Unsupervised Learning Reveals Geography of Global Ocean Dynamical Regions

Unsupervised Learning Reveals Geography of Global Ocean Dynamical Regions

Dynamically similar regions of the global ocean are identified using a barotropic vorticity (BV) framework from a 20‐year mean of the Estimating the Circulation and Climate of the Ocean state estimate at 1° resolution. An unsupervised machine learning algorithm, K‐means, objectively clusters the sta...

Full description

Bibliographic Details
Main Authors: Maike Sonnewald, Carl Wunsch, Patrick Heimbach
Format: Article
Language:English
Published: American Geophysical Union (AGU) 2019-05-01
Series:Earth and Space Science
Subjects:
machine learning
global patterns
ocean dynamics
big data
ocean modeling
physical oceanography
Online Access:https://doi.org/10.1029/2018EA000519

Internet

https://doi.org/10.1029/2018EA000519

Similar Items