Nonlinear Data Assimilation for Ocean Forecasting

Abstract

This report presents research conducted in investigating a non-linear data assimilation method, diffusive back and forth nudging (DBFN). DBFN may be able to reduce errors like four-dimensional variational assimilation, the most advanced data assimilation method, but at greatly reduced computational cost. DBFN works by running a model forwards and backwards in time with an additional nudging term that updates the ocean model with information from observations per a prescribed covariance. DBFN is tested here in two simple dynamical systems. Results are promising and demonstrate a need for further research.

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Document Details

Document Type
Technical Report
Publication Date
Mar 08, 2021
Accession Number
AD1124365

Entities

People

  • John Osborne

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Abstracts
  • Advection
  • Algorithms
  • Assimilation
  • Boundaries
  • Cartesian Coordinates
  • Covariance
  • Data Science
  • Delphi Method
  • Diffusion
  • Diffusion Coefficient
  • Dynamics
  • Equations
  • Errors
  • Four Dimensional
  • Gaussian Distributions
  • Information Science
  • Mathematical Filters
  • Observation
  • Oceans
  • Physics
  • Statistical Analysis
  • Statistics
  • Time Intervals

Fields of Study

  • Environmental science

Readers

  • Artificial Intelligence
  • Coastal Oceanography
  • Internal Combustion Engine (ICE) Technology.