Verification and training of real-time forecasting of multi-scale ocean dynamics for maritime rapid environmental assessment

Leslie, Wayne G.; Robinson, Allan R.; Haley, Patrick J.; Logutov, Oleg G.; Moreno, P.; Lermusiaux, Pierre F. J.; Coelho, Emanuel

doi:10.1016/j.jmarsys.2007.02.001

Cited by 39 publications

(12 citation statements)

References 14 publications

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“…In the Mediterranean, we had found through our exercises (Lermusiaux, 1999b;Robinson, 1999;Coelho et al, 2004;Leslie et al, 2008) that using climatological data sets is not often adequate due to seasonal and longer-time scale changes in the mean ocean fields in the region. Similar findings have been reported by Pinardi and Masetti (2000), Onken et al (2003Onken et al ( , 2008, and others.…”

Section: Initialization and Boundary Conditionsmentioning

confidence: 86%

At-sea real-time coupled four-dimensional oceanographic and acoustic forecasts during Battlespace Preparation 2007

Lam

Haley

Janmaat

et al. 2009

Journal of Marine Systems

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Systems capable of forecasting ocean properties and acoustic performance in the littoral ocean are becoming a useful capability for scientific and operational exercises. The coupling of a data-assimilative nested ocean modeling system with an acoustic propagation modeling system was carried out at sea for the first time, within the scope of Battlespace Preparation 2007 (BP07) that was part of Marine Rapid Environmental Assessment (MREA07) exercises. The littoral region for our studies was southeast of the island of Elba ( Italy) in the Tyrrhenian basin east of Corsica and Sardinia. During BP07, several vessels collected in situ ocean data, based in part on recommendations from oceanographic forecasts. The data were assimilated into a fourdimensional high-resolution ocean modeling system. Sound-speed forecasts were then used as inputs for bearing-and range-dependent acoustic propagation forecasts. Data analyses are carried out and the set-up of the coupled oceanographic-acoustic system as well as the results of its real-time use are described. A significant finding is that oceanographic variability can considerably influence acoustic propagation properties, including the probability of detection, even in this apparently quiet region around Elba. This strengthens the importance of coupling at-sea acoustic modeling to real-time ocean forecasting. Other findings include the challenges involved in downscaling basin-scale modeling systems to high-resolution littoral models, especially in the Mediterranean Sea. Due to natural changes, global human activities and present model resolutions, the assimilation of synoptic regional ocean data is recommended in the region.

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Section: Initialization and Boundary Conditionsmentioning

confidence: 86%

At-sea real-time coupled four-dimensional oceanographic and acoustic forecasts during Battlespace Preparation 2007

Lam

Haley

Janmaat

et al. 2009

Journal of Marine Systems

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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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“…is known, more powerful techniques can be used (e.g. [7][26] [27][28] [29]). On the contrary, when the covariance is unavailable, a technique like the one based on clustering and presented in [19] can be used.…”

Section: Background: Adaptive Sampling Using Clusteringmentioning

confidence: 99%

Adaptive sampling using fleets of underwater gliders in the presence of fixed buoys using a constrained clustering algorithm

Cococcioni

Lazzerini

Lermusiaux

2015

OCEANS 2015 - Genova

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Abstract-This paper presents a novel way to approach the problem of how to adaptively sample the ocean using fleets of underwater gliders. The technique is particularly suited for those situations where the covariance of the field to sample is unknown or unreliable but some information on the variance is known. The proposed algorithm, which is a variant of the well-known fuzzy C-means clustering algorithm, is able to exploit the presence of non-maneuverable assets, such as fixed buoys. We modified the fuzzy C-means optimization problem statement by including additional constraints. Then we provided an algorithmic solution to the new, constrained problem.

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Verification and training of real-time forecasting of multi-scale ocean dynamics for maritime rapid environmental assessment

Cited by 39 publications

References 14 publications

At-sea real-time coupled four-dimensional oceanographic and acoustic forecasts during Battlespace Preparation 2007

At-sea real-time coupled four-dimensional oceanographic and acoustic forecasts during Battlespace Preparation 2007

Autonomous and adaptive oceanographic feature tracking on board autonomous underwater vehicles

Adaptive sampling using fleets of underwater gliders in the presence of fixed buoys using a constrained clustering algorithm

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