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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 [email protected]

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

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Page 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

Reconstruction of Ocean Currents From Sparse and Noisy

Data

Peter C Chu and Leonid IvanovNaval Postgraduate School

T.P. Korzhova, T.M. Margolina, O.V. Melnichenko

Marine Hydrophysical InstituteSevastopol, Crimea, Ukraine

[email protected]

Page 2: 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

Can we get the velocity signal from sparse and noisy data?

• Black Sea

Page 3: 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

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

Page 4: 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

Flow Decomposition

• 2 D Flow (Helmholtz)

• 3D Flow (Toroidal & Poloidal): Very popular in astrophysics

Page 5: 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

3D Incompressible Flow

• If Incompressible

• We have

Page 6: 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

Flow Decomposition

Page 7: 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

Boundary Conditions

Page 8: 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

Basis Functions

Page 9: 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

Determination of Basis Functions

• Poisson Equations• Γ - Rigid Boundary• Γ’ – Open Boundary

• {λk}, {μm} are Eigenvalues.

• Basis Functions are predetermined.

Page 10: 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

Flow Reconstruction

Page 11: 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

Reconstructed Circulation

Page 12: 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

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