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: Charles.Sun@noaa.gov

(2)Naval Postgraduate School, Monterey, CA 93943

E-Mail: pcchu@nps.edu

HTML: http://faculty.nps.edu/pcchu/

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

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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