Articles | Volume 5-opsr
https://doi.org/10.5194/sp-5-opsr-22-2025
https://doi.org/10.5194/sp-5-opsr-22-2025
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

Related authors

Numerical models for monitoring and forecasting sea ice: a short description of present status
Laurent Bertino, Patrick Heimbach, Ed Blockley, and Einar Ólason
State Planet, 5-opsr, 14, https://doi.org/10.5194/sp-5-opsr-14-2025,https://doi.org/10.5194/sp-5-opsr-14-2025, 2025
Short summary
Unlocking the power of parallel computing: GPU technologies for ocean forecasting
Andrew R. Porter and Patrick Heimbach
State Planet, 5-opsr, 23, https://doi.org/10.5194/sp-5-opsr-23-2025,https://doi.org/10.5194/sp-5-opsr-23-2025, 2025
Short summary
Northern Hemisphere Stratospheric Temperature Response to External Forcing in Decadal Climate Simulations
Abdullah A. Fahad, Andrea Molod, Krzysztof Wargan, Dimitris Menemenlis, Patrick Heimbach, Atanas Trayanov, Ehud Strobach, and Lawrence Coy
EGUsphere, https://doi.org/10.21203/rs.3.rs-1892797/v2,https://doi.org/10.21203/rs.3.rs-1892797/v2, 2025
Short summary
Evaluation of operational ocean forecasting systems from the perspective of the users and the experts
Stefania A. Ciliberti, Enrique Alvarez Fanjul, Jay Pearlman, Kirsten Wilmer-Becker, Pierre Bahurel, Fabrice Ardhuin, Alain Arnaud, Mike Bell, Segolene Berthou, Laurent Bertino, Arthur Capet, Eric Chassignet, Stefano Ciavatta, Mauro Cirano, Emanuela Clementi, Gianpiero Cossarini, Gianpaolo Coro, Stuart Corney, Fraser Davidson, Marie Drevillon, Yann Drillet, Renaud Dussurget, Ghada El Serafy, Katja Fennel, Marcos Garcia Sotillo, Patrick Heimbach, Fabrice Hernandez, Patrick Hogan, Ibrahim Hoteit, Sudheer Joseph, Simon Josey, Pierre-Yves Le Traon, Simone Libralato, Marco Mancini, Pascal Matte, Angelique Melet, Yasumasa Miyazawa, Andrew M. Moore, Antonio Novellino, Andrew Porter, Heather Regan, Laia Romero, Andreas Schiller, John Siddorn, Joanna Staneva, Cecile Thomas-Courcoux, Marina Tonani, Jose Maria Garcia-Valdecasas, Jennifer Veitch, Karina von Schuckmann, Liying Wan, John Wilkin, and Romane Zufic
State Planet, 1-osr7, 2, https://doi.org/10.5194/sp-1-osr7-2-2023,https://doi.org/10.5194/sp-1-osr7-2-2023, 2023
Potential artifacts in conservation laws and invariants inferred from sequential state estimation
Carl Wunsch, Sarah Williamson, and Patrick Heimbach
Ocean Sci., 19, 1253–1275, https://doi.org/10.5194/os-19-1253-2023,https://doi.org/10.5194/os-19-1253-2023, 2023
Short summary

Cited articles

Abarbanel, H. D. I., Rozdeba, P. J., and Shirman, S.: Machine Learning: Deepest Learning as Statistical Data Assimilation Problems, Neural Comput., 30, 2025–2055, https://doi.org/10.1162/neco_a_01094, 2018. 
Abernathey, R. P., Augspurger, T., Banihirwe, A., Blackmon-Luca, C. C., Crone, T. J., Gentemann, C. L., Hamman, J. J., Henderson, N., Lepore, C., McCaie, T. A., Robinson, N. H., and Signell, R. P.: Cloud-Native Repositories for Big Scientific Data, Comput. Sci. Eng., 23, 26–35, https://doi.org/10.1109/mcse.2021.3059437, 2020. 
Accarino, G., Chiarelli, M., Immorlano, F., Aloisi, V., Gatto, A., and Aloisio, G.: MSG-GAN-SD: A Multi-Scale Gradients GAN for Statistical Downscaling of 2-Meter Temperature over the EURO-CORDEX Domain, AI, 2, 600–620, https://doi.org/10.3390/ai2040036, 2021. 
Arcomano, T., Szunyogh, I., Pathak, J., Wikner, A., Hunt, B. R., and Ott, E.: A Machine Learning-Based Global Atmospheric Forecast Model, Geophys. Res. Lett., 47, e2020GL087776, https://doi.org/10.1029/2020GL087776, 2020. 
Download
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.
 
Share
Altmetrics
Final-revised paper
Preprint