An Operational Technology for Assimilating Lagrangian Data Based on Dynamical Systems Techniques

Abstract

Much data in the ocean is Lagrangian in nature. Its full use in ocean prediction could advance significantly the Navy's ability to predict ocean conditions. The goal of this project is the development of a data assimilation scheme that will afford a full naval predictive capacity in fixed ocean regions which can be comprehensively surveyed by Lagrangian measuring devices. This will be based on the use of dynamical systems ideas that can generate strategies for deploying Lagrangian observational devices and their associated sensors. An effective Lagrangian data assimilation scheme coupled with an optimal deployment strategy will form the basis of an integrated prediction scheme for the ocean that can feed on both purely Lagrangian and mixed source data.

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Document Details

Document Type
Technical Report
Publication Date
Sep 30, 2007
Accession Number
ADA573394

Entities

People

  • Christopher K. R. T. Jones

Organizations

  • University of North Carolina at Chapel Hill

Tags

Communities of Interest

  • Autonomy
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Assimilation
  • Bayesian Networks
  • Deployment
  • Equations
  • Filters
  • Flow
  • Flow Fields
  • Fungi
  • Geometry
  • Kalman Filters
  • Models
  • North Carolina
  • Observation
  • Oceans
  • Predictive Modeling
  • Shallow Water

Fields of Study

  • Environmental science

Readers

  • Atmospheric Science/Meteorology
  • Finite Element Method (FEM) for solving Partial Differential Equations (PDEs)
  • Systems Analysis and Design