Articles | Volume 16, issue 12
https://doi.org/10.5194/essd-16-5737-2024
https://doi.org/10.5194/essd-16-5737-2024
Data description paper
 | 
18 Dec 2024
Data description paper |  | 18 Dec 2024

A submesoscale eddy identification dataset in the northwest Pacific Ocean derived from GOCI I chlorophyll a data based on deep learning

Yan Wang, Ge Chen, Jie Yang, Zhipeng Gui, and Dehua Peng

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Cited articles

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Mesoscale eddies are ubiquitous in the ocean and account for 90 % of its kinetic energy, but their generation and dissipation are difficult to observe using current remote sensing technology. Our submesoscale eddy dataset, formed by suppressing large-scale circulation signals and enhancing small-scale chlorophyll structures, has important implications for understanding marine environments and ecosystems, as well as improving climate model predictions.
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