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Interdisciplinary Nonlinear Bayesian Data Assimilation

P.F.J. Lermusiaux, P.J. Haley, Jr.,
M. Doshi, J. Lin,
W.H. Ali, A. Gkirgkis,
A. Charous, Z. Duguid

Massachusetts Institute of Technology
Center for Ocean Engineering
Mechanical Engineering
Cambridge, Massachusetts

Project Summary
Ongoing MIT-MSEAS Research
Additional Links
MSEAS Project-supported Publications
Background Information

 

This research is sponsored by the Office of Naval Research.

Project Summary

Our long-term goal is to: generalize, develop, and implement our stochastic dynamically-orthogonal decompositions and nonlinear Bayesian filtering and smoothing schemes forprincipled probabilistic predictions and predictability studies of physical-acoustical-biogeochemical-sea-ice dynamics, and for interdisciplinary nonlinear Bayesian data assimilation, adaptive sampling, and quantification of observation needs for naval operations.

Background information is available below.

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Ongoing MIT-MSEAS Research

Specific Objectives:

Publications

MSEAS Project-supported Publications

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Additional Links

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Background Information

Our research builds on years of modeling experience in multiple ocean regions for physical, acoustical, and biogeochemical studies. The MSEAS software (MSEAS Group, 2010, Haley et al., 2015) has been used for fundamental research and for realistic simulations and forecasts of fields and uncertainties in many regions of the world's ocean (Lermusiaux et al., 2006; Leslie et al, 2008; Onken et al, 2003, 2008; Haley et al, 2009; Gangopadhyay et al., 2011; Ramp et al., 2011; Colin et al., 2013; Kelly and Lermusiaux, 2016; Lermusiaux et al., 2011, 2017a,b). Practical applications include ocean monitoring (Lermusiaux et al., 2007); real-time acoustic predictions and data assimilation (Xu et al., 2008; Lam et al., 2009; Lermusiaux et al., 2010; Duda et al., 2011); biogeochemical-ecosystem predictions and environmental management (Besiktepe et al., 2003; Cossarini et al., 2009; Coulin et al., 2017); relocatable rapid response (e.g. Rixen et al., 2012; De Dominicis et al., 2014); path planning for underwater vehicles (Schofield et al., 2010; Lolla et al., 2014a,b; Lermusiaux et al., 2016); and, adaptive sampling (Lermusiaux, 2007; Heaney et al., 2007, 2016). MSEAS has been tested and validated in a wide range of real-time forecasting exercises. They include: AWACS and SW-06 (Haley and Lermusiaux, 2010; Colin et al., 2013); AOSN-II and MB-06 (Lermusiaux et al., 2006, Gangopadhyay et al., 2011; Ramp et al., 2011); QPE-08 and -09 (Lermusiaux et al., 2010; Gawarkiewicz et al., 2011; Lermusiaux et al., in prep.); PhilEx-08 and -09 (Agarwal and Lermusiaux, 2011; Lermusiaux et al., 2011); and NASCar and FLEAT (Lermusiaux et al., 2017a,b; Pan et al., 2018). Recently, we also issued forecasts of Lagrangian transports and coherent structures, and their uncertainties, on multiple time and space scales, as well as real-time guidance for optimal Lagrangian sampling (NSF-ALPHA). Using our ESSE methodology, we also forecast and transferred the probability of high-resolution ocean fields (sound-speed, currents, etc.) for three-dimensional underwater positioning system exercises (POINT). All of this expertise will be most useful. Although parameter tuning is always required, the present proposed research will leverage other efforts

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