Adaptive extraction and quantification of geophysical vortices

Area of Science:

• Fluid Dynamics
• Oceanography
• Computational Science

Background:

• Turbulent flows contain discrete vortices crucial for energy and momentum transport.
• Accurate vortex identification is challenging due to the complex and dynamic nature of turbulence.
• Existing methods often struggle with parameter selection for reliable vortex extraction.

Purpose of the Study:

• To develop a novel, model-based approach for extracting two-dimensional vortices from turbulent flow data.
• To establish a quantitative metric for assessing the similarity between ideal and detected vortex cores.
• To provide a data-driven method for selecting optimal vortex detection thresholds based on statistical confidence.

Main Methods:

• Utilized a reference model of an idealized vortex to derive a strength-size correlation.
• Employed a vortex strength parameter (strain squared minus vorticity squared) for core localization.
• Measured similarity between ideal and real vortex cores across varying strength thresholds.
• Determined per-vortex extraction thresholds based on a target coefficient of determination for statistical confidence.

Main Results:

• A novel correlation between vortex size and strength was derived from the reference model.
• The method provides a metric to quantify how well a detected vortex core matches an ideal vortex.
• Validated on global ocean simulation data, yielding a map of expected vortex strengths.

Conclusions:

• The developed approach offers a robust and statistically grounded method for vortex extraction in turbulent flows.
• It provides valuable insights into threshold selection for vortex identification.
• The derived global map of ocean vortex strengths advances our understanding of oceanic dynamics.