Oceanic eddy detection and lifetime forecast using machine learning methods
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AshkezariEtAl2016full.pdf
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Author(s)
Ashkezari, Mohammad D.
Hill, Christopher N.
Follett, Christopher N.
Forget, Gaël
Follows, Michael J.
Date Issued
October 4, 2018
Journal
Geophysical Research Letters
Publisher
American Geophysical Union (AGU)
Citation
Ashkezari, Mohammad D., Christopher N. Hill, Christopher N. Follett, Gaël Forget, and Michael J. Follows. “Oceanic Eddy Detection and Lifetime Forecast Using Machine Learning Methods.” Geophysical Research Letters 43, no. 23 (December 15, 2016): 12,234–12,241. doi:10.1002/2016gl071269.
Version
Author's final manuscript
Abstract
©2016. American Geophysical Union. All Rights Reserved. We report a novel altimetry-based machine learning approach for eddy identification and characterization. The machine learning models use daily maps of geostrophic velocity anomalies and are trained according to the phase angle between the zonal and meridional components at each grid point. The trained models are then used to identify the corresponding eddy phase patterns and to predict the lifetime of a detected eddy structure. The performance of the proposed method is examined at two dynamically different regions to demonstrate its robust behavior and region independency.
MIT Department
Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1002/2016GL071269
