Our unmanned aerial system (UAS) current mapping is based on optical video data of the sea surface. We use three-dimensional fast Fourier transform and least squares fitting to measure the surface waves' phase velocities and the currents via the linear dispersion relationship. Our UAS is a low-cost off-the-shelf quadcopter with inaccurate camera position and attitude measurements, which may cause spurious currents as large as the signal. We present a novel wave-based UAS heading and position correction, improving the image rectification accuracy by a factor of similar to 3.5 and the current measurements' temporal repeatability by factors of 1.8-4.8. This validation study maps the currents at high spatiotemporal resolution (5 m and 4 s) across the similar to 700-m-wide tidally dominated Bear Cut channel in Miami, Florida. The UAS currents are compared to flotsam tracks, obtained through automated UAS video object detection and tracking, drifter tracks, and acoustic Doppler current profiler measurements. The root-mean-square errors of the cross- and along-channel currents are better than 0.03 m s(-1) for the flotsam comparison and better than 0.06 m s(-1) for the drifter comparison; the latter revealed a 0.06 m s(-1) along-wind bias due to wind- and wave-driven vertical current shear. UAS current mapping could be used to monitor river discharge, buoyant pollutants, or submesoscale fronts and eddies; the proposed wave-based heading and position correction enables its use in areas without ground control points.

Much of the vertical transport near the surface of the ocean, which plays a critical role in the transport of dissolved nutrients and gases, is thought to be associated with ageostrophic submesoscale phenomena. Vertical velocities are challenging not only to model accurately, but also to measure because of how difficult they are to locate in the surface waters of the ocean. Using unique massive drifter releases during the Lagrangian Submesoscale Experiment (LASER) campaign in the Gulf of Mexico and the Coherent Lagrangian Pathways from the Surface Ocean to the Interior (CALYPSO) experiment in the Mediterranean Sea, we investigate the generation of submesoscale structures along two different mesoscale fronts. We use a novel method to project Lagrangian trajectories to Eulerian velocity fields, in order to calculate horizontal velocity gradients at the surface, which are used as a proxy for vertical transport. The velocity reconstruction uses a squared-exponential covariance function, which characterizes velocity correlations in horizontal space and time, and determines the scales of variation using the data itself. SST and towed CTD measurements support the findings revealed by the drifter data. Due to the production of a submesoscale instability eddy in the Gulf of Mexico, convergence magnitudes of up to ∼20 times the planetary vorticity, f, are observed, the value of which is almost 3 times larger than that found in the mesoscale dominated Western Mediterranean Sea.

A Lagrangian floating oil trajectory model is developed to simulate a continuous surface spill in the Florida Straits. The trajectories are initiated at locations representative of exploratory drilling sites around Cuba's Exclusive Economic Zone and integrated for ten days. The simulation is initialized for different seasons, using leeway-corrected observed winds, ocean currents, different climatologies, and a first-order Markov Lagrangian Stochastic Model. A Monte-Carlo scheme is used to parameterize oil weathering processes collectively. Overall, the simulations exhibit a strong seasonal dependence, where the Florida coast is most affected during the summer and the Cuban coast in the winter. Drilling sites at the center of the Straits show the largest impact on Florida. Cuba is most affected by shoreline locations. A significant amount of oil reaches the Florida coastline within two to ten days, and Cuba is potentially affected within hours. Many simulations project impacts for the Florida Keys, South Florida, and the north shore of Cuba.

The Lagrangian Submesoscale Experiment (LASER) was designed to study surface flows during winter conditions in the northern Gulf of Mexico. More than 1000 mostly biodegradable drifters were launched. The drifters consisted of a surface floater extending 5 cm below the surface, containing the satellite tracking system, and a drogue extending 60 cm below the surface, hanging beneath the floater on a flexible tether. On some floats, the drogue separated from the floater during storms. This paper describes methods to detect drogue loss based on two properties that distinguish drogued from undrogued drifters. First, undrogued drifters often flip over, pointing their satellite antenna downward and thus intermittently reducing the frequency of GPS fixes. Second, undrogued drifters respond to wind forcing more than drogued drifters. A multistage analysis is used: first, two properties are used to create a preliminary drifter classification; then, the motion of each unclassified drifter is compared to that of its classified neighbors in an iterative process for nearly all of the drifters. The algorithm classified drifters with a known drogue status with an accuracy of virtually 100%. Drogue loss times were estimated with a precision of less than 0.5 and 3 h for 60% and 85% of the drifters, respectively. An estimated 40% of the drifters lost their drogues in the first 7 weeks, with drogue loss coinciding with storm events, particularly those with steep waves. Once the drogued and undrogued drifters are classified, they can be used to quantify the differences in material dispersion at different depths.

Oil slicks and sheens reside at the air-sea interface, a region of the ocean that is notoriously difficult to measure. Little is known about the velocity field at the sea surface in general, making predictions of oil dispersal difficult. The Ship-Tethered Aerostat Remote Sensing System (STARSS) was developed to measure Lagrangian velocities at the air-sea interface by tracking the transport and dispersion of bamboo dinner plates in the field of view of a high-resolution aerial imaging system. The camera had a field of view of approximately 300 x 200 m and images were obtained every 15 s over periods of up to 3 h. A series of experiments were conducted in the northern Gulf of Mexico in January-February 2016. STARSS was equipped with a GPS and inertial navigation system (INS) that was used to directly georectify the aerial images. A relative rectification technique was developed that translates and rotates the plates to minimize their total movement from one frame to the next. Rectified plate positions were used to quantify scale-dependent dispersion by computing relative dispersion, relative diffusivity, and velocity structure functions. STARSS was part of a nested observational framework, which included deployments of large numbers of GPS-tracked surface drifters from two ships, in situ ocean measurements, X-band radar observations of surface currents, and synoptic maps of sea surface temperature from a manned aircraft. Here we describe the STARSS system and image analysis techniques, and present results from an experiment that was conducted on a density front that was approximately 130 km offshore. These observations are the first of their kind and the methodology presented here can be adopted into existing and planned oceanographic campaigns to improve our understanding of small-scale and high-frequency variability at the air-sea interface and to provide much-needed benchmarks for numerical simulations.

Floating oil, plastics, and marine organisms are continually redistributed by ocean surface currents. Prediction of their resulting distribution on the surface is a fundamental, long-standing, and practically important problem. The dominant paradigm is dispersion within the dynamical context of a nondivergent flow: objects initially close together will on average spread apart but the area of surface patches of material does not change. Although this paradigm is likely valid at mesoscales, larger than 100 km in horizontal scale, recent theoretical studies of submesoscales (less than ∼10 km) predict strong surface convergences and downwelling associated with horizontal density fronts and cyclonic vortices. Here we show that such structures can dramatically concentrate floating material. More than half of an array of ∼200 surface drifters covering ∼20 × 20 km 2 converged into a 60 × 60 m region within a week, a factor of more than 10 5 decrease in area, before slowly dispersing. As predicted, the convergence occurred at density fronts and with cyclonic vorticity. A zipperlike structure may play an important role. Cyclonic vorticity and vertical velocity reached 0.001 s −1 and 0.01 ms −1 , respectively, which is much larger than usually inferred. This suggests a paradigm in which nearby objects form submesoscale clusters, and these clusters then spread apart. Together, these effects set both the overall extent and the finescale texture of a patch of floating material. Material concentrated at submesoscale convergences can create unique communities of organisms, amplify impacts of toxic material, and create opportunities to more efficiently recover such material.

Targeted observations of submesoscale currents are necessary to improve science's understanding of oceanic mixing, but these dynamics occur at spatiotemporal scales that are currently challenging to detect. Prior studies have recently shown that the submesoscale surface velocity field can be measured by tracking hundreds of surface drifters released in tight arrays. This strategy requires drifter positioning to be accurate, frequent, and to last for several weeks. However, because of the large numbers involved, drifters must be low-cost, compact, easy to handle, and also made of materials harmless to the environment. Therefore, the novel Consortium for Advanced Research on Transport of Hydrocarbon in the Environment (CARTHE) drifter was designed following these criteria to facilitate massive sampling of near-surface currents during the Lagrangian Submesoscale Experiment (LASER). The drifting characteristics were determined under a wide range of currents, waves, and wind conditions in laboratory settings. Results showed that the drifter accurately follows the currents in the upper 0.60m, that it presents minimal wave rectification issues, and that its wind-induced slip velocity is less than 0.5% of the neutral wind speed at 10m. In experiments conducted in both coastal and deep ocean conditions under wind speeds up to 10 m s(-1), the trajectories of the traditional Coastal Ocean Dynamics Experiment (CODE) and the CARTHE drifters were nearly identical. Following these tests, 1100 units were produced and deployed during the LASER campaign, successfully tracking submesoscale and mesoscale features in the Gulf of Mexico. It is hoped that this drifter will enable high-density sampling near metropolitan areas subject to stress by the overpopulation, such as lakes, rivers, estuaries, and environmentally sensitive areas, such as the Arctic.

On 23 July 2013, a marine gas rig (Hercules 265) ignited in the northern Gulf of Mexico. The rig burned out of control for 2 days before being extinguished. We conducted a rapid-response sampling campaign near Hercules 265 after the fire to ascertain if sediments and fishes were polluted above earlier baseline levels. A surface drifter study confirmed that surface ocean water flowed to the southeast of the Hercules site, while the atmospheric plume generated by the blowout was in eastward direction. Sediment cores were collected to the SE of the rig at a distance of approximate to 0.2, 8, and 18 km using a multicorer, and demersal fishes were collected from approximate to 0.2 to 8 km SE of the rig using a longline (508 hooks). Recently deposited sediments document that only high molecular weight (HMW) polycyclic aromatic hydrocarbon (PAH) concentrations decreased with increasing distance from the rig suggesting higher pyrogenic inputs associated with the blowout. A similar trend was observed in the foraminifera Haynesina germanica, an indicator species of pollution. In red snapper bile, only HMW PAH metabolites increased in 2013 nearly double those from 2012. Both surface sediments and fish bile analyses suggest that, in the aftermath of the blowout, increased concentration of pyrogenically derived hydrocarbons was transported and deposited in the environment. This study further emphasizes the need for an ocean observing system and coordinated rapid-response efforts from an array of scientific disciplines to effectively assess environmental impacts resulting from accidental releases of oil contaminants.

The Grand LAgrangian Deployment (GLAD) used multiscale sampling and GPS technology to observe time series of drifter positions with initial drifter separation of O(100 m) to O(10 km), and nominal 5 min sampling, during the summer and fall of 2012 in the northern Gulf of Mexico. Histograms of the velocity field and its statistical parameters are non-Gaussian; most are multimodal. The dominant periods for the surface velocity field are 1-2 days due to inertial oscillations, tides, and the sea breeze; 5-6 days due to wind forcing and submesoscale eddies; 9-10 days and two weeks or longer periods due to wind forcing and mesoscale variability, including the period of eddy rotation. The temporal e-folding scales of a fitted drifter velocity autocorrelation function are bimodal with time scales, 0.25-0.50 days and 0.9-1.4 days, and are the same order as the temporal e-folding scales of observed winds from nearby moored National Data Buoy Center stations. The Lagrangian integral time scales increase from coastal values of 8 h to offshore values of approximately 2 days with peak values of 3-4 days. The velocity variance is large, O(1)m2/s2, the surface velocity statistics are more anisotropic, and increased dispersion is observed at flow bifurcations. Horizontal diffusivity estimates are O(103)m2/s in coastal regions with weaker flow to O(105)m2/s in flow bifurcations, a strong jet, and during the passage of Hurricane Isaac. The Gulf of Mexico surface velocity statistics sampled by the GLAD drifters are a strong function of the feature sampled, topography, and wind forcing.

In the summer and fall of 2012, during the GLAD experiment in the Gulf of Mexico, the Consortium for Advanced Research on Transport of Hydrocarbon in the Environment (CARTHE) used several ocean models to assist the deployment of more than 300 surface drifters. The Navy Coastal Ocean Model (NCOM) at 1 km and 3 km resolutions, the US Navy operational NCOM at 3 km resolution (AMSEAS), and two versions of the Hybrid Coordinates Ocean Model (HYCOM) set at 4 km were running daily and delivering 72-h range forecasts. They all assimilated remote sensing and local profile data but they were not assimilating the drifter's observations. This work presents a non-intrusive methodology named Multi-Model Ensemble Kalman Filter that allows assimilating the local drifter data into such a set of models, to produce improved ocean currents forecasts. The filter is to be used when several modeling systems or ensembles are available and/or observations are not entirely handled by the operational data assimilation process. It allows using generic in situ measurements over short time windows to improve the predictability of local ocean dynamics and associated high-resolution parameters of interest for which a forward model exists (e.g. oil spill plumes). Results can be used for operational applications or to derive enhanced background fields for other data assimilation systems, thus providing an expedite method to non-intrusively assimilate local observations of variables with complex operators. Results for the GLAD experiment show the method can improve water velocity predictions along the observed drifter trajectories, hence enhancing the skills of the models to predict individual trajectories.

ABSTRACTCARTHE (http://carthe.org/) is a Gulf of Mexico Research Initiative (GoMRI) consortium established through a competitive peer-reviewed selection process. CARTHE comprises 26 principal investigators from 14 universities and research institutions distributed across four Gulf of Mexico states and other four states. It fuses into one group investigators with unique scientific and technical knowledge and extensive publications related to oil fate/transport processes, oceanic and atmospheric turbulence, air-sea interactions, tropical cyclones and winter storms, and coastal and nearshore modeling and observations.Our primary goal is to accurately predict the fate of hydrocarbons released into the environment. Achieving this goal is particularly challenging since petroleum releases into the environment interact with natural processes across six orders of magnitude of time and space scales. We are developing a multi-scale modeling tool by incorporating state-of-the-art hydrophysical models, each applicable for a restricted range of scales, into a single, interconnected modeling system to predict the physical dispersal of hydrocarbons across scales ranging from the microscale at the wellhead to oceanic and atmospheric mesoscales. CARTHE is also conducting novel in-situ observations and laboratory experiments specifically designed for quantifying submesoscale dispersion as well as for both model validation and parameterization. Finally, we are providing a robust set of uncertainty metrics and analysis tools to assess model performance and quantify predictive uncertainty.

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.

Ocean prediction systems rely on an array of assumptions to optimize their data assimilation schemes. Many of these remain untested, especially at smaller scales, because sufficiently dense observations are very rare. A set of 295 drifters deployed in July 2012 in the north-eastern Gulf of Mexico provides a unique opportunity to test these systems down to scales previously unobtainable. In this study, background error covariance assumptions in the 3DVar assimilation process are perturbed to understand the effect on the solution relative to the withheld dense drifter data. Results show that the amplitude of the background error covariance is an important factor as expected, and a proposed new formulation provides added skill. In addition, the background error covariance time correlation is important to allow satellite observations to affect the results over a period longer than one daily assimilation cycle. The results show the new background error covariance formulations provide more accurate placement of frontal positions, directions of currents and velocity magnitudes. These conclusions have implications for the implementation of 3DVar systems as well as the analysis interval of 4DVar systems. Published by Elsevier Ltd.

A series of large eddy simulations is used to assess the transport properties of multi-scale ocean flows. In particular, we compare scale-dependent measures of Lagrangian relative dispersion and the evolution of passive tracer releases in models containing only submesoscale mixed layer instabilities and those containing mixed layer instabilities modified by deeper, baroclinic mesoscale disturbances. Visualization through 3D finite-time Lyapunov exponents and spectral analysis show that the small scale instabilities of the mixed layer rapidly lose coherence in the presence of larger-scale straining induced by the mesoscale motion. Eddy diffusivities computed from passive tracer evolution increase by an order of magnitude as the flow transitions from small to large scales. During the time period when both instabilities are present, scale-dependent relative Lagrangian dispersion, given by the finite-scale Lyapunov exponent (lambda), shows two distinct plateau regions clearly associated with the disparate instability scales. In this case, the maximum value of lambda over the submesocales at the surface flow is three times greater than lambda at the mixed layer base which is only influenced by the deeper baroclinic motions. The results imply that parameterizations of submesoscale transport properties may be needed to accurately predict surface dispersion in models that do not explicitly resolve submesoscale turbulent features. (c) 2012 Elsevier Ltd. All rights reserved.

Two oil particle trajectory forecasting systems were developed and applied to the 2010 Deepwater Horizon Oil Spill in the Gulf of Mexico. Both systems use ocean current fields from high-resolution numerical ocean circulation model simulations, Lagrangian stochastic models to represent unresolved sub-grid scale variability to advect oil particles, and Monte Carlo-based schemes for representing uncertain biochemical and physical processes. The first system assumes two-dimensional particle motion at the ocean surface, the oil is in one state, and the particle removal is modeled as a Monte Carlo process parameterized by a one number removal rate. Oil particles are seeded using both initial conditions based on observations and particles released at the location of the Maconda well. The initial conditions (ICs) of oil particle location for the two-dimensional surface oil trajectory forecasts are based on a fusing of all available information including satellite-based analyses. The resulting oil map is digitized into a shape file within which a polygon filling software generates longitude and latitude with variable particle density depending on the amount of oil present in the observations for the IC. The more complex system assumes three (light, medium, heavy) states for the oil, each state has a different removal rate in the Monte Carlo process, three-dimensional particle motion, and a particle size-dependent oil mixing model.Simulations from the two-dimensional forecast system produced results that qualitatively agreed with the uncertain "truth" fields. These simulations validated the use of our Monte Carlo scheme for representing oil removal by evaporation and other weathering processes. Eulerian velocity fields for predicting particle motion from data-assimilative models produced better particle trajectory distributions than a free running model with no data assimilation. Monte Carlo simulations of the three-dimensional oil particle trajectory, whose ensembles were generated by perturbing the size of the oil particles and the fraction in a given size range that are released at depth, the two largest unknowns in this problem. 36 realizations of the model were run with only subsurface oil releases. An average of these results yields that after three months, about 25% of the oil remains in the water column and that most of the oil is below 800 m. (C) 2011 Elsevier B.V. All rights reserved.