Four-Dimensional Data Assimilation for Coupled Physical-Acoustical Fields

Abstract

The estimation of oceanic environmental and acoustical fields is considered as a single coupled data assimilation problem. The four-dimensional data assimilation methodology employed is Error Subspace Statistical Estimation. Environmental fields and their dominant uncertainties are predicted by an ocean dynamical model and transferred to acoustical fields and uncertainties by an acoustic propagation model. The resulting coupled dominant uncertainties define the error subspace. The available physical and acoustical data are then assimilated into the predicted fields in accord with the error subspace and all data uncertainties. The criterion for data assimilation is presently to correct the predicted fields such that the total error variance in the error subspace is minimized. The approach is exemplified for the New England continental shelfbreak region, using data collected during the 1996 Shelfbreak Primer Experiment. The methodology is discussed, computational issues are outlined and the assimilation of model-simulated acoustical data is carried out. Results are encouraging and provide some insights into the dominant variability and uncertainty properties of acoustical fields.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2002
Accession Number
ADA487813

Entities

People

  • C. S. Chiu
  • Pierre F. J. Lermusiaux

Organizations

  • Harvard University

Tags

Communities of Interest

  • Energy and Power Technologies
  • Sensors
  • Space

DTIC Thesaurus Topics

  • Acoustic Measurement
  • Acoustic Propagation
  • Acoustic Properties
  • Acoustic Waves
  • Acoustics
  • Assimilation
  • Covariance
  • Data Sets
  • Equations
  • Four Dimensional
  • Grids
  • Measurement
  • Physical Properties
  • Physics
  • Sound Pressure
  • Three Dimensional
  • Waves

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computational Modeling and Simulation
  • Ocean-Atmosphere Mesoscale Modeling, Data Assimilation, and Flux Boundary Layers