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Oceanic eddy detection and lifetime forecast using machine learning methods
Author(s) -
Ashkezari Mohammad D.,
Hill Christopher N.,
Follett Christopher N.,
Forget Gaël,
Follows Michael J.
Publication year - 2016
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1002/2016gl071269
Subject(s) - zonal and meridional , grid , identification (biology) , altimeter , artificial intelligence , computer science , eddy current , machine learning , geology , pattern recognition (psychology) , remote sensing , geodesy , climatology , physics , botany , biology , quantum mechanics
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.