Optimal Spectral Decomposition (OSD) for Ocean Data Assimilation

Abstract

Optimal spectral decomposition (OSD) is applied to ocean data assimilation with variable (temperature, salinity, or velocity) anomalies (relative to background or modeled values) decomposed into generalized Fourier series, such that any anomaly is represented by a linear combination of products of basis functions and corresponding spectral coefficients. It has three steps: 1) determination of the basis functions, 2) optimal mode truncation, and 3) update of the spectral coefficients from innovation (observational increment). The basis functions, depending only on the topography of the ocean basin, are the eigenvectors of the Laplacian operator with the same lateral boundary conditions as the assimilated variable anomalies. The Vapnik Chervonkis dimension is used to determine the optimal mode truncation. After that, the model field updates due to innovation through solving a set of a linear algebraic equations of the spectral coefficients. The strength and weakness of the OSD method are demonstrated through a twin experiment using the Parallel Ocean Program (POP) model.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2015
Accession Number
ADA623267

Entities

People

  • Chenwu Fan
  • Peter Cheng Chu
  • Robin T. Tokmakian

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Air Platforms
  • Materials and Manufacturing Processes
  • Space

DTIC Thesaurus Topics

  • Assimilation
  • Boundaries
  • Coefficients
  • Data Analysis
  • Decomposition
  • Eigenvectors
  • Equations
  • Fourier Series
  • Geography
  • Grids
  • Kalman Filters
  • Linear Algebraic Equations
  • Oceanography
  • Oceans
  • Pacific Ocean
  • Topography
  • Two Dimensional

Readers

  • Calculus or Mathematical Analysis
  • Ocean-Atmosphere Mesoscale Modeling, Data Assimilation, and Flux Boundary Layers
  • Small Business Innovation Research Program (SBIR) EDI Research and Innovation.