Entry Date:
August 21, 2017

Bayesian Nonlinear Assimilation of Eulerian and Lagrangian Coastal Flow Data

Principal Investigator Pierre Lermusiaux

The long-term goal is this project 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 motivation is to exploit the information provided by coastal platforms (drifters, floats, gliders, AUVs or HF-radars) so as to best augment the limited resolution and accuracy of satellite data in coastal regions and to determine coastal sampling needs for successful Bayesian field estimation in diverse coastal regimes. Our aim is not to shy away from the known nonlinearities and unstationary heterogeneous statistics, but to utilize these known information structures, for robust and accurate Bayesian estimation.