Scientific synthesis reports and assessments
Scientific synthesis reports and assessments
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Articles | Volume 5-opsr
Articles | Volume 5-opsr
https://doi.org/10.5194/sp-5-opsr-22-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Report:
Ocean prediction: present status and state of the art (OPSR)
https://doi.org/10.5194/sp-5-opsr-22-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 5-opsr
02 Jun 2025
 | OPSR | Chapter 8.3
 | 02 Jun 2025 | OPSR | Chapter 8.3

Crafting the Future: Machine learning for ocean forecasting

Patrick Heimbach, Fearghal O'Donncha, Timothy A. Smith, Jose Maria Garcia-Valdecasas, Alain Arnaud, and Liying Wan

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Latest update: 06 Jun 2025
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Short summary
Operational ocean prediction relies on computationally expensive numerical models and complex workflows, known as data assimilation, in which models are fit to observations to produce optimal initial conditions for prediction. Machine learning has the potential to vastly accelerate ocean prediction, improve uncertainty quantification through massive surrogate model-based ensembles, and render simulations more accurate through better model calibration. We review the developments and challenges.
 
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Ocean prediction: present status and state of the art (OPSR)
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