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  • 1
    Publication Date: 2022-05-26
    Description: Author Posting. © American Meteorological Society, 2016. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Physical Oceanography 46 (2016): 3661-3679, doi:10.1175/JPO-D-16-0018.1.
    Description: A hydrostatic, coupled-mode, shallow-water model (CSW) is described and used to diagnose and simulate tidal dynamics in the greater Mid-Atlantic Bight region. The reduced-physics model incorporates realistic stratification and topography, internal tide forcing from a priori estimates of the surface tide, and advection terms that describe first-order interactions of internal tides with slowly varying mean flow and mean buoyancy fields and their respective shear. The model is validated via comparisons with semianalytic models and nonlinear primitive equation models in several idealized and realistic simulations that include internal tide interactions with topography and mean flows. Then, 24 simulations of internal tide generation and propagation in the greater Mid-Atlantic Bight region are used to diagnose significant internal tide interactions with the Gulf Stream. The simulations indicate that locally generated mode-one internal tides refract and/or reflect at the Gulf Stream. The redirected internal tides often reappear at the shelf break, where their onshore energy fluxes are intermittent (i.e., noncoherent with surface tide) because meanders in the Gulf Stream alter their precise location, phase, and amplitude. These results provide an explanation for anomalous onshore energy fluxes that were previously observed at the New Jersey shelf break and linked to the irregular generation of nonlinear internal waves.
    Description: We thank the National Science Foundation for support under Grant OCE-1061160 (ShelfIT) to the Massachusetts Institute of Technology (MIT) and under Grant OCE-1060430 to the Woods Hole Oceanographic Institution. PFJL and PJH also thank the Office of Naval Research for research support under Grants N00014-11-1-0701 (MURI-IODA), N00014-12-1-0944 (ONR6.2), and N00014-13-1-0518 (Multi-DA) to MIT.
    Description: 2017-06-14
    Keywords: Continental shelf/slope ; Inertia-gravity waves ; Internal waves ; Boundary currents ; Tides ; Baroclinic models
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 2
    Publication Date: 2022-05-26
    Description: Author Posting. © The Author(s), 2016. This is the author's version of the work. It is posted here by permission of Springer for personal use, not for redistribution. The definitive version was published in Ocean Dynamics 66 (2016): 1209–1229, doi:10.1007/s10236-016-0976-5.
    Description: 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.
    Description: This work was supported in part by a Space and Naval Warfare Center (SPAWAR) SBIR program. PFJL and PJH are also grateful to the Office of Naval Research for partial support under grants N00014-14-1-0476 (Science of Autonomy LEARNS), N00014-11-1-0701 (MURI-IODA) and N00014-12-1-0944 (ONR6.2) to the Massachusetts Institute of Technology. TFD’s contribution was funded by the SBIR and grant N00014- 11-1-0701 (MURI-IODA).
    Description: 2017-08-19
    Keywords: Genetic algorithms ; Ocean technology ; Optimization methods ; Sampling methods ; Adaptive sampling ; Computational ocean modeling ; Data assimilation ; Error subspace statistical estimation ; OSSE
    Repository Name: Woods Hole Open Access Server
    Type: Preprint
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