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This page last modified 11 September 2009
Physical and Interdisciplinary Regional Ocean Dynamics and Modeling Systems
This research is concerned with the fundamental understanding and modeling of complex physical, acoustical and biogeochemical oceanic dynamics and processes. New mathematical models and computational methods are created, developed and utilized for: i) ocean predictions and dynamical diagnostics, ii) data assimilation and data-model comparisons, and, iii) optimization and control of autonomous ocean observation systems. The regional dynamics involves interactions of sub-mesoscale and mesoscale ocean processes in the littoral as well as effects from large-scale processes in ocean basins. Such interactions and feedbacks with scales smaller and larger than the mesoscale need be better quantified. The technical approach is rooted in the comparison and optimal combination of measurements and models via nonlinear data assimilation (DA), including the development of adaptive modeling and adaptive sampling schemes based on Error Subspace Statistical Estimation. Our research group is updating and renewing our previous approaches and computational schemes and systems. We will keep and modernize the strengths of our methods and codes, but we will also progressively utilize other ocean dynamical models, or parts thereof, and explore novel numerical systems.
The research topics that are specific to the present effort include:
- Three-dimensional (3D) acoustic modeling coupled with high-resolution 4D physics modeling
- Ocean modeling incubator: structured and unstructured grids; investigations and evaluations of the next generation of numerical schemes for physical, acoustical and biological dynamics
- Interactions of internal tides/waves and mixing processes with mesoscale dynamics, their high-resolution modeling and multi-scale diagnostics
- Lagrangian coherent structures and ocean features: their prediction, dynamics and assimilation
- Nonlinear DA and adaptive DA, including (super)-tidal constraints and assimilation
- Use of several ocean models, model uncertainty estimation, and multi-model fusion and DA
General objectives are to:
- Analyze and study regional physical and acoustical-physical-biogeochemical dynamics
- Incubate and develop new numerical modeling systems, including next generation ocean physics, 3D acoustics and Lagrangian coherent structures predictions
- Update existing and create new nonlinear and adaptive assimilation schemes and systems, including parameter estimation
- Evolve concepts and determine methodologies for regional adaptive modeling and adaptive sampling with the intent to increase predictive capabilities
- Quantify regional predictabilities and improve probability and uncertainty modeling
- Utilize several ocean models, estimate their uncertainty statistics and fuse their estimates
An emphasis is on acoustical-physical interactions in 3D space and time, and on acoustical-biogeochemical-physical estimation. The investigations are generic but the focus is on specific ocean regions: the Mid-Atlantic Bight (MAB) and Shelfbreak Front region, the Chinese-Taiwanese Seas and Philippine Seas; the Monterey Bay and California Current System (CCS) region, the Massachusetts Bay/New England shelf region, and the Mediterranean and Black Seas. Several of these regions have been or are investigated under other collaborative efforts, some of which sponsored by the Office of Naval Research.
Interdisciplinary Modeling and Dynamics of Archipelago Straits
This research is concerned with interdisciplinary modeling, data assimilation and dynamical studies in the Straits regions of the Philippines Archipelago. The general focus is to better understand, model and predict sub-mesoscale and mesoscale physical and biogeochemical dynamics in sea straits. The technical approach is based on interdisciplinary data assimilation using the Error Subspace Statistical Estimation scheme, quantitative model evaluation and selection through adaptive modeling, and sensitivity and dynamical process studies. The work and its results are expected to contribute to navy operations including the surveillance of transit routes, safety of man-based activities, management of autonomous vehicles, and overall tactical and strategic decision making under uncertainties in sensitive regions.
Better understand, model and predict interactive dynamics and variability of sub-mesoscale and mesoscale features and processes in Straits regions and their impacts on local ecosystems through (i) interdisciplinary physical-biogeochemical-acoustical data assimilation of novel multidisciplinary observations, (ii) adaptive, multi-scale physical and biogeochemical modeling, and (iii) process and sensitivity studies based on a hierarchy of simplified simulations and focused modeling.
Specific objectives are to:
- Utilize and develop the Error Subspace Statistical Estimation (ESSE) system for interdisciplinary data assimilation and uncertainty estimation with the physical Primitive-Equation (PE) and generalized biogeochemical model of the Harvard Ocean Prediction System (HOPS)
- Study, describe and model the variability and dynamics of flow separations and associated eddies and filaments, of water mass evolutions and pathways, and of locally trapped waves
- Develop and implement schemes for parameter estimation and selection of model structures and parameterizations, and for high-resolution nested domains towards non-hydrostatic modeling
This five-year research plan (including two optional years) is expected to contribute to coastal physical and biogeochemical oceanography in general and dynamics of Straits in particular. Such research will increase capabilities of navy operations in these regions, especially the surveillance of transit routes, safety of man-based activities, management of autonomous vehicles, and overall tactical and strategic decision making under uncertainties in sensitive areas.
Adaptive Sampling and Prediction (ASAP)
Optimal Asset Distribution for Environmental Assessment and Forecasting Based on Observations, Adaptive Sampling, and Numerical Prediction (ASAP)
The recent proliferation of unmanned air and undersea vehicles has spawned a research issue of pressing importance, namely: How does one deploy, direct and utilize these vehicles most efficiently to sample the ocean, assimilate the data into numerical models in real or near-real time, and predict future conditions with minimal error? A corollary to this central issue would be: What constitutes the minimal necessary and sufficient suite of vehicles required to constrain the models and provide accurate ocean forecasts? Implementation of an appropriate sampling plan requires an assessment of the initial oceanographic situation, understanding the capabilities and limitations of individual vehicles, vehicle coordination and control, and numerical models equipped to assimilate and utilize data which were irregularly sampled in space and time.
The Adaptive Sampling and Prediction (ASAP) program proposed here will directly address the questions above with a focused research effort in and around the Monterey Bay, California. A combination of gliders, propeller-driven AUVs, research aircraft, and ships will be used to adaptively sample three-dimensional upwelling and relaxation processes off Point A.no Nuevo at the north entrance to the bay. Quantitative metrics have been defined to guide the adaptive sampling scheme, including a coverage metric for minimizing synoptic error, a dynamic variability metric for maximizing sampling of important physical phenomena, and an uncertainty metric. A modular approach allows metric optimization via cueing on several different measures of ocean variability: a) synoptic observational error minimization using coordinated control; b) feature tracking; c) maximizing the skill of the Error Subspace Statistical Estimation (ESSE) forecast from the Harvard Ocean Model; d) optimal assessment of the ocean acoustic propagation environment; and e) efficient glider navigation using Lagrangian Coherent Structures (LCS).
The unifying scientific goal of the ASAP experiment will be to construct a volume and heat budget for the three-dimensional upwelling center off Point A.no Nuevo, CA during upwelling, relaxation, and transition events. The centerpiece of the initial three-year effort will be a month-long field program in the Monterey Bay during June 2006, a month when several events and transitions can be captured. A second major experiment is planned in the Monterey Bay during June 2008. The program will be executed by a multi-disciplinary team consisting of physical oceanographers, marine acousticians, control systems engineers, and numerical modelers. The operational principals thus derived are portable and relevant to a wide variety of space and time scales. The expected project outcome is superior sampling strategies for AUVs of all types, improved data assimilation, and improved model forecast skill, resulting in the most efficient use of these vehicles in operational scenarios. DoD sectors to reap these benefits include mine intervention warfare, expeditionary warfare, undersea warfare, and marine survey.
We plan to: a) Perform real-time nested HOPS/ESSE (sub)-mesoscale field and uncertainty predictions and physical-acoustical data assimilation with quantitative adaptive sampling integrating 3 metrics and linking to LCS and real-time glider models. b) Develop theory and software for momentum, heat and mass budgets on multiple-scales with uncertainties, allowing for time-dependent volumes to account for evolution of plume and boundary layer effects. c) Perform science-focused sensitivity simulations under different atmospheric conditions to quantify effects of atmospheric resolution, surface and bottom BL formulations, idealized geometries on plume formation and relaxation. d) Evaluate predictive capability limits and predictability limits for upwelling and relaxation processes, improving model parameterizations based on data-model misfit and theory and software for measuring skill of upwelling plume forecast (size of plume, scales of jet and eddies at plume edges, thickness of boundary layers and surface and bottom fluxes). e) Develop new ESSE nonlinear adaptive sampling scheme for identifying future regions in most need of sampling based on a tree-structured multi-ensemble prediction, with error models for glider/AUV/ship/aircraft data (with WHOI/Scripps/MIT/NPS) and predictions of data. f) Investigate adaptive bottom and surface boundary layers with distributed GRID software, non-hydrostatic computations, theoretical upwelling and relaxation dynamics research, physical-biogeochemical balances and inter-annual variability.
Quantifying, Predicting and Exploiting Environmental and Acoustic Fields and Uncertainties
The long-term goal is to: Research, integrate, demonstrate and utilize end-to-end prediction and DA systems to better study, understand, forecast and exploit environmental and acoustic fields and uncertainties for efficient sonar operations.
Specific objectives for the first and last four years of the DRI are to:
- Improve the understanding of dynamics, predictabilities and uncertainties in the southern East China Sea (ECS) and Northern Philippine Sea region
- Study, model and quantify the interactions of Kuroshio meanders, mesoscale features and internal tides and waves, based on process and sensitivity studies
- Further research coupled environmental and acoustic modeling; assimilate ocean physics, acoustic and seabed data; and, utilize data-model misfits to improve the corresponding models
- Link the regional modeling effort to larger-scale modeling, including the use of acoustic measurements in deep waters
- Design observation system properties and adaptive sampling schemes to optimize the placement of sensor systems for the reduction of uncertainty and best exploitation of the environment
- Collaborate with the team selected by ONR and provide guidance for the planning of the DRI
End-to-end Prediction and DA Systems and their Uncertainties. An important component of the proposed DRI will involve the research, integration, demonstration and utilization of end-to-end prediction and DA systems for efficient sonar operations. In collaborations with the team selected, we plan to further research and integrate the following components of such systems: ocean physics models (the free-surface model of HOPS and if possible, the MIT-gcm and ROMS models), acoustic models (NPS model and RAM), coupling schemes for these water-column and acoustic models, and the corresponding DA systems.
In collaboration with the other selected investigators, we plan to extend our modeling experience and ESSE data assimilation system to seabed and signal-to-noise-ratio (SNR) modeling and assimilation, for fully coupled ocean-physics-acoustic-seabed-SNR estimations. The accounting of all system uncertainties including those of the ocean and bottom environment, and of the sonar equations, will need to be accurate enough for successful end-to-end estimations (Lermusiaux, 2006a). The uncertainty estimates computed by the DA systems will be evaluated by statistical analyses and comparison to data-forecast misfits. Interesting research involves the theoretical modeling and estimation of uncertainties for idealized systems. Such idealized research is necessary for determining the accurate representation and transfer of uncertainties across the various disciplines. With such understanding, more complex and realistic cases can be investigated.Ocean Dynamics, Features and Predictability. We plan to study, model and quantify ocean dynamics and features in the East China Sea (ECS) and Northern Philippine Sea region, with emphases on oceanic events that are acoustically important. Processes of interests include interactions of the meandering Kuroshio with shelf dynamics and topographic features (entrainment, encircling of ECS waters, eddying, etc) and interactions of mesoscales with internal tides and waves in the ECS.
Autonomous Wide Aperture Cluster for Surveillance (AWACS)
Goal: Improve modeling of ocean dynamics, and develop and
evaluate new adaptive sampling and search methodologies, for the
environments in which the main AWACS-06, -07 and -09 experiments
will occur, using the re-configurable REMUS cluster and coupled
data assimilation Specific objectives are to:
- Evaluate current methods and develop new algorithms for adaptive
environmental-acoustical sampling, search and coupled DA techniques
(Stage 1), based on a re-configurable REMUS cluster and on
idealized and realistic simulations (with NPS/OASIS/Duke)
- Research optimal REMUS configurations for the sampling of
interactions of the oceanic mesoscale with inertial oscillations,
internal tides and boundary layers (with WHOI/NPS/OASIS)
- Develop new adaptive ocean model parameterizations for specific
AWACS-06, -07 and -09 processes, and compare these regional
dynamics (with WHOI)
- Provide near real-time fields and uncertainties in AWACS-06, -07
and -09 experiments and, in the final 2 years, develop algorithms
for fully-coupled physical-acoustical DA among relocatable nested
3D physical and 2D acoustical domains
- Provide adaptive sampling guidance for array performance and surveillance (Stage 2), and link HU research with vehicle models and command and control.
Persistent Littoral Undersea Surveillance - Simulation, Estimation, and Assimilation Systems
Kauai 2009 page [Password Protected]
Research page [Password Protected]
Autonomous Marine Intelligent Swarming Systems for Interdisciplinary Observing Networks (A-MISSION)
Today, the number of autonomous platforms used in semi-coordinated operations is often larger than 10 and this number is rapidly increasing. It is imperative to advance the interdisciplinary science of autonomy to take full advantage of these new capabilities and so maintain our leading scientific and engineering edge. We propose to research novel fundamental formalisms and principled methodologies for optimal sensing using heterogeneous and collaborative swarms of autonomous platforms that are smart. Intelligence here refers to the ability to compute and autonomously adapt an optimal sensing plan based on the predicted environment and acoustic performance and their uncertainties, and on the predicted effects of environmental and acoustic sensing on future operations. Our approach is generic and applicable to any Naval swarms that move and sense large-dimension dynamics fields, but our focus is underwater sensing with a range of platforms including AUVs, gliders, ships, moorings and remote sensing.
When compared to other control problems of large dimensions, the differences with our problem are that: (i) naval platforms are heterogeneous and their data are gappy but multivariate; (ii) marine fields are dynamic on multiple-scales and have very large dimensions, but are predictable to some degree; and (iii), the measurements to be collected will affect these future predictions. Therefore, there are feedbacks between optimal sensing and predicting, in time and space, and across variables. We will use guidance from ocean-acoustic modeling, dynamical system theory, uncertainty prediction, decision-making under uncertainty, artificial intelligence, bio-inspired algorithms with emergence of global properties, and distributed computing. In all cases, we are interested in the global swarms and high-level optimization, not the detailed control of a single robot. A global objective function defines the optimal dynamic and collaborative autonomy. In our case, objective functions depend on the predicted environment, on the predicted values and positions of the expected data, and on the feedbacks between data and dynamics.
We will research autonomous sensing swarms and formations that exploit the multi-scale, multivariate, four-dimensional environmental-acoustic dynamics and predictabilities. We will develop new global swarm patterns and high-level optimization schemes based on control theory and dynamical system theory (e.g. artificial potential functions, nonlinear contraction analysis), artificial intelligence (e.g. hybrid evolutionary optimization, particle swarm optimization and reinforcement learning) and bio-inspired behaviors (e.g. distributed flocking/swarming, ant algorithms). From the methods explored, a small subset of candidate schemes will be selected based on accuracy, robustness, predictability and generality. These selected schemes will be augmented to learn from (i) environmental forecasts and their ssociated uncertainties and (ii) the projected impact of the sensing on the forecasts. The novel methods that result will be developed with diverse marine regimes in mind. Incremental testing of the algorithms will be accomplished using idealized simulations. System tests will use hindcasts from our extensive set of at-sea exercises, followed by two in situ engineering trials and a real-time exercise.
Ocean Observatories Initiative (OOI)
The Ocean Observatories Initiative (OOI) is a NSF Division of Ocean Sciences program that focuses the science, technology, education and outreach of an emerging network of science driven ocean observing systems.
The core capabilities and the principal objectives of ocean observatories are collecting real-time data, analyzing data and modeling the ocean on multiple scales, and enabling adaptive experimentation within the ocean. A traditional data-centric CI, in which a central data management system ingests data and serves them to users on a query basis, is not sufficient to accomplish the range of tasks ocean scientists will engage in when the OOI is implemented. Instead, a highly distributed set of capabilities are required that allow:
- end-to-end data preservation and access,
- end-to-end, human-to-machine and machine-to-machine control of how data are collected and analyzed,
- direct, closed loop interaction of models with the data acquisition process,
- virtual collaborations created on demand to drive data-model coupling and share ocean observatory resources (e.g., instruments, networks, computing, storage and workflows),
- end-to-end preservation of the ocean observatory process and its outcomes, and
- automation of the planning and prosecution of observational programs.