Researching Interior Ocean Trajectories: Sensing, Quantifying, Utilizing, and Adapting to Dynamics

Abstract

Project Summary / AbstractApproved for Public ReleaseUnderstanding submesoscale features in the interior ocean and developing new approaches to observing interior Lagrangian circulation structures are the goals of the "Researching Interior Ocean Trajectories" Departmental Research Initiative (DRI) of the Office of Naval Research.The overall goal of our proposal #Sensing, Quantifying, Utilizing, and Adapting to Dynamics (SQUAD)# is to better understand and model interior ocean trajectories and subsurface Lagrangian coherent structures by i) optimizing sensing plans for the most informative observations, ii) quantifying interior circulation patterns, water pathways, coherent sets, and submesoscale processes, iii) utilizing our probabilistic data-assimilative multiresolution (non)-hydrostatic ocean modeling capabilities, and iv) adapting models and analyses with machine and Bayesian learning of closures and parameterizations. The research thrusts of our RIOT-SQUAD effort are to: i) Collaborate with observational scientists to design field experiments and observing systems for the ocean interior and to optimally adapt the sampling during real-time sea exercises; ii) Complete innovative analyses and process studies of interior dynamics and bathymetric effects, using our flow-map-based Lagrangian analyses, term balances, dynamics decompositions, non-hydrostatic simulations, visualizations, and causality analyses; iii) Utilize our Multidisciplinary Simulation, Estimation, and Assimilation Systems (MSEAS) for probabilistic multiresolution ocean modeling, Eulerian-Lagrangian analyses, predictability studies, Bayesian data assimilation, and real-time sea exercises; and iv) Adapt models and analyses from data using our machine and Bayesian learning.Our project is intended to be part of the overall integrated and collaborative DRI effort involving other PIs. Our modeling, derivations, and ocean dynamics analyses will be motivated by and coordinated with theoverall RIOT effort. We plan to collaborate with the scientists involved in the DRI and provide them with our results. We also expect to collaborate and transfer data, expertise, approaches, algorithms, and/or software to the Naval Research Laboratory (NRL), Naval Oceanographic Office (NAVOCEANO), and other related Navy colleagues. Finally, we will continue to contribute to and leverage the MIT-WHOI joint program and other MIT naval officer programs.

Document Details

Document Type
DoD Grant Award
Publication Date
Nov 09, 2024
Source ID
N000142412715

Entities

People

  • Pierre Felix Lermusiaux

Organizations

  • Massachusetts Institute of Technology
  • Office of Naval Research
  • United States Navy

Tags

Fields of Study

  • Environmental science

Readers

  • Neural Network Machine Learning.
  • Ocean-Atmosphere Mesoscale Modeling, Data Assimilation, and Flux Boundary Layers
  • Research Science/Academic Research

Technology Areas

  • AI & ML