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.
We present an analysis of ocean surface dispersion characteristics, on 1–100-m scales, obtained by optically tracking a release of [Formula: see text] bamboo plates for 2 h in the northern Gulf of Mexico. Under sustained 5–6 m s−1 winds, energetic Langmuir cells are clearly delineated in the spatially dense plate observations. Within 10 min of release, the plates collect in windrows with 15-m spacing aligned with the wind. Windrow spacing grows, through windrow merger, to 40 m after 20 min and then expands at a slower rate to 50 m. The presence of Langmuir cells produces strong horizontal anisotropy and scale dependence in all surface dispersion statistics computed from the plate observations. Relative dispersion in the crosswind direction initially dominates but eventually saturates, while downwind dispersion exhibits continual growth consistent with contributions from both turbulent fluctuations and organized mean shear. Longitudinal velocity differences in the crosswind direction indicate mean convergence at scales below the Langmuir cell diameter and mean divergence at larger scales. Although the second-order structure function measured by contemporaneous GPS-tracked surface drifters drogued at ~0.5 m shows persistent r2/3 power law scaling down to 100–200-m separation scales, the second-order structure function for the very near surface plates observations has considerably higher energy and significantly shallower slope at scales below 100 m. This is consistent with contemporaneous data from undrogued surface drifters and previously published model results indicating shallowing spectra in the presence of direct wind-wave forcing mechanisms.
Large eddy simulation (LES), in which the large scales of turbulence are simulated while the effects of the small scales are modeled, is an attractive approach for predicting the behavior of turbulent flows. However, there are a number of modeling and formulation challenges that need to be addressed for LES to become a robust and reliable engineering analysis tool. Optimal LES is a LES modeling approach developed to address these challenges. It requires multipoint correlation data as input to the modeling, and to date these data have been obtained from direct numerical simulations (DNSs). If optimal LES is to be generally useful, this need for DNS statistical data must be overcome. In this paper, it is shown that the Kolmogorov inertial range theory, along with an assumption of small-scale isotropy, the application of the quasinormal approximation and a mild modeling assumption regarding the three-point third-order correlation are sufficient to determine all the correlation data required for optimal LES modeling. The models resulting from these theoretically determined correlations are found to perform well in isotropic turbulence, with better high-wavenumber behavior than the dynamic Smagorinsky model. It is expected that these theory-based optimal models will be applicable to a wide range of turbulent flows, in which the small scales can be modeled as isotropic and inertial. The optimal models developed here are expressed as generalized quadratic and linear finite-volume operators. There are significant quantitative differences between these optimal LES operators and standard finite-volume operators, and these differences can be interpreted as the model of the subgrid effects. As with most other LES models, these theory-based optimal models are expected to break down near walls and other strong inhomogeneities.
Surface drifter observations from the LAgrangian Submesoscale ExpeRiment (LASER) campaign in the Gulf of Mexico are paired with Eulerian (ship‐borne X‐band radar) data to demonstrate that velocity structure functions from drifters differ systematically from Eulerian structure functions over scales from 0.4 to 7 km. These differences result from drifters oversampling surface convergences and regions of intense vorticity. The first‐, second‐, and third‐order structure functions are calculated using quasi‐Lagrangian (drifter) and Eulerian data from approximately the same location and time. Differences between quasi‐Lagrangian and Eulerian structure functions are attributed to two forms of bias. The first bias results from the mean divergence or vorticity of the background flow creating nonzero first‐order structure functions. This background bias affects both quasi‐Lagrangian and Eulerian data when insufficiently time‐averaged. It severely biases the drifter third‐order structure functions but is smaller in Eulerian structure functions at both second and third order. This bias can be corrected for using lower‐order structure functions. The second form of bias results from drifters accumulating in regions with flow statistics that differ from undersampled regions. This accumulation bias is diagnosed by identifying the dependence of the Eulerian structure functions on divergence and vorticity as well as scale. Together, both biases suggest that caution is needed when interpreting second‐order drifter statistics and that linking raw third‐order drifter statistics to energy fluxes is often erroneous in ocean data: Even with background correction and sufficient time‐averaging, drifters overestimate the Eulerian estimate of the third‐order structure function by up to a factor of 5 when signs are consistent.
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 × 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 Carlson et al. Surface Ocean Dispersion Observations 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.
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