Canyon of the northern Gulf of Mexico (GoM) from a subsurface pipe over ∼84 d in the spring and summer of 2010 (1). Primary scientific questions, with immediate practical implications, arising from such catastrophic pollutant injection events are the path, speed, and spreading rate of the pollutant patch. Accurate prediction requires knowledge of the ocean flow field at all relevant temporal and spatial scales. Whereas ocean general circulation models were widely used during and after the DwH incident (2-6), such models only capture the main mesoscale processes (spatial scale larger than 10 km) in the GoM. The main factors controlling surface dispersion in the DeSoto Canyon region remain unclear. The region lies between the mesoscale eddy-driven deep water GoM (7) and the winddriven shelf (8) while also being subject to the buoyancy input of the Mississippi River plume during the spring and summer months (9). Images provided by the large amounts of surface oil produced in the DwH incident revealed a rich array of flow patterns (10) showing organization of surface oil not only by mesoscale straining into the loop current "Eddy Franklin," but also by submesoscale processes. Such processes operate at spatial scales and involve physics not currently captured in operational circulation models. Submesoscale motions, where they exist, can directly influence the local transport of biogeochemical tracers (11, 12) ...

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“…The Lagrangian submesoscale experiment (LASER) was an expedition carried out in the Gulf of Mexico (GoM) in the winter of 2016 with the purpose of measuring surface transport pathways in the northern GoM (NGoM), motivated by the 2010 Deepwater Horizon (DwH) event, the largest accidental marine oil spill in history (D'Asaro et al, ). LASER was based mostly on Lagrangian sampling, namely, deployment of 1,000 surface drifters, aimed at capturing both mesoscale nondivergent flows and submesoscale divergent flows without the spatial‐temporal aliasing problems inherent in many other sampling techniques (Özgökmen & Fischer, ).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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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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CFD application to oceanic mixed layer sampling with Lagrangian platforms

Cited by 16 publications

References 64 publications

Submesoscale Kinematic Properties in Summer and Winter Surface Flows in the Northern Gulf of Mexico

Submesoscale Kinematic Properties in Summer and Winter Surface Flows in the Northern Gulf of Mexico

Submesoscale dispersion in the vicinity of the Deepwater Horizon spill

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

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