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Optimal Spectral Decomposition (OSD): An Advanced Approach for Optimal

Optimal Spectral Decomposition (OSD): An Advanced Approach for Optimal Estimation of Ocean States and Data QC Tests. 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

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Optimal Spectral Decomposition (OSD): An Advanced Approach for Optimal

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  1. Optimal Spectral Decomposition (OSD): An Advanced Approach for Optimal Estimation of Ocean States and Data QC Tests • 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/

  2. 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.

  3. 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.

  4. Spectral RepresentationFourier Series Expansion  Basis functions (not sinusoidal) c any ocean variable

  5. Inter-comparison of the OSD-Derived Velocity Vectors and Drifter Observations at 50 m on 00:00 July 9, 1998

  6. More Recent Study

  7. Results of Removal of “Spike”

  8. 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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