Articles | Volume 21, issue 1
https://doi.org/10.5194/os-21-113-2025
https://doi.org/10.5194/os-21-113-2025
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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Cited articles

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Bozec, A., Chassignet, E. P., and Srinivasan, A.: GOMb0.04 Reanalysis for the Gulf of Mexico, https://www.hycom.org/data/gomb0pt04/gom-reanalysis (last access: 21 January 2025), 2025. a
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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.