Articles | Volume 5-opsr
https://doi.org/10.5194/sp-5-opsr-9-2025
https://doi.org/10.5194/sp-5-opsr-9-2025
02 Jun 2025
 | OPSR | Chapter 4.3
 | 02 Jun 2025 | OPSR | Chapter 4.3

Data assimilation schemes for ocean forecasting: state of the art

Matthew J. Martin, Ibrahim Hoteit, Laurent Bertino, and Andrew M. Moore

Related authors

Core services: an introduction to global ocean forecasting
Yann Drillet, Matthew Martin, Yosuke Fujii, Eric Chassignet, and Stefania Ciliberti
State Planet, 5-opsr, 2, https://doi.org/10.5194/sp-5-opsr-2-2025,https://doi.org/10.5194/sp-5-opsr-2-2025, 2025
Short summary
Numerical models for simulating ocean physics
Michael J. Bell, Andreas Schiller, and Stefania Ciliberti
State Planet, 5-opsr, 10, https://doi.org/10.5194/sp-5-opsr-10-2025,https://doi.org/10.5194/sp-5-opsr-10-2025, 2025
Short summary
Assessing the impact of future altimeter constellations in the Met Office global ocean forecasting system
Robert R. King, Matthew J. Martin, Lucile Gaultier, Jennifer Waters, Clément Ubelmann, and Craig Donlon
Ocean Sci., 20, 1657–1676, https://doi.org/10.5194/os-20-1657-2024,https://doi.org/10.5194/os-20-1657-2024, 2024
Short summary
Updates to the Met Office’s global ocean-sea ice forecasting system including model and data assimilation changes
Davi Mignac, Jennifer Waters, Daniel J. Lea, Matthew J. Martin, James While, Anthony T. Weaver, Arthur Vidard, Catherine Guiavarc’h, Dave Storkey, David Ford, Edward W. Blockley, Jonathan Baker, Keith Haines, Martin R. Price, Michael J. Bell, and Richard Renshaw
EGUsphere, https://doi.org/10.5194/egusphere-2024-3143,https://doi.org/10.5194/egusphere-2024-3143, 2024
Short summary
Marine data assimilation in the UK: the past, the present and the vision for the future
Jozef Skakala, David Ford, Keith Haines, Amos Lawless, Matthew Martin, Philip Browne, Marcin Chrust, Stefano Ciavatta, Alison Fowler, Daniel Lea, Matthew Palmer, Andrea Rochner, Jennifer Waters, Hao Zuo, Mike Bell, Davi Carneiro, Yumeng Chen, Susan Kay, Dale Partridge, Martin Price, Richard Renshaw, Georgy Shapiro, and James While
EGUsphere, https://doi.org/10.5194/egusphere-2024-1737,https://doi.org/10.5194/egusphere-2024-1737, 2024
Short summary

Cited articles

Alvarez Fanjul, E. and Bahurel, P.: OceanPrediction Decade Collaborative Center: Connecting the world around ocean forecasting, in: Ocean prediction: present status and state of the art (OPSR), edited by: Álvarez Fanjul, E., Ciliberti, S. A., Pearlman, J., Wilmer-Becker, K., and Behera, S., Copernicus Publications, State Planet, 5-opsr, 1, https://doi.org/10.5194/sp-5-opsr-1-2025, 2025. 
Alvarez Fanjul, E., Ciliberti, S., and Bahurel, P.: Implementing Operational Ocean Monitoring and Forecasting Systems, IOC-UNESCO, GOOS-275, https://doi.org/10.48670/ETOOFS, 2022. 
Bannister, R. N.: A review of operational methods of variational and ensemble-variational data assimilation, Q. J. Roy. Meteor. Soc., 143, 607–633, https://doi.org/10.1002/qj.2982, 2017. 
Barbosa Aguiar, A., Bell, M. J., Blockley, E., Calvert, D., Crocker, R., Inverarity, G., King, R., Lea, D. J., Maksymczuk, J., Martin, M. J., Price, M. R., Siddorn, J., Smout-Day, K., Waters, J., and While, J.: The Met Office Forecast Ocean Assimilation Model (FOAM) using a 1/12-degree grid for global forecasts, Q. J. Roy. Meteor. Soc., 150, 3827–3852, https://doi.org/10.1002/qj.4798, 2024. 
Barthélémy, S., Brajard, J., Bertino, L., and Counillon, F.: Superresolution data assimilation, Ocean Dynam., 72, 661–678, https://doi.org/10.1007/s10236-022-01523-x, 2022. 
Download
Short summary
Observations of the ocean from satellites and platforms in the ocean are combined with information from computer models to produce predictions of how the ocean temperature, salinity, and currents will evolve over the coming days and weeks and to describe how the ocean has evolved in the past. This paper summarises the methods used to produce these ocean forecasts at various centres around the world and outlines the practical considerations for implementing such forecasting systems.
Share
Altmetrics
Final-revised paper
Preprint