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Enhancing Ocean Current Reconstruction from Sparse & Noisy Data

Dive into reconstructing ocean currents from limited, noisy data using innovative flow decomposition techniques. Explore the application of 2D and 3D flow models, including basis function determination, Eigenvalues analysis, and boundary conditions for accurate circulation reconstruction with a focus on the Black Sea region. Discover the versatility of this model-independent scheme for assimilating sparse velocity data efficiently.

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Enhancing Ocean Current Reconstruction from Sparse & 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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