Shallow Water '06: A Joint Acoustic Propagation/Nonlinear Internal Wave Physics Experiment

Tang, Dajun; Moum, James N.; Lynch, James F.; Abbot, Philip; Chapman, Ross; Dahl, Peter H.; Duda, Timothy F.; Gawarkiewicz, Glen; Glenn, Scott; Goff, John A.; Graber, Hans C.; Kemp, John N.; Maffei, A. R.; Nash, Jonathan D.; Newhall, Arthur E.

doi:10.5670/oceanog.2007.16

Cited by 152 publications

(93 citation statements)

References 11 publications

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“…Data were collected onboard the R/V Oceanus during August 2006 as part of the Office of Naval Research's NonLinear Internal Wave Initiative (NLIWI) over the New Jersey slope and shelf (Tang et al 2007; Fig. 1a).…”

Section: Methodsmentioning

confidence: 99%

Stratification and mixing regimes in biological thin layers over the Mid‐Atlantic Bight

Benoit‐Bird

Nash

Moum

2014

Limnology & Oceanography

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We use density and microstructure data to characterize the properties and physical setting of optical thin layers observed over the New Jersey shelf in the summer of 2006. Layers were differentiated into two types by their vertical position in the water column, fluorescence intensity, and possibly community composition or cell condition as indicated by the measured differences in the ratio of fluorescence to optical backscatter. Both layer types were associated with gradients in stratification; but, the turbulent mixing environment for the two layer types differed significantly. Shallow layers were located within the pycnocline near the maximum stratification and were exposed to strong turbulence within the surface mixed layer. Consequently, turbulent mixing of buoyancy may have been a key factor in maintaining shallow layers. In contrast, relatively weak density gradients and mixing indicate that turbulent processes may have been less critical for deep layers, which were located at the base of the pycnocline. More generally, both shallow and deep layers were geographically located in regions of low median and high mean turbulent kinetic energy dissipation, i.e., regions of rare yet intense mixing events. This work highlights the need to understand the detailed statistics of mixing at time and vertical scales relevant to thin layers, and more specifically, the need to discern the time history of mixing of the fluid that composes layers.

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Section: Methodsmentioning

confidence: 99%

Stratification and mixing regimes in biological thin layers over the Mid‐Atlantic Bight

Benoit‐Bird

Nash

Moum

2014

Limnology & Oceanography

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“…Furthermore, the variability of internal waves can significantly alter the acoustic field as they propagate through an area, irregularly displacing the pycnocline. Several studies have been conducted highlighting the common presence of internal waves along the New England shelf (Apel & Jackson, 2002;Colosi, et al, 2001;Jackson, 2004;Lynch, Lin, Duda, Newhall, & Gawarkiewicz, 2009;Tang, et al, 2006).…”

Section: Environmental Variabilitymentioning

confidence: 99%

Environmental analysis and prediction of transmission loss in the region of the New England Shelfbreak

Hornick¹

2009

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A confluence of several coastal oceanographic features creates an acoustically interesting region with high variability along the New England Shelfbreak. Determining the effect of the variability on acoustic propagation is critical for sonar systems. In the Nantucket Shoals area of the Middle Atlantic Bight, two experiments, the New England Shelfbreak Tests (NEST), were conducted in May and June, 2007 and 2008, to study this variability. A comprehensive climatology of the region along with the experimental data provided detailed information about the variability of the water column, particularly the temperature and sound speed fields. Empirical orthogonal function (EOF) analysis of the ocean sound speed field defined a set of perturbations to the background sound speed field for each of the NEST Scanfish surveys.Attenuation due to bottom sediments is the major contributor of transmission loss in the ocean. In shallow water, available propagation paths most often include bottom interaction. Perturbations in the ocean sound speed field can cause changes in the angle of incidence of sound rays with the bottom, which can result in changes to the amount of sound energy lost to the bottom. In lieu of complex transmission loss models, the loss/bounce model provides a simpler way to predict transmission loss changes due to perturbations in the background sound speed field in the ocean. Using an acoustic wavenumber perturbation method, sound speed perturbations, defined by the ocean EOF modes, are translated into a change in the horizontal wavenumber, which in turn changes the modal angle of incidence. The loss/bounce model calculates the loss of sound energy (dB) per bottom bounce over a given distance based on the change in angle of incidence. Evaluated using experimental data from NEST, the loss/bounce model provided accurate predictions of changes to transmission loss due to perturbations of the background sound speed field.

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“…The Office of Naval Research sponsored Shallow Water 2006 (SW06) experiment included collection of a large set of oceanographic and acoustic measurements on the New Jersey Shelf/Hudson Canyon region of the Mid-Atlantic bight (Tang, Moum et al 2007, Chapman and Lynch 2010, Lin, Newhall et al 2010. The operation took place in late July-September 2006.…”

Section: Shallow Water 2006 Experiments and Ocean Modelmentioning

confidence: 99%

Validation of genetic algorithm-based optimal sampling for ocean data assimilation

et al. 2016

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Abstract-Regional ocean models are capable of forecasting conditions for usefully long intervals of time (days) provided that initial and ongoing conditions can be measured. In resource-limited circumstances, the placement of sensors in optimal locations is essential. Here, a nonlinear optimization approach to determine optimal adaptive sampling that uses the Genetic Algorithm (GA) method is presented. The method determines sampling strategies that minimize a user-defined physics-based cost function. The method is evaluated using identical twin experiments, comparing hindcasts from an ensemble of simulations that assimilate data selected using the GA adaptive sampling and other methods. For skill metrics, we employ the reduction of the ensemble root-mean-square-error (RMSE) between the "true" data-assimilative ocean simulation and the different ensembles of data-assimilative hindcasts. A 5-glider optimal sampling study is set up for a 400 km x 400 km domain in the Middle Atlantic Bight region, along the New Jersey shelf-break. Results are compared for several ocean and atmospheric forcing conditions. Index Terms-Genetic algorithms, ocean technology, optimization methods sampling methods, adaptive sampling, computational ocean modeling, data assimilation, error subspace statistical estimation, OSSE

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Shallow Water '06: A Joint Acoustic Propagation/Nonlinear Internal Wave Physics Experiment

Cited by 152 publications

References 11 publications

Stratification and mixing regimes in biological thin layers over the Mid‐Atlantic Bight

Stratification and mixing regimes in biological thin layers over the Mid‐Atlantic Bight

Environmental analysis and prediction of transmission loss in the region of the New England Shelfbreak

Validation of genetic algorithm-based optimal sampling for ocean data assimilation

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