Ocean Science
Ocean Science
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Articles | Volume 21, issue 1
Articles | Volume 21, issue 1
https://doi.org/10.5194/os-21-113-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/os-21-113-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 21, issue 1
Research article
 | 
24 Jan 2025
Research article |  | 24 Jan 2025

Convolutional neural networks for sea surface data assimilation in operational ocean models: test case in the Gulf of Mexico

Olmo Zavala-Romero, Alexandra Bozec, Eric P. Chassignet, and Jose R. Miranda

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Short summary
This study shows AI can speed up data assimilation in ocean models. Researchers used convolutional neural networks (CNNs) to assimilate sea surface temperature and height observations in the Gulf of Mexico, learning to replicate corrections made by traditional, computationally expensive methods. CNN design and training window size significantly impacted accuracy, but the percentage of ocean pixels did not. These findings suggest CNNs may accelerate data assimilation in realistic settings.
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