Interdisciplinary Nonlinear Bayesian Data Assimilation
Abstract
The long-term goal is to generalize, develop, and implement stochastic dynamically-orthogonal (DO) decompositions and nonlinear Bayesian filtering and smoothing schemes for principled 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. Our motivation is to enable a complete and accurate exploitation of the information provided by heterogeneous, gappy, multidisciplinary data. To do so, we plan to develop measurement models for physical-acoustical-biogeochemicalsea- ice data and allow accurate Bayesian updates. We will research principled interdisciplinary adaptive sampling schemes and illustrate how we can estimate the sampling needs for future Bayesian field estimation in several ocean regimes.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Sep 30, 2019
- Source ID
- N000141912693
Entities
People
- Pierre Felix Lermusiaux
Organizations
- Massachusetts Institute of Technology
- Office of Naval Research
- United States Navy