Inverse barotropic tidal estimation for regional ocean applications

Logutov, Oleg G.; Lermusiaux, Pierre F. J.

doi:10.1016/j.ocemod.2008.06.004

Cited by 54 publications

(35 citation statements)

References 63 publications

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“…The physical model is coupled to multiple biological models (Besiktepe et al, 2003;Tian et al, 2004) and acoustic models (Lam et al, 2009;Lermusiaux et al, 2010). The ocean physics is forced with high-resolution barotropic tides, estimated using nested coastal inversions (Logutov and Lermusiaux, 2008). Due to the complex multiconnected sea domains, all ocean fields are initialized here with new objective mapping schemes developed specifically for PhilEx, using fast marching methods Agarwal and Lermusiaux, 2010).…”

Section: Simulation Estimation and Assimilationmentioning

confidence: 99%

Multiscale Physical and Biological Dynamics in the Philippine Archipelago: Predictions and Processes

Lermusiaux¹,

Haley²,

Leslie³

et al. 2011

Oceanog

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Section: Simulation Estimation and Assimilationmentioning

confidence: 99%

Multiscale Physical and Biological Dynamics in the Philippine Archipelago: Predictions and Processes

Lermusiaux¹,

Haley²,

Leslie³

et al. 2011

Oceanog

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“…Its modeling capabilities include implicit two-way nesting for multiscale hydrostatic primitive equation (PE) dynamics with a nonlinear free-surface [97] and a high-order finite element code on unstructured grids for non-hydrostatic processes also with a nonlinear free-surface [98][99][100]. Other MSEAS subsystems include: initialization schemes [101], nested data-assimilative tidal prediction and inversion [102]; fast-marching coastal objective analysis [103]; stochastic subgrid-scale models (e.g., [104,105]); generalized adaptable biogeochemical modeling systems; Lagrangian Coherent Structures; non-Gaussian data assimilation and adaptive sampling [106][107][108]; dynamically-orthogonal equations for uncertainty predictions [109][110][111]; and machine learning of model formulations [112]. e MSEAS software is used for basic and fundamental research and for realistic simulations and predictions in varied regions of the world's ocean [113][114][115][116][117][118][119][120], including monitoring [121], naval exercises including real-time acoustic-ocean predictions [122] and environmental management [123].…”

Section: Mseas Modeling System E Multidisciplinary Simulation Estimmentioning

confidence: 99%

Autonomous and adaptive oceanographic feature tracking on board autonomous underwater vehicles

Petillo

Schmidt

Lermusiaux

et al. 2015

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Abstracte capabilities of autonomous underwater vehicles (AUVs) and their ability to perform tasks both autonomously and adaptively are rapidly improving, and the desire to quickly and efficiently sample the ocean environment as Earth's climate changes and natural disasters occur has increased significantly in the last decade. As such, this thesis proposes to develop a method for single and multiple AUVs to collaborate autonomously underwater while autonomously adapting their motion to changes in their local environments, allowing them to sample and track various features of interest with greater efficiency and synopticity than previously possible with preplanned AUV or ship-based surveys. is concept is demonstrated to work in field testing on multiple occasions: with a single AUV autonomously and adaptively tracking the depth range of a thermocline or acousticline, and with two AUVs coordinating their motion to collect a data set in which internal waves could be detected. is research is then taken to the next level by exploring the problem of adaptively and autonomously tracking spatiotemporally dynamic underwater fronts and plumes using individual and autonomously collaborating AUVs. Stephanie entered the Massachusetts Institute of Technology/Woods Hole Oceanographic Institution Joint Graduate Program following college to study Oceanographic Engineering. Her Doctoral research, which comprises this thesis, has focused on using autonomous underwater vehicles (AUVs) to perform autonomous and environmentally adaptive sampling of the ocean environment, focusing on underwater feature detection and tracking for more efficient and synoptic data collection with AUVs. Stephanie has enjoyed being on and near the ocean, working with oceanographic vehicles and instrumentation, and seeing her complex underwater vehicle missions work out in the water, and she has had the great opportunity of participating in over a dozen research cruises since she first started working with underwater vehicles in college. roughout graduate school, Stephanie has continued her many hobbies and activities, adding hiking, farming, sailing, and woodworking to the list. I would also like to thank all the members of the LAMSS lab past and present: ank you Toby for fixing nearly any software problem we threw at you on cruise and in the office, and for completing our A-team for every cruise we were on together. ank you also for being my partner in crime on cruise, on travel, and in life. I could not have completed this research nearly as efficiently without you there to devise all sorts of work-arounds when I wanted extra features to make my virtual experiments easier to run. ank you to Erin and Sheida for being both great friends and lab mates and for teaching me that it is okay to not be the only woman in an engineering lab! anks Stephanie for always being excited about my research and hosting craft nights. om, thanks for being such a quick learner on cruise and generally a fun person to hang out with. AcknowledgmentsYou will be a good rep...

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“…These were from SW06 forecasts and/or hindcasts made with the oceanographic modeling and assimilation schemes of the Massachusetts Institute of Technology (MIT, Cambridge) Multidisciplinary Simulation, Estimation, and Assimilation System (MSEAS) [16]. This system included oceanographic primitive-equation models [17] and tidal models [18] with data assimilation and uncertainty prediction [8], [19]. The primitive-equation model used here was a new free-surface version of the Harvard Ocean Prediction System (HOPS) [20] completed at MIT.…”

Section: A Incorporating Salinity Information and Data-assimilated Omentioning

confidence: 99%

Merging Multiple-Partial-Depth Data Time Series Using Objective Empirical Orthogonal Function Fitting

Lin

Newhall

Duda

et al. 2010

IEEE J. Oceanic Eng.

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Abstract-In this paper, a method for merging partial overlapping time series of ocean profiles into a single time series of profiles using empirical orthogonal function (EOF) decomposition with the objective analysis is presented. The method is used to handle internal waves passing two or more mooring locations from multiple directions, a situation where patterns of variability cannot be accounted for with a simple time lag. Data from one mooring are decomposed into linear combination of EOFs. Objective analysis using data from another mooring and these patterns is then used to build the necessary profile for merging the data, which is a linear combination of the EOFs. This method is applied to temperature data collected at a two vertical moorings in the 2006 New Jersey Shelf Shallow Water Experiment (SW06). Resulting profiles specify conditions for 35 days from sea surface to seafloor at a primary site and allow for reliable acoustic propagation modeling, mode decomposition, and beamforming.

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Inverse barotropic tidal estimation for regional ocean applications

Cited by 54 publications

References 63 publications

Multiscale Physical and Biological Dynamics in the Philippine Archipelago: Predictions and Processes

Multiscale Physical and Biological Dynamics in the Philippine Archipelago: Predictions and Processes

Autonomous and adaptive oceanographic feature tracking on board autonomous underwater vehicles

Merging Multiple-Partial-Depth Data Time Series Using Objective Empirical Orthogonal Function Fitting

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