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Reconstruction of Ocean Currents From Sparse and Noisy Data

Reconstruction of Ocean Currents From Sparse and Noisy Data. Peter C Chu and Leonid Ivanov Naval Postgraduate School T.P. Korzhova, T.M. Margolina, O.V. Melnichenko Marine Hydrophysical Institute Sevastopol, Crimea, Ukraine chu@nps.navy.mil.

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Reconstruction of Ocean Currents From Sparse and Noisy Data

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  1. Reconstruction of Ocean Currents From Sparse and Noisy Data Peter C Chu and Leonid Ivanov Naval Postgraduate School T.P. Korzhova, T.M. Margolina, O.V. Melnichenko Marine Hydrophysical Institute Sevastopol, Crimea, Ukraine chu@nps.navy.mil

  2. Can we get the velocity signal from sparse and noisy data? • Black Sea

  3. How can we assimilate sparse and noisy velocity data into numerical model?

  4. Flow Decomposition • 2 D Flow (Helmholtz) • 3D Flow (Toroidal & Poloidal): Very popular in astrophysics

  5. 3D Incompressible Flow • If Incompressible • We have

  6. Flow Decomposition

  7. Boundary Conditions

  8. Basis Functions

  9. Determination of Basis Functions Poisson Equations Γ - Rigid Boundary Γ’ – Open Boundary {λk}, {μm} are Eigenvalues. Basis Functions are predetermined.

  10. Flow Reconstruction

  11. Reconstructed Circulation

  12. Conclusions • Reconstruction is a useful tool for processing real-time velocity data with short duration and limited-area sampling. • The scheme can handle highly noisy data. • The scheme is model independent. • The scheme can be used for assimilating sparse velocity data

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