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Bayesian Nonlinear Assimilation of Eulerian and Lagrangian Coastal Flow Data

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Technical Report,01 May 2014,30 Apr 2017

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Massachusetts Institute of Technology Cambridge United States

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The long-term goal is to Develop and apply theory, schemes and computational systems for rigorous Bayesian nonlinear assimilation of Eulerian and Lagrangian coastal flow data, fully exploiting nonlinear governing equations and mutual information structures inherent to coastal ocean dynamical systems and optimally inferring multiscale coastal ocean fields for quantitative scientific studies and efficient naval operations. The specific objectives for the three-year project are to Implement and further develop our DO equations and numerical schemes for predicting the pdfs of nonlinear multiscale coastal ocean fields, both in Eulerian and Lagrangian forms. Further develop and implement our GMM-DO schemes for robust Bayesian nonlinear estimation of coastal ocean fields by assimilation of Eulerian and Lagrangian flow data. Apply our DO and GMM-DO schemes, as well as their theoretical extensions, numerical schemes and distributed implementation, in idealized-to-realistic coastal dynamics conditions and coastal flow observing system simulation experiments. Evaluate results using information-theoretic metrics, explain multiscale dynamics and interactions, quantify coastal flow observation requirements, and complete computational analyses. Collaborate and transfer data, expertise, approaches, algorithms and software to NRL and other colleagues. Utilize and leverage the MIT Naval Officer education program.

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