Physical and Interdisciplinary Regional Ocean Dynamics and Modeling Systems
Our research includes: quantifying of ocean features and dynamics, multi-scale numerical modeling, and uncertainty quantification and assimilation schemes. Recent results include high-order hybrid Finite-Element schemes for physical-biological dynamics at shelfbreaks (bottom left), rigorous nonlinear and non-Gaussian data assimilation using our GMM-DO filter (bottom right) and exact path planning for swarms of underwater vehicles using level-set equations (top right).
Integrated Ocean Dynamics and Acoustics (IODA)
The goal is multi-resolution data-assimilative modeling to study truly multiscale 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; and, 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.
Causes and Effects of Shelf-edge Internal Tide Variability
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 is concentrating 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.
Active Transfer Learning for Ocean Modeling (ATL)
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.
Autonomous Marine Intelligent Swarming Systems for Interdisciplinary Observing Networks (A-MISSION)
We research autonomous sensing swarms and formations that exploit the multi-scale, multivariate, four-dimensional environmental-acoustic dynamics and predictabilities. We 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).
Quantifying, Predicting and Exploiting Environmental and Acoustic Fields and Uncertainties
The goal is to research prediction and data assimilation (DA) systems to study, understand, forecast and exploit environmental and acoustic fields and uncertainties. Objectives: (i) improve understanding of dynamics, predictabilities and uncertainties in the Taiwan-Kuroshio region; (ii) model and quantify interactions of Kuroshio meanders, mesoscale features and internal tides; (iii) advance coupled ocean and acoustic modeling and DA; and (iv) develop adaptive sampling for reduction of uncertainty and best exploitation of the environment.
Ocean Observatories Initiative (OOI)
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 supported field operations of ships and mobile platforms, aggregate data from fixed platforms, shore-based radars, and satellites and offered 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.