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Data-adaptive harmonic analysis of oceanic waves and turbulent flows
D Kondrashov1, E A Ryzhov2, P Berloff2
1Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, California 90095, USA.
Chaos (Woodbury, N.Y.)
|July 3, 2020
Summary
Data-adaptive harmonic decomposition (DAHD) reveals hidden patterns in complex ocean flows. This method simplifies chaotic dynamics into fundamental spatial patterns with simple temporal oscillations, enhancing data analysis.
Area of Science:
- Oceanography
- Fluid Dynamics
- Data Analysis
Background:
- Characterizing spatiotemporal variability in high-dimensional oceanic flow datasets is challenging.
- Complex and multiscale dynamics in ocean flows require advanced analytical techniques.
Purpose of the Study:
- Introduce new features of data-adaptive harmonic decomposition (DAHD).
- Demonstrate DAHD's capability in characterizing spatiotemporal variability in oceanic flows.
- Offer novel insights into complex and chaotic ocean dynamics.
Main Methods:
- DAHD applied to synthetic data for identifying oceanic waves in noise.
- Analysis of turbulent oceanic flows using Regional Oceanic Modeling System and a quasigeostrophic ocean model.
- Eigendecomposition of the Hermitian cross-spectral matrix to identify low-rank behavior.
Main Results:
- DAHD effectively characterizes spatiotemporal variability in complex oceanic flows.
- Spectra from analyzed flows show a thin energy line at specific temporal frequencies with scaling behavior.
- DAHD enables sparse representation of chaotic dynamics using data-inferred spatial patterns and simple temporal oscillations.
Conclusions:
- DAHD provides a powerful tool for analyzing multiscale and chaotic dynamics in oceanic systems.
- The method allows for ranking and reconstruction of spatiotemporal modes based on energy capture.
- DAHD offers a distinct approach compared to spectral proper orthogonal decomposition, utilizing a correlogram estimator.
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