This project, under lead PI Eric Chassignet (FSU), aims to achieve measurable and significant improvements in short- to medium-range (1- to 10-day), subseasonal, and long-range (3- to 6-month) prediction skill of sustained continuous operational forecasts of Gulf of Mexico (GoM) ocean dynamics. The specific objectives are to i) Improve the prediction skill for Gulf of Mexico operational forecast models run within NOAA, U.S. Navy, and industry environments by increasing and enhancing model and DA capabilities and by maximizing the use of existing and new near-real-time observations, including those adaptively deployed; ii) Develop new derived products and tools from observations and numerical models to provide skillful operational forecasts and knowledge of full-water-column currents (including upper ocean circulation and near-bottom currents); and iii) Apply improvements in hydrodynamic forecasting capabilities to hurricane forecasting and fisheries management.

In this research we combine of probabilistic ocean modeling and control approaches such as reachability with machine learning for optimal path-planning algorithms in strong uncertain dynamic fields. Research tasks include: (1) Realistic Data-driven Ocean and Platform Modeling and Simulation; (2) Optimal Path Planning with High-Dimensional Reachability, Plan Adaptation and Learning; (3) Learning a Final Value Function for long-term Anticipation and Deep Reinforcement Learning Algorithms.

The primary goal of this project is to enable effective use of ocean forecast data on forward operating platforms with limited communications and computing resources. The aim is to mature and assess promising approaches to this problem for subsequent investment leading to transition of the developed capabilities to operations. Our MIT-MSEAS research will consist of four research thrusts involving reduced order modeling (ROM): i) decomposition of deterministic and probabilistic forecasts for efficient compression, reduction and reconstruction; ii) machine learning for reduced-order modeling and forecasting; iii) adaptive and data-assimilative reduced-order modeling and forecasting; and iv) multi-resolution and multi-dynamics ROMs.

The deep sea is currently at the heart of the last remaining, big knowledge frontier. Information about the deep sea is critical to close the loop from oceans to near space. The breath of scales is large, but the challenges are mostly technological. Inhospitable and extreme environments are the obstacle, where the rules are radically different. For this project, the MSEAS team will develop and apply its expertise in multiscale physical-biogeochemical-acoustical ocean modeling and data assimilation, Bayesian inference and scientific machine learning, and principled optimal planning for coordinated fleets of AUVs, surface autonomous vehicles, UAVs and other aircrafts, and near space assets.

For intelligent ocean exploration and sustainable ocean utilization, the need for smart autonomous underwater vehicles, surface craft, and small aircraft is rapidly increasing. Applications include scientific studies, solar-wind-wave energy harvesting, transport and distribution of goods, naval operations, security, acoustic surveillance, communication, search and rescue, marine pollution, ocean cleanup, conservation, fisheries, aquaculture, mining, and monitoring and forecasting. Designing optimal paths leads to cost savings, longer operational time, and environmental protection. Our goal is to develop and apply our optimal planning theory and methodology to increase the efficiency of surface craft and underwater vehicles operating in uncertain dynamic ocean conditions. For the first time, we combine environmental forecasting with stochastic control and risk theory, and employ fundamental partial-differential-equations (PDEs) and efficient level-set solutions for exact reachability and path planning. Our novel proposed ocean applications include energy-optimal path planning, optimal environment harvesting, optimal cleanup, and information-optimal exploration and Bayesian machine learning.

Our long-term goal is to obtain machine-intelligent modeling systems that seamlessly integrate stochastic ocean dynamical models and their multi-fidelity representations with Bayesian and generative learning from data-model misfits, to construct improved ocean models with more accurate parameterizations and discover invariances or differential equations, over a range of spatial and temporal scales. We will use and vastly extend stochastic dynamic reduced-order methods, Bayesian GPs, adaptive DL schemes, and generative networks. Ultimately, our symbolic interpretation of ML models into emergent dynamical and constitutive relations would further compress knowledge relative to deep networks, thus extending outside the range of the training data.

The main goal for our project is to further develop, implement, apply, and validate theory, algorithms, and computational schemes for dynamic environmental estimation, prediction, and acoustic inference (DEEP-AI). The specific research thrusts are to: (i) Predict and characterize underwater sound propagation PDFs due to the uncertain ocean oceanographic, bathymetry, and seabed fields, (ii) Assimilate the sparse acoustic and oceanographic data using multivariate principled Bayesian inversion and estimation of ocean oceanographic, acoustic, bathymetry, and seabed fields, (iii) Learn and discover acoustic parameterizations, model improvements, new processes, and most informative observation needs using new deep machine learning and Bayesian learning, and (iv) Develop efficient computational methods for the above prediction, assimilation, and learning.

Our long-term goal is to: generalize, develop, and implement our stochastic dynamically-orthogonal decompositions and nonlinear Bayesian filtering and smoothing schemes forprincipled probabilistic predictions and predictability studies of physical-acoustical-biogeochemical-sea-ice dynamics, and for interdisciplinary nonlinear Bayesian data assimilation, adaptive sampling, and quantification of observation needs for naval operations.

We plan to collaborate with Applied Ocean Sciences (AOS) to help designing and delivering a compact system to assess local uncertainties and track the evolution of the maritime environment around unmanned platforms at sea. Such a system can run both at control centers and on-board Underwater and Surface Unmanned Vehicles (UUV/SUV) under different network bandwidth constraints. The system uses the Navy ocean forecasts for initial environmental guesses and outlooks up to 2 weeks (or more in future generations) and then implements a Reduced Order Model (ROM) derived from Dynamically Orthogonal (DO) solutions to deliver a local uncertainty picture (for the next 24-48 hours). The ROM-DO solutions will target the variables and parameters of relevance for the UUV/SUV fleet missions planning and execution. These solutions are using a set of dynamic modes from which the reduced order estimates for the parameters and variables of interest are computed. They are then integrated with the local network data collected during the past days-hours, using a non-intrusive filter, and deliver an updated local forecast for the next 12-24 hour. The new fields are then used to compute marginal and conditional probability distributions of pre-loaded dynamical functions/modes that are sent to the forward deployed platforms. These probabilities are then integrated in dedicated payloads with the platform sensor data in real-time to locally reconstruct and update the most likely environments for the next 1-12 hours. This will assure best fits to the in-situ data and sensor performance observations and updates the forecasts around the platforms. These solutions can be used for path optimization and environmental adaptation/adaptive sampling, assuming operators and/or on-board middleware software can then specify a decision point for choosing the path based on mission parameters.

Understanding the scale of deep-sea mining’s effects is paramount to understand their ecosystem consequences. There is no doubt that deep-sea mining will kill organisms and permanently alter habitats (Drazen et al., submitted). Seafloor disturbance studies clearly show that benthic communities don’t fully recover even after several decades (Jones et al., 2017). Further, much of the fauna relies on the nodules themselves (Amon et al., 2016; Vanreusel et al., 2016) which take millions of years to form. These effects over hundreds of meters to a few kilometers may not comprise a substantial ecosystem impact given the scale of abyssal habitats, but effects over hundreds to thousands of kilometers would surely be defined as serious environmental harm (Levin et al., 2016). Thus the spatial and temporal scales of mining impacts will decide policies and decisions about deep-sea mining activities. There is a clear need to use sediment plume models to estimate the scale of mining impacts in the midwater realm.

The matter of ocean forecasting and route planning is an important aspect of maximizing the value of the Autonomous Tow Vessels for Offshore Macroalgae Farming developed C.A. Goudey & Associates. For this challenging project, we apply and further develop our experience with the optimization of vehicle trajectories in dynamic flow fields and in forecasting such flows and optimal trajectories. Our MSEAS group research has an emphasis on the following support: (1) Examine the importance of route optimization in Drone Tug applications in local tidal and coastal flows based on speed and maneuvering abilities; (2) Consultations on Drone Tug evaluation and applications with a focus on operations in the vicinity of Woods Hole and Nantucket Sound; (3) Support of Drone Tug demonstrations at Nantucket Sound kelp farm.

Since the 19th and early-20th century, plastics have become ubiquitous in the world. Plastics have outgrown most man-made materials: their global volume production has surpassed that of steel production in the late 1980s (Fernandez et al., 2018). Plastic production continues to increase and undesirable impacts from plastic pollution have proliferated throughout the world, in our lands, rivers and oceans, as well as in animals and human foods. It is time for the world to solve this problem. Banning plastics is not sufficient nor immediately practical. Just as for CFCs, we need to engineer alternatives, but in the plastic case, we also need to clean the environment due to the long plastic lifetimes. Some of the needs include: design and manufacture plastic alternatives for varied applications, from packaging to automotive and fishing; understand, model, and forecast plastic transports and dispersion in our estuaries and oceans, combining fundamental dynamics with uncertainty quantification, data assimilation, and machine learning; develop and build intelligent autonomous robots for optimized plastic sensing and cleaning, on land and at sea; harvest energy or other useful by-products from plastic waste without new pollution; integrate all of these systems into practical world and regional solutions. The MIT Environmental Solutions Initiative (MIT-ESI) aims to tackle this challenge through the expertise of our interdisciplinary faculty, ranging from materials, to manufacturing and design, to smart sensing and advanced computational modeling and data-driven learning. Through our approaches and collaborations, our long-term goal is to develop a plastic free environment that will ensure the health of our planet.

Our primary goal is to further develop, implement, apply, and validate theory and schemes for a prototype Wide Area Ocean Floor Mapping system. Our specific objectives are to: (i) Characterize and predict underwater sound propagation uncertainty/distributions due to inexact bathymetry fields and ocean environmental and seabed uncertainties, and (ii) assimilate limited acoustic and oceanographic data for the joint principled Bayesian inversion of environmental and acoustical fields, and for the corresponding rigorous estimation of bathymetry.

Our research aims to address the outstanding questions surrounding deep-sea mining sediment plumes as this underpins all understanding and prediction of the biological response to deep-sea mining activities, and furthermore provides the foundation for the design of marine protected areas and other regulations. Limited resources have been available to understand this issue, which policymakers have identified as being of urgent need. Over the past fifteen years, only two sets of model results have been obtained (one from a European researcher and another from a European contractor) and they differ significantly in their predictions.

We plan to develop state-of-the-art numerical models of sediment plume transport that can be applied to regions such as the Clarion-Clipperton Fracture Zone (CCFZ), the mineral-rich and biodiverse region between Hawaii and Mexico in the Pacific Ocean where the majority of mining claims have been made, and/or the Exclusive Economic Zone (EEZ) of the Cook Islands, as they are currently the most active small island nation considering the possibility of deep-sea mining.

The overarching goal of this project is to develop and demonstrate principled Bayesian intelligent ocean modeling and acidification prediction systems that discriminate among and infer new ocean acidification (OA) models, rigorously learning from data-model misfits and accounting for uncertainties, so as better monitor, predict, and characterize OA over time-scales of days to months in the Massachusetts Bay and Stellwagen Bank region.

This project will develop Bayesian data-driven estimation and model learning methods, stochastic forecasts, and analysis products for ocean physics, biogeochemistry and fisheries. Our data-assimilative ocean field and uncertainty estimates, optimal data collection guidance, and coastal ecosystem-based scenario and risk analyses will serve as quantitative technical decision aides for sustainable rights-based fisheries management. Our forecasts have diverse local commercial and societal applications involving not only fisheries but also coastal zone management, pollution mitigation, monitoring, ocean engineering, tourism, shipping, financial hedging, and re-insurance. In addition to educating students, we also collaborate with colleagues from IISc Bangalore (Prof. Deepak N. Subramani), governmental and non-governmental agencies to transfer our technology and train the users.

The overarching goal of this collaborative project is to achieve greater understanding of the physical processes that control the circulation in the Gulf of Mexico, in particular the Loop Current and Loop Current eddy separation dynamics, through advanced data assimilative modeling and analyses. Our MSEAS group plans to: contribute to the skill assessment and analysis of the operational modeling systems; complete targeted multi-resolution modeling experiments to study the effect of model resolution, initial and boundary conditions; illustrate our capabilities of probabilistic predictability and process analyses to quantify variability due to initial/boundary conditions, predictability limits, predictive capabilities, and mutual information and causality metrics between Gulf and LC variables; and, predict the information content and impacts of observations for principled observational campaign design.

Describing and quantifying the truly three-dimensional and time-dependent transports of ocean properties from the surface ocean to the interior is a fascinating observational, theoretical, and modeling challenge. The CALYPSO initiative addresses this challenge, with a focus on the southwest Mediterranean Sea region. Our goal is to develop novel efficient four-dimensional Lagrangian analysis theory and methods, and apply and expand our capabilities in multi-resolution multi-disciplinary ocean modeling, uncertainty, predictability, and Lagrangian–Eulerian data assimilation, to predict and characterize multiscale ocean transports, coherent structures, and subduction/stirring/mixing processes, and optimally guide ocean platforms towards the most informative observations.

The long-term MIT-MSEAS goals of the two phases of the S-DUCT effort are to (i) employ and develop our high-resolution MSEAS modeling system in ocean regimes with surface ducts to provide high-fidelity non-hydrostatic/hydrostatic sound speed fields for acoustic studies, (ii) develop and utilize our coupled oceanographic-acoustic probabilistic modeling based on rigorous Dynamically-Orthogonal (DO) differential equations for the stochastic characterization of surface ducts, (iii) employ and advance our coupled Bayesian data assimilation (GMM-DO filter and smoother) for the joint dynamic ocean-physics-acoustics inversion and next-generation tomography, (iv) apply our theory and schemes based on Bayesian mutual information fields to predict the placement of assets that maximize information and optimize the probability of detection, and (v) quantify the sound speed and transmission loss variability in surface duct regions (e.g. mixed-layer depth variations, internal wave effects scattering acoustic energy out of the surface duct, etc.) and investigate models of such effects that are useful for naval applications.

Our research focus will be the Lagrangian connectivity of marine ecosystems and sea hazards due to natural or made-made Lagrangian material transports. Specifically, we plan to further develop and apply our new theory and schemes for (i) the study and quantification of biogeochemical coherent structures and Lagrangian connectivity of marine ecosystems, and (ii) the study and mitigation of sea hazards due to stochastic advection and Lagrangian material transports including marine contaminations and spills. To do so, we will employ and improve our schemes and computational systems for probabilistic ocean physical-biogeochemical modeling and forecasting, for predicting Lagrangian Coherent Structures and their uncertainties, and for Bayesian nonlinear Eulerian and Lagrangian data assimilation. We will build upon our experiences, especially those that involved large and collaborative research. Several of our recent MIT-MSEAS methods and software will be used and further developed for the Red Sea Initiative. Our long-term approach is to utilize these known information structures, for robust and accurate Bayesian forecasting, Lagrangian transport studies, data assimilation, and optimal planning of ocean sensing.

Hazards due to the fundamental process of advection of natural and anthropogenic material in environmental flows are ubiquitous and profoundly impact society; preparedness and effective response can save many lives, untold environmental damage and enormous financial cost. Recent catastrophic examples include: oil spill advection during the Deep Water Horizon disaster, the passage of the ash cloud from the Eyjafjallajökull volcano through commercial air space, and the trail of radioactive waste from the Fukushima reactor disaster. On a day-to-day operational level, search-and-rescue operations at sea rely critically on correctly modeling and interpreting flow transport in order to inform life-or-death decisions. Understanding how flow transport is organized and predicting where things go in complex environmental flows remains a formidable scientific challenge, however, due to unsteady nonlinear and multiscale flows, ambiguities in defining material transport, multiple sources of uncertainty, the difficulty of identifying and acquiring pertinent data to assimilate into models, the variability of predictions across different models, and the complexity of analyzing vast data sets and visually representing the results. The NSF-ALPHA (Advanced Lagrangian Predictions for Hazards Assessments) team (MIT, WHOI, Virginia Tech and UC Berkeley) proposes transformational progress in tackling these science issues by exploiting and advancing recent fundamental breakthroughs in four-dimensional (3D plus time) Lagrangian methods. An integrated theoretical, computational and observational approach will be employed to develop, implement and utilize these cutting-edge Lagrangian methods with data-driven modeling for the purpose of uncovering, quantifying and predicting key transport processes and structures during regional flow-based hazards in the ocean and atmosphere. The challenges are broad in scope, encompassing engineering, geosciences, applied mathematics, and computational and information science and engineering. The overall goal of the ALPHA project is to employ an integrated theoretical, computational and observational approach to develop, implement and utilize these cutting-edge Lagrangian methods with data-driven modeling for the purpose of uncovering, quantifying and predicting key transport processes and four-dimensional (3D plus time) structures during regional flow-based hazards in the ocean and atmosphere.

The presence of large gradients often renders the quantitative analysis of dynamical systems challenging, be the analysis theoretical, observational or computational. This is because large gradients commonly lead to strong nonlinearities and to coupling among state variables and parameters. The emphasis of the Flow Encountering Abrupt Topography (FLEAT) initiative is on the effects of large topographic gradients and complex subsurface geometry on major current systems. First, the processes involved in these strong topographic interactions are not yet well known. Their consequences, including alteration of circulation features, spawning of internal waves and vortices, and formation of unstable downslope flows and gravity currents, require novel integrated analyses. Second, major ocean ridges and archipelagos and islands are not properly represented in larger-scale modeling systems, and novel downscaling and two-way nesting schemes need to be utilized, developed and evaluated with real ocean data. This set of research activities is the emphasis of our FLEAT research project.

The focus of this project is to kick-start an initiative at MIT to assess the impacts of deep-sea mining by developing a high fidelity, regional, physical-biogeochemical oceanographic model of the Bismarck Sea. The latest Lagrangian data processing and nonlinear dynamical systems tools will be used to understand three-dimensional flow transport in the region, in order to provide a clearer understanding of the fate of material released by the mining activities at different depths and locations. The project brings together three MIT faculty with complementary expertise in numerical ocean modeling, dynamical systems methods for flow transport, and modeling of ocean-biological systems. We consider this to be a nucleus of a team that, based on the outcomes of this project, can grow over the next few years to encompass a wider scope, and involve other researchers at MIT and WHOI. The tools we implement and develop can be applied to assess the environmental impact at any proposed location for the growing field of deep-sea mining.

The primary goals of our MIT effort are to: (i) employ and develop our regional ocean modeling, data assimilation and uncertainty quantification for the estimation of sound speed variability, coupled oceanographic-acoustic forecasting and inversion relevant to the POINT effort; (ii) apply our theory and schemes for optimal placement, path planning and persistent ocean sampling with swarms of ocean drifters and other acoustic source platforms; and (iii), further quantify the ocean dynamics and variability of the regional areas of interest, utilizing our multi-resolution data-assimilative ocean modeling and process studies.

The research thrust is on developing and providing ocean physical and biogeochemical forecasting products and technologies for coastal fisheries management in India. Our modeling systems will provide ocean field and uncertainty estimates, optimal sensing guidance, coastal ecosystem-based scenario analyses, and technical decision aides. We also focus on building practical systems and products that are tailored to the Indian context and usable for local commercial and societal applications involving fisheries but also coastal zone management, monitoring, ocean engineering, financial hedging, and re-insurance.

One of our research thrusts is to derive and apply advanced techniques for multiscale modeling of tidal-to-mesoscale processes over regional domains (nearshore-coastal-basin) with complex geometries including shallow seas with strong tides, steep shelfbreaks with fronts, and deep ocean interactions. On the one hand, our conservative implicit two-way nesting for realistic multi-resolution modeling has enabled such high-fidelity coupled multiscale dynamics studies. On the other hand, a high-order multi-dynamics modeling capability based on novel hybridizable discontinuous Galerkin (HDG) numerical schemes is also promising for seamless conservative multi-resolution forecasting. These two research topics are the backbones of our National Oceanographic Partnership Program (NOPP) research project.

Today, the number of autonomous platforms used in semi-coordinated sea operations can be larger than 10 and this number is increasing. This new paradigm in ocean science and operations calls for investigations as those envisioned by the Northern Arabian Sea Circulation – autonomous research (NASCar) initiative. The need for clever autonomous observing and prediction systems is especially acute in the NASCar region due to the frequent pirate activities and the relative paucity of in situ observations.

Our long-term goal is to develop autonomy for AUVs to enable intercept and proximity operations with underway surface vessels. In collaboration with the Lincoln Lab researchers and personnel, and other MIT PIs our specific objectives are to:

• Model and simulate elements of surface ship encounter and develop required AUV autonomy
• Perform flow modeling and autonomy development, and leverage experiments of opportunity and existing assets for in-water demonstrations.

The long-term goal is this project to develop and apply theory, schemes and computational systems for rigorous Bayesian nonlinear assimilation of Eulerian and Lagrangian coastal flow data, fully exploiting nonlinear governing equations and mutual information structures inherent to coastal ocean dynamical systems and optimally inferring multiscale coastal ocean fields for quantitative scientific studies and efficient naval operations. The motivation is to exploit the information provided by coastal platforms (drifters, floats, gliders, AUVs or HF-radars) so as to best augment the limited resolution and accuracy of satellite data in coastal regions and to determine coastal sampling needs for successful Bayesian field estimation in diverse coastal regimes. Our aim is not to shy away from the known nonlinearities and unstationary heterogeneous statistics, but to utilize these known information structures, for robust and accurate Bayesian estimation.

In the ocean domain, opportunities for a paradigm shift in the science of autonomy involve fundamental theory, rigorous methods and efficient computations for autonomous systems that collect information, learn, collaborate and make decisions under uncertainty, all in optimal integrated fashion and over long duration, persistently adapting to and utilizing the ocean environment. The corresponding basic research is the emphasis of the present project. As humans, we often combine what we learn over time, and then make informed decisions and complete desired and new tasks. The learning over time, or backward and forward inference, is critical for long-term autonomy, especially in complex nonlinear settings that are ubiquitous in the ocean domain. Mathematically, all initial and acquired information, from both models and data, should be integrated in the form of posterior marginals of the variables of interest, while respecting nonlinearities. The accurate posterior probabilities then facilitate persistent learning, metacognition, informed decisions and tasks completion under uncertainty. Such rigorous nonlinear time-space integration of information and learning, and its application to sustained coordinated autonomous operations of multiple collaborative vehicles is a major focus of the present research. Challenges in our ocean domain arise due to the: complex nonlinear multiscale, multivariate ocean dynamics; large-dimension of the autonomy problems over long duration and large spatial extent; sparse, gappy and multivariate measurements; autonomous coordination and collaboration among heterogeneous vehicles into efficient swarms; and, integration of multiple disciplines into environmentally-adaptive, autonomous and rigorous naval systems. Our long-term goal is to develop and apply new theory, algorithms and computational systems for the sustained coordinated operation of multiple collaborative autonomous vehicles over long time durations in realistic multiscale nonlinear ocean settings such that the integrated naval system optimally collects observations, rigorously propagates information backward and forward in time, and accurately completes persistent learning, environmental adaptation, machine metacognition and decision making under uncertainty.

Ocean modeling is the process of developing and utilizing theoretical and computational models for the understanding and prediction of ocean dynamics. Data assimilation is the process of quantitatively estimating dynamically evolving fields by combining information from observations with those predicted by models, ideally respecting nonlinear dynamics and capturing non-Gaussian features, without heuristics or ad hoc approximations. Even though ocean dynamics often involve multiple scales, the theory for rigorous multiscale data assimilation is still in its infancy. The present project is to research next-generation multiscale data assimilation, with a focus on shelfbreak regions, including non-hydrostatic effects.

The primary goal of our MIT component is to provide expertise in ocean modeling, data assimilation and uncertainty quantification for coupled oceanographic-acoustic predictions. Our MSEAS team provides forecast and hindcast simulations of the uncertainty in the environment. In such high-fidelity multi-resolution simulations, the probability density function of the full ocean state is predicted and include the integration of model estimates with observations. With this modeling and data assimilation, accurate estimates of the probability density functions (pdf) of oceanographic variability are available. They become inputs to our end-to-end oceanographic-seabed-acoustic-sonar probabilistic TDAs and thus allow us to estimate and forecast realistic acoustic vulnerability. These oceanographic pdf estimates are provided by the MIT team for the East China Sea, Taiwan and Kuroshio region. Other regions which involve operationally relevant ocean-acoustic studies, e.g. the Middle Atlantic Bight Shelfbreak front region, as well as the Kauai Strait and Hawaiian Islands region can also be utilized.

The goal is multi-resolution data-assimilative modeling to study truly multi-scale coastal ocean dynamics and their acoustic effects, with an emphasis on resolving internal tides and long nonlinear internal waves and their interactions with the real ocean, including:

• All coastline, shelf, shelfbreak and deep ocean features; high-resolution steep bathymetry; and, atmospheric fluxes as external forcing
• Stochastic parameterizations of sub-grid scales (nonlinear internal waves and other effects) for 4D hydrostatics models, and new non-hydrostatic HDG scheme in idealized conditions

Studies involve the coupling of ocean-acoustic models in 4D, using a hierarchy of acoustic codes, in collaboration with MIT and other PIs. The specific research tasks include:

• Complete multiscale ocean re-analyses, improving initial conditions and model parameters and increasing resolutions, and distribute these fields for collaborative internal wave and acoustic studies with other PIs
• Quantify multiscale ocean dynamics using term balances and multiscale energy and vorticity analyses focusing on internal tides and waves at the shelf-edge
• Couple oceanic and acoustic deterministic models in 4D for unified studies of idealized and realistic processes, in collaboration with NM, DY and WHOI
• Develop stochastic parameterizations of sub-grid scale physics based on the statics of our deterministic simulations, and quantify ocean and acoustic uncertainties using our new dynamically orthogonal equations, in collaboration with WHOI and MIT
• Utilize new 4D non-hydrostatic HDG model for high-resolution studies of effects on internal waves from the atmosphere, stratification and shelfbreak front features

The overall objective of this program is to conduct basic research that will help enable robust autonomy and automation in dynamic, unconstrained environments and contexts. The two science problems of interest are how a learning machine may leverage all relevant prior knowledge and how it may leverage occasional in situ availability of a subject matter expert (SME). This leads naturally to the existing research of Transfer Learning and Active Learning. The intent of the ATL program is to improve upon these two existing areas of research and combine them to produce a novel, powerful learning capability. The primary deficiency of active learning is that it typically involves only labeling exemplars and does not allow the SME to fully impart his/her rich domain knowledge as they would to a human student. A deficiency of transfer learning is that when it fails it is typically not possible for an SME to repair or complete the transfer in situ. By exploiting both of these deficiencies, this ATL program seeks to fundamentally extend the scope of active learning and incorporate it into the knowledge transfer process. The two specific technical goals are to capitalize on the occasional availability of an SME to enable: 1) the robust transfer of knowledge from existing sources; and, 2) the injection of new knowledge in situ. This first technical goal includes both machine-initiated and human-guided exploration of existing knowledge sources as well as machine-based reasoning on knowledge sufficiency for prompting SME queries. This second technical goal includes both machine-initiated queries of target knowledge as well as SME injection of new, rich domain knowledge into the target.

Internal tide generation and propagation near continental slopes are being studied using a four-dimensional numerical simulation and diagnosis approach. The purpose is to explain observed variability in internal tides and the nonlinear waves they spawn. The study concentrates on long wavelength linear internal waves (internal tides) generated from subcritical tidal flow (current speed less than wave speed), ubiquitous around the world. Three internal tide effects are being examined: variable generation, heterogeneous propagation (i.e. focusing), and conversion to nonlinear waveform. The first two effects, largely unexplored thus far, will create wave energy density structure, and may give spatial/temporal structure to the nonlinear conversion process. A set of simulations are being performed with MSEAS, mostly with the hydrostatic primitive equation model already tuned at mesoscales via comparison with data. Modeled configurations will range from idealized bathymetric, stratification, and flow conditions to realistic conditions obtained via data-driven modeling. Inter-comparisons of the collected results will divulge the physics of variable four-dimensional internal tide generation and propagation, with the intent of describing how the process occurs in the real ocean. The new MSEAS non-hydrostatic model will then be used to study nonlinear conversion processes. Applicability of the results to the real ocean will be verified via comparison to remote sensing and in situ data from a one-month long experiment. The main application region is the Middle-Atlantic Bight because large constraining data sets and available tuned model, but Asian Seas areas, also with existing models and data sets, will be briefly explored to examine inter-regional differences.

Stellwagen Bank supports a multiplicity of life forms, from plankton to whales. This project seeks to mathematically model the interplay among physical and biological processes that support the productivity of the Bank’s ecosystem. The multi-scale MSEAS modeling system is being used to determine the roles of physical features and processes (e.g. topography, internal tides/waves, recirculation eddies, coastal currents, etc.) in distributing nutrients and thus the production and retention of phytoplankton biomass. The modeling research will involve both idealized and realistic simulations so as to isolate and characterize processes, and will be validated using historical and available synoptic data.

This research is a collaboration between the NATO Undersea Research Centre (CMRE) and our MSEAS group. The collaboration involves real-time ocean exercises, with ocean forecasting, ocean dynamics studies, adaptive sampling and uncertainty quantification. It also involves a series of ocean sensors and platforms with multi-resolution ocean modeling systems.

A main focus of this research is to determine the role of stochastic forcing on ocean uncertainty and variability predictions. The work includes collaborations with NRL-Stennis to prepare the transfer of a subset of the capabilities and software developed by the MSEAS group. This applied research in stochastic modeling and ocean uncertainty prediction is linked to two growing fundamental fields: prediction and reduction of uncertainties; and, estimation of properties by combining models with data. From a fundamental viewpoint, uncertainty is characterized by a probability density function (pdf). One of the aims of the applied research and collaborations with NRL will be to improve the prediction of such pdfs. The research thrusts for this effort include:

• Stochastic forcing and uncertainty/variability predictions
• Sensitivity analysis for forecast quality control, data-model comparisons and data error models
• Multiscale covariance modeling and mapping
• Ensemble initialization and generation, towards non-Gaussian ensemble initialization

Our specific objectives are to:

• Develop, demonstrate and transfer techniques for stochastic error modeling and stochastic boundary forcing for improved ensemble uncertainty predictions with NCOM and COAMPS
• Develop and transfer software for ocean data management, quality control and automated robust distribution, including data error-models and data-model comparison codes
• Demonstrate and transfer techniques for multiscale covariance modeling and level-set-based objective analysis codes for mapping data in complex coastal/archipelago domains
• Develop and demonstrate ensemble initialization and generation schemes, towards non-Gaussian ensemble initialization
• Apply the above advances in collaborative sea exercises of opportunity
• Strengthen existing and initiate new collaborations with NRL, using and leveraging the MIT Naval Officer education program

As part of the NSF Ocean Observatories Initiative (OOI), the oceanographic community is deploying a facility called the “Pioneer Array” tethered to the bottom of the ocean off the New England Coast. It is the first community relocatable process-oriented observatory, the equivalent of an international cyclotron for coastal ocean science. The array will enable a flotilla of measurement devices of different types to provide a rich data stream of physical and biological processes in the vicinity of the shelf-break front. This is a pilot project to put in place the multi-scale computational fluid dynamical modeling infrastructure required to make best use of the observations to be collected.

The pilot project will seed an ocean modeling collaboration that will exploit opportunities provided by the Pioneer Array for longer term research and educational activities in the MIT, UMass and WHOI communities and around the region. We are supporting two activities:

• a workshop aimed at fostering collaborations between observers, engineers, technologists and computational fluid dynamicists around the OOI Pioneer Array (held 4-5 June 2012 at UMass-Dartmouth) and
• a multi-scale ocean modeling pilot study designed to synthesize the observations and exploit the synthesis for science and engineering applications.

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 are researching 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 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 are researching autonomous sensing swarms and formations that exploit the multi-scale, multivariate, four-dimensional environmental-acoustic dynamics and predictabilities. We are developing 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 are being selected based on accuracy, robustness, predictability and generality. These selected schemes will be augmented to learn from (i) environmental forecasts and their associated uncertainties and (ii) the projected impact of the sensing on the forecasts. Incremental testing of the algorithms will be accomplished using idealized simulations. System tests use hindcasts from our extensive set of at-sea exercises.

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.

OOI CyberInfrastructure (CI) conducted an Observing System Simulation Experiment (OSSE) to test the capabilities of the OOI CI to support field efforts in a distributed ocean observatory in the Mid-Atlantic Bight. The goal was to provide a real oceanographic test bed in which the CI will support field operations of ships and mobile platforms, aggregate data from fixed platforms, shore-based radars, and satellites and offer these data streams to data assimilative forecast models. The MAB region was selected because of the existing communities and the presence of NOAA, ONR coordinated by Oscar Schofield in the context of the MARCOOS effort. The experiment took place October-December 2009.

The MSEAS group and the MIT Laboratory for Autonomous Marine Sensing Systems (LAMSS) group supported the OSSE through the integration of MOOSDB and MOOS-IvP components, behaviors and autonomous platform systems with ocean modeling and forecasting, data assimilation and uncertainty estimation, and adaptive sampling. The integrated OSSE prototype was developed in a simulator environment which allowed for the testing of glider and AUV parameters in the MSEAS simulated ocean conditions. Selected configurable MOOS-IvP behaviors were provided suitable for adaptive observations in OSSE scenarios utilizing available real-time numerical model output and CASPER execution.

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 specific to this effort include: (i) three-dimensional (3D) acoustic modeling coupled with high-resolution 4D physics modeling; (ii) ocean modeling incubator: structured and unstructured grids; investigations and evaluations of the next generation of numerical schemes for physical, acoustical and biological dynamics; (iii) interactions of internal tides/waves and mixing processes with mesoscale dynamics, their high-resolution modeling and multi-scale diagnostics; (iv) Lagrangian coherent structures and ocean features: their prediction, dynamics and assimilation; (v) nonlinear DA and adaptive DA, including (super)-tidal constraints and assimilation; and, (vi) use of several ocean models, model uncertainty estimation, and multi-model fusion and DA.

General objectives are to: (i) analyze and study regional physical and acoustical-physical-biogeochemical dynamics; (ii) incubate and develop new numerical modeling systems, including next generation ocean physics, 3D acoustics and Lagrangian coherent structures predictions; (iii) update existing and create new nonlinear and adaptive assimilation schemes and systems, including parameter estimation; (iv) evolve concepts and determine methodologies for regional adaptive modeling and adaptive sampling with the intent to increase predictive capabilities; (v) quantify regional predictabilities and improve probability and uncertainty modeling; and, (vi) 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.

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 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

End-to-end Prediction and DA Systems and their Uncertainties. An important component of this work involves the research, integration, demonstration and utilization of end-to-end prediction and DA systems for efficient sonar operations. Our research collaborations include the following components of such systems: ocean physics models (the free-surface model of MSEAS), acoustic models (NPS model and RAM), coupling schemes for these water-column and acoustic models, and the corresponding DA systems. We have extended 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, must to be accurate enough for successful end-to-end estimations (Lermusiaux, 2006a). The uncertainty estimates computed by the DA systems are evaluated by statistical analyses and comparison to data-forecast misfits. Interesting research has involved the theoretical modeling and estimation of uncertainties for idealized systems. Such idealized research has been necessary for determining the accurate representation and transfer of uncertainties across the various disciplines.

Ocean Dynamics, Features and Predictability. We are studying, modeling and quantifying 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.

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.

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 research is part of a large initiative sponsored by the Office of Naval Research which aims to field and operate a prototype distributed remote surveillance system based on glider and AUV technology […]

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