Understanding and Predicting the Gulf of Mexico Loop Current
Numerical Modeling
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P.F.J. Lermusiaux, P.J. Haley, Jr., C. Mirabito, K. Gkirgkis, A. Gupta, A. Ghodgaonkar, W.H. Ali Massachusetts Institute of Technology
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Project Summary Ongoing MIT-MSEAS Research Additional Links MSEAS Loop Current-supported Publications Background Information
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Image credit: NAS |
Image credit: BOEM |
| This research is sponsored by the The National Academies of Sciences, Engineering, and Medicine. | |||
Project Summary
The overarching goal of this multi-institution collaborative project led by Prof. Ruoying He (NCSU) is to achieve greater understanding of the physical processes that control the circulation in the Gulf of Mexico (GOM), in particular the Loop Current and Loop Current eddy separation dynamics, through advanced data assimilative modeling and analyses. The project, closely following recommendations made in the National Academies (2018) “Understanding and Predicting the Gulf of Mexico Loop Current Critical Gaps and Recommendations”, in particular Recommendation 22, will perform a new skill assessment among existing Gulf prediction systems to test current model performance in resolving both surface and subsurface circulation, long-range prediction capabilities, and to better inform the observing campaign's final design. The specific objectives of the MIT-MSEAS component of the research are described below.
Background information is available below.
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Ongoing MIT-MSEAS Research
Specific Objectives:
- 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
- Predict the information content and impacts of observations for principled observational campaign design
Publications
MSEAS Loop Current-supported Publications
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Additional Loop Current Links
- Gulf of Mexico Loop Current Main Research Page, Ocean Observing and Modeling Group, NCSU
- UGOS Program Main Page
- December 19, 2018 NAS Press Release
- List of Awardees
- 2018 NAS Report: Understanding and Predicting the Gulf of Mexico Loop Current; Critical Gaps and Recommendations (online book)
- January 11, 2019 Kickoff Meeting Registration Page
- UGOS Past Events (Annual Meetings and Talks)
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Background Information
For modeling, we will employ our MSEAS software (http://mseas.mit.edu/software).
At the core of MSEAS are three solvers of governing fluid and ocean dynamics equations. The first solver is part of an extensive modeling system for hydrostatic primitive-equation dynamics with a nonlinear free surface, based on second-order structured finite volumes (Haley and Lermusiaux, 2010). It is used to study and quantify tidal-to-mesoscale processes over regional domains with complex geometries and varied interactions. The MSEAS capabilities include: fast-marching coastal objective analysis (Agarwal and Lermusiaux, 2011); estimation of spatial and temporal scales from data (Agarwal, 2009); initializations of fields and ensembles (Lermusiaux et al., 2000; Lermusiaux, 2002; Haley et al., 2015); nested data-assimilative tidal prediction and inversion (Logutov and Lermusiaux, 2008); implicit two-way nesting and tiling (Haley and Lermusiaux, 2010); stochastic subgrid-scale forcing (Lermusiaux, 2006); adaptive data assimilation, sampling and learning (e.g. Lermusiaux, 2007; Schofield et al., 2010); biogeochemical modeling (Besiktepe et al., 2003); Lagrangian coherent structures and their uncertainties (Lermusiaux et al., 2006; Feppon and Lermusiaux, 2018a,b); many-task computing (Evangelinos et al., 2011); and systems for the control of such legacy codes (Evangelinos et al., 2006). Integral to our uncertainty prediction is our Error Subspace Statistical Estimation for ensemble forecasting and data assimilation (Lermusiaux, 2006, 2007). We also compare predictions to data: examples include physical-biogeochemical forecasts for the Philippines Archipelago (Lermusiaux et al., 2011); uncertainty forecasts for the Taiwan region during the Quantifying, Predicting and Exploiting (QPE) uncertainty DRI (Gawarkiewicz et al., 2011; Lermusiaux et al., 2010, in prep.); and stochastic reachability, path planning and adaptive sampling forecasts for gliders and floats during NASCar (Lermusiaux et al., 2017a). Recently, we also implemented the DO primitive-equations for stochastic ocean predictions (Subramani, 2018; Subramani and Lermusiaux, in prep.). Even though this new MSEAS capability is so far coded only in a serial fashion, it is already useful for regional applications. For example, with the DO primtibe-equations, we issued in real-time a 5-day probabilistic forecast for the Lakshadweep Islands region that was equivalent to a massive ensemble containing 100,000 perturbed members.
The other two MSEAS solvers are non-hydrostatic fluid and ocean dynamics models. One is a simple 2D model using conservative finite-volumes implemented in Matlab (Ueckermann and Lermusiaux, 2012). This simple 2D model has been very useful for diverse ocean-dynamics process studies and for the incubation of advanced schemes and methodologies. The other model is a non-hydrostatic 3D Navier-Stokes and Boussinesq code using the finite-element method. Specifically, novel Hybridizable Discontinuous Galerkin finite-element schemes were combined with projection methods for Navier-Stokes and Boussinesq non-hydrostatic primitive-equations (Ueckermann, 2014; Ueckermann and Lermusiaux, 2016; Ueckermann et al., 2018). These models can be employed for targeted non-hydrostatic process-studies, focusing on the variability of sub-mesoscale processes in the Gulf.
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