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...

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Veröffentlicht in:	Earth and space science (Hoboken, N.J.) Jg. 6; H. 5; S. 784 - 794
Hauptverfasser:	Sonnewald, Maike, Wunsch, Carl, Heimbach, Patrick
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States John Wiley & Sons, Inc 01.05.2019 John Wiley and Sons Inc American Geophysical Union (AGU)
Schlagworte:	Algorithms Artificial intelligence big data Budgets Computational Geophysics Consortia Data Analysis: Algorithms and Implementation General Circulation General circulation models Geodesy and Gravity Geography global patterns Informatics Machine Learning Mass Balance Natural Hazards Neural Networks, Fuzzy Logic, Machine Learning ocean dynamics ocean modeling Ocean Monitoring with Geodetic Techniques Oceanography: Physical physical oceanography Physics Statistical Analysis Statistical methods: Descriptive Topography ocean dynamics ocean modeling physical oceanography big data machine learning global patterns
ISSN:	2333-5084, 2333-5084
Online-Zugang:	Volltext
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