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Optimal Spectral Decomposition (OSD): An Advanced Approach for Optimal
Estimation of Ocean States and Data QC Tests
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Charles Sun(1) and Peter C Chu(2)
(1) NOAA/NODC, Silver Spring, MD 20910
E-Mail: [email protected]
(2)Naval Postgraduate School, Monterey, CA 93943
E-Mail: [email protected]
HTML: http://faculty.nps.edu/pcchu/
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Classical Objective Analysis (OA)
• Requires the background field and autocorrelation function of the variables should be given.
• The estimation of the variables’ de-correlation scales in time and space was often too subjective to produce meaningful ocean structures.
• May yield unrealistic current speeds in the vicinity of coastlines or velocities are far from the historical range.
• Never fulfills the physical boundary condition such as the normal component of current velocity should be zero at the coast.
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Optimal Spectral Decomposition (OSD)
• Overcomes the deficiencies of the classical OA method and can process sparse and noisy ocean data without knowing the background field and de-correlation scale.
• Always satisfies physical boundary conditions to produce realistic oceanic fields near coastlines.
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Spectral RepresentationFourier Series Expansion
m Basis functions (not sinusoidal)
c any ocean variable
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Inter-comparison of the OSD-Derived Velocity Vectors and Drifter Observations at 50 m on
00:00 July 9, 1998
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More Recent Study
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Results of Removal of “Spike”
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Summary
• OSD is a useful tool for processing real-time velocity data with short duration and sparse sampling area such as Argo and GTSPP data.
• OSD can handle highly noisy data and can be used for velocity data assimilation and automated QC tests.
• Don’t need first guess field and autocorrelation functions: a significant improvement over classical OA.
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