Optimal Spectral Decomposition (OSD) for Ocean Data Assimilation
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Authors
Chu, Peter C.
Tokmakian, Robin T.
Fan, Chenwu
Sun, L. Charles
Subjects
Advisors
Date of Issue
2015-04
Date
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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 optimalmode truncation.After that, themodel field updates due to innovation
through solving a set of a linear algebraic equations of the spectral coefficients. The strength andweakness of the
OSD method are demonstrated through a twin experiment using the Parallel Ocean Program (POP) model.
Type
Description
The article of record as published may be found at http://dx.doi.org/10.1175/JTECH-D-14-00079.1
Series/Report No
Department
Oceanography
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Sponsors
The Office of Naval Research, the
Naval Oceanographic Office, and the Naval Postgraduate
School supported this study.
Funder
Format
Citation
Journal of Atmospheric and Oceanic Technology, Volume 32, pp. 828-841, April 2015.
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This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.