Deep learning to infer eddy heat fluxes from sea surface height patterns of mesoscale turbulence
Eddy heat fluxes crucially affect large-scale oceanic currents but are challenging to monitor on a global scale. Here the authors develop a Deep Learning model to predict the eddy heat fluxes from sea surface height data only, bypassing the need for simultaneous observations of the deep ocean.
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| Main Authors: | , , |
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| Format: | article |
| Language: | EN |
| Published: |
Nature Portfolio
2021
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| Subjects: | |
| Online Access: | https://doaj.org/article/c7651d6337f64a0bb9c40bf8010fa9bc |
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| Summary: | Eddy heat fluxes crucially affect large-scale oceanic currents but are challenging to monitor on a global scale. Here the authors develop a Deep Learning model to predict the eddy heat fluxes from sea surface height data only, bypassing the need for simultaneous observations of the deep ocean. |
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