Four-Dimensional Lagrangian Alysis, Numverics,and Estimation Systems (4D LANES)
Abstract
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 ???Coherent Lagrangian Pathways from the Surface Ocean to Interior (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.We will derive and utilize novel efficient four-dimensional Lagrangian theory and methods rooted in fundamental equations and high-order schemes to predict, analyze, and characterize multiscale ocean transports, coherent structures, material sets, and subduction/stirring/mixing processes. We will compute the flow map fields using advection PDEs and our method of composition. We plan to extend our theory to residence times, age of water masses, and non-Lagrangian particles such as inertial drifting objects, chemicals, and biological organisms. We will optimize algorithms and software, and extend our method of composition to stochastic DO equations. We plan to develop a theory to differentiate the regions with coherent sets or pathways from the regions that are most conducive to turbulent mixing. Using our stochastic DO equations and non-Gaussian filters and smoothers, we will develop new probabilistic Lagrangian predictions and Bayesian Eulerian-Lagrangian data assimilation. We will study Lagrangian mutual information and predictability limits. Theories and methods will be developed using a set of idealized transport process studies relevant to the Alboran Sea and then applied to realistic simulations and forecasts with drifters, floats, and other platforms.We will apply and expand our multi-resolution submesoscale-to-regional-scale ocean modeling, 2-way nesting and tiling, and uncertainty and predictability predictions, for innovative physical, biological, and acoustical real-time forecasting and careful process and sensitivity studies. This research will involve parameter and parameterization tuning, data assimilation, and data-model comparisons. We will utilize information theoretic schemes to estimate Lagrangian-Eulerian predictability limits, correlations, and mutual information fields. We plan to improve the understanding of ocean processes occurring in the Alboran Sea and complex frontal regions, focusing on (sub)-mesoscale instabilities, subduction from the mixed layer, frontogenesis, and mixing, and the corresponding biogeochemical and acoustical effects. For biology, the focus will be the responses of chlorophyll, plankton, and oxygen to subduction and upwelling/downwelling.We plan to participate in the real-time field campaigns. We will provide multi-resolution 2-way nested ocean forecasts of Eulerian and Lagrangian variables, dynamics descriptions, and Bayesian sampling guidance. This includes not only the central forecast fields, but also statistics and probabilities using the DO equations. We hope to help design field experiments with partners using Bayesian OSSEs. We plan to predict sampling strategies during sea operations that maximize mutual information on 4D pathways, velocity, structures, processes, and/or parameterizations. Finally, we plan to collaborate and transfer data, approaches, and algorithms to naval laboratories and other colleagues.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Sep 04, 2018
- Source ID
- N000141812781
Entities
People
- Pierre Felix Lermusiaux
Organizations
- Massachusetts Institute of Technology
- Office of Naval Research
- United States Navy