Researching Interior Ocean Trajectories: Sensing, Quantifying, Utilizing, and Adapting to Dynamics (RIOT - SQUAD)
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P.F.J. Lermusiaux, P.J. Haley, Jr., C. Mirabito, A. Saravanakumar, S. Nieradzik-Kozic Massachusetts Institute of Technology
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Project Summary Ongoing MIT-MSEAS Research Additional Links MSEAS RIOT-SQUAD-supported Publications Background Information
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| This research is sponsored by the Office of Naval Research. | ||
Project Summary
Understanding 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. Our overall goal 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 specific objectives of the MIT-MSEAS component of the research are described below.
Background information is also available below.
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Ongoing MIT-MSEAS Research
Specific Objectives:
- 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
- Complete innovative analyses and process studies of interior dynamics and bathymetric effects, using flow-map-based Lagrangian analyses, term balances, dynamics decompositions, non-hydrostatic simulations, visualizations, and causality analyses
- Utilize MSEAS for probabilistic multiresolution ocean modeling, Eulerian-Lagrangian analyses, predictability studies, Bayesian data assimilation, and real-time sea exercises
- Adapt models and analyses from data using our machine and Bayesian learning
Publications
MSEAS RIOT-SQUAD-supported Publications
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Additional RIOT-SQUAD Links
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Background Information
Our research builds on many years of experience in multidisciplinary fields. The MSEAS software (MSEAS, 2010; Haley et al., 2015) has been used for fundamental research and for simulations and forecasts of fields and uncertainties in many regions of the world’s oceans (Lermusiaux et al., 2006; Leslie et al., 2008; Onken et al., 2003, 2008; Haley et al., 2009; Ramp et al., 2011; Gangopadhyay et al., 2011; Colin et al., 2013; Kelly and Lermusiaux, 2016; Lermusiaux et al., 2011, 2017a,b; Subramani et al., 2017a,b; Kulkarni et al., 2018; Gupta et al., 2019; Lermusiaux et al., 2019, Haley et al., 2023). Modeling capabilities include implicit two-way nesting/tiling for multiscale hydrostatic PE dynamics with a nonlinear free surface (Haley and Lermusiaux, 2010) and a high-order finite element code on unstructured grids for non-hydrostatic processes (Ueckermann and Lermusiaux, 2010, 2016; Foucart et al., 2021). The MSEAS subsystems that are of interest to this proposal include: initialization schemes (Haley et al., 2015), nested data-assimilative tidal prediction and inversion (Logutov and Lermusiaux, 2008); fast-marching objective analysis around complex topography (Agarwal and Lermusiaux, 2011); subgrid-scale models (e.g., Lermusiaux, 2001, 2006); advanced data assimilation (Lermusiaux, 1999, 2007); planning for underwater vehicles (Schofield et al., 2010; Lolla et al., 2014a,b; Lermusiaux et al., 2016; Subramani et al., 2017a,b, 2018; Subramani and Lermusiaux, 2019; Kulkarni and Lermusiaux, 2020; Doshi et al., 2023); and, adaptive sampling (Lermusiaux, 2007, 2017a,b; Heaney et al., 2007, 2016). MSEAS has been validated in numerous real-time forecasting exercises (see this page for the complete list). The most recent exercises include NASCar (Lermusiaux et al., 2017a,b), FLEAT (Pan et al., 2021; Johnston et al., 2019a,b; Haley et al., 2024c-prep), Lagrangian transport studies for NSF-ALPHA (Kulkarni and Lermusiaux, 2019; Haley et al., 2024-prep), modeling for deep-sea sediment plumes from mining (Muñoz-Royo et al., 2021), probabilistic ocean forecasting for 3D underwater positioning (DARPA-POINT, Lermusiaux et al., 2020a), and prediction of subduction dynamics in the Alboran Sea (CALYPSO; Garcia-Jove et al., 2022; Aravind et al., 2023; Mirabito et al., 2024-prep). Recent efforts have been made to develop reduced-order modeling systems that could be utilized aboard UUVs/SUVs (Heuss et al., 2020; Ryu et al., 2021). Applications of our MSEAS-PE system include: ocean monitoring (Lermusiaux et al., 2007); real-time acoustic predictions (Xu et al., 2008; Lam et al., 2009; Lermusiaux et al., 2010; Duda et al., 2011; Lermusiaux et al., 2020a,b); biogeochemical-ecosystem predictions and environmental management (Besiktepe et al., 2003; Cossarini et al., 2009; Coulin et al., 2017); and relocatable rapid response (e.g., Rixen et al., 2012; De Dominicis et al., 2014).
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