Oceanographic and atmospheric conditions on the continental shelf north of the Monterey Bay during August 2006

Ramp, Steven R.; Lermusiaux, Pierre F. J.; Shulman, Igor; Chao, Yi; Wolf, Rebecca E.; Bahr, Fred

doi:10.1016/j.dynatmoce.2011.04.005

Cited by 28 publications

(19 citation statements)

References 42 publications

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“…At this point, the simulation can be performed. At the end of the simulation, the GUI allows the Before describing the oil spill model (Section 2.2), we note that downscaling and nesting of different regional models has been successfully completed before (e.g., Onken et al 2005;Ramp et al 2011, andLermusiaux et al 2011). In Onken et al (2005), a degradation of the interior solution has been reported for long runs.…”

Section: Irenom-interactive Relocatable Nested Ocean Modelmentioning

confidence: 99%

A relocatable ocean model in support of environmental emergencies

et al. 2014

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During the Costa Concordia emergency case, regional, subregional, and relocatable ocean models have been used together with the oil spill model, MEDSLIK-II, to provide ocean currents forecasts, possible oil spill scenarios, and drifters trajectories simulations. The models results together with the evaluation of their performances are presented in this paper. In particular, we focused this work on the implementation of the Interactive RElo- Concordia emergency and on its validation using drifters released in the area of the accident. It is shown that thanks to the capability of improving easily and quickly its configuration, the IRENOM results are of greater accuracy than the results achieved using regional or subregional model products. The model topography, the initialization procedures, and the horizontal resolution are the key model settings to be configured. Furthermore, the IRENOM currents and the MEDSLIK-II simulated trajectories showed to be sensitive to the spatial resolution of the meteorological fields used, providing higher prediction skills with higher resolution wind forcing.

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Section: Irenom-interactive Relocatable Nested Ocean Modelmentioning

confidence: 99%

A relocatable ocean model in support of environmental emergencies

et al. 2014

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“…A scientific study, based on data and model output, of the oceanographic and atmospheric conditions during the ASAP experiment is described in [94]. Strategies for the coordinated glider sampling were intended to be responsive to the dynamics of intermittent upwelling events in Monterey Bay.…”

Section: Cooperative Gliders In Asapmentioning

confidence: 99%

Cooperative Vehicle Environmental Monitoring

Leonard

2016

Springer Handbook of Ocean Engineering

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Over the last decade, methodologies for automated cooperative control of robotic vehicles have been designed, deployed and proven to provide efficient, reliable, and sustained monitoring of the uncertain and inhospitable ocean environment. Unprecedented data sets have been collected from deployments of cooperative vehicles in the field, and both real-time and post-deployment analyses have led to new understanding of the environment. This first decade of success in cooperative vehicle environmental monitoring sets the stage for new opportunities and future gain, especially as the development of cooperative control methodologies can continue to leverage ongoing technological and scientific advances in underwater communication and sensing, energy and computational efficiency, vehicle size, speed, maneuverability and cost, and ocean modeling and prediction.Indeed, the demonstrated potential of cooperative vehicle control has led to increased demand for fleets of autonomous underwater vehicles (AUVs) for use in measuring ocean physics, biology, chemistry and geology to improve understanding of natural dynamics and human-influenced changes in the marine environment. Further, methodologies for cooperative control of robotic vehicles in the ocean are readily adaptable to applications on land, in the air and in space; likewise, there is much to be learned from developments in these other domains. The recent explosion in research on networks and complex systems, including investigation of mechanisms that explain a "collective intelligence" exhibited by animal aggregations on the move, are also being leveraged to advance design of cooperative vehicle dynamics.For environmental monitoring to be successful, physical, chemical, and biological variables must be measured across a range of spatial and temporal scales; in the ocean the monitoring strategy must also contend with a harsh, three-dimensional physical space that is highly uncertain and dynamic. Small spatial and temporal scales associated with the measured variables typically make a stationary sensor array impractical because a very large number of sensors would be needed to get sufficient resolution in space and/or time. An array of mobile sensors, however, may be very well suited to such a challenge since mobility can be exploited to dynamically distribute fewer sensors according to the spatial and temporal scales.The underlying principle of cooperative control of vehicles for environmental monitoring leverages mobility of sensors and uses an interacting dynamic among the individual sensors to yield a collective behavior that performs better than the sum of the parts. If the vehicles can communicate their state or measure the relative state of others in the team, then they can cooperate and the cooperative vehicle dynamics can provide coordinated motion of the team as a whole. The resulting vehicle network functions as a dynamically reconfigurable sensor array with a capability for high performance in environmental monitoring not available at the level of individual...

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“…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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Oceanographic and atmospheric conditions on the continental shelf north of the Monterey Bay during August 2006

Cited by 28 publications

References 42 publications

A relocatable ocean model in support of environmental emergencies

A relocatable ocean model in support of environmental emergencies

Cooperative Vehicle Environmental Monitoring

Autonomous and adaptive oceanographic feature tracking on board autonomous underwater vehicles

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