Marine data assimilation in the UK: the past, the present and the vision for the future

Skákala, J., Ford, D., Haines, K. ORCID: https://orcid.org/0000-0003-2768-2374, Lawless, A. ORCID: https://orcid.org/0000-0002-3016-6568, Martin, M. J., Browne, P., Chrust, M., Ciavatta, S., Fowler, A. ORCID: https://orcid.org/0000-0003-3650-3948, Lea, D., Palmer, M., Rochner, A., Waters, J., Zuo, H., Banerjee, D. S., Bell, M., Carneiro, D. M., Chen, Y. ORCID: https://orcid.org/0000-0002-2319-6937, Kay, S., Partridge, D., Price, M., Renshaw, R., Shapiro, G. and While, J. (2025) Marine data assimilation in the UK: the past, the present and the vision for the future. Ocean Science, 21 (4). pp. 1709-1734. ISSN 1812-0792 doi: 10.5194/os-21-1709-2025

Abstract/Summary

In the last 2 decades, UK research institutes have led a wide range of developments in marine data assimilation (MDA), covering areas from operational applications in physics and biogeochemistry to fundamental theory. We highlight the emergence of strong collaboration in the UK MDA community over this period and the increasing unification of its tools. We focus on identifying the MDA stakeholder community and current/future areas of impact, as well as current trends and future opportunities. This includes the rapid growth of machine learning (ML)/artificial intelligence (AI) and digital-twin applications. We articulate a vision for the future, including the need for future types of observational data (whether planned missions or hypothetical) and how the community should respond to increases in computational power and new computer architectures (e.g. exascale computing). We contrast the requirements of different MDA areas, including physics, biogeochemistry, and coupled data assimilation (DA). Although the specifics of the vision depend on each area, common themes emerge. We advocate for balanced redistribution of new computational capability among increased model resolution, model complexity, more sophisticated DA algorithms, and uncertainty representation (e.g. ensembles). We also advocate for integrated approaches, such as strongly coupled DA (ocean–atmosphere, physics–biogeochemistry, and ocean–sea ice) and the use of ML/AI components (e.g. for multivariate increment balancing, bias correction, model emulation, observation re-gridding, or fusion).

Item Type	Article
URI	https://centaur.reading.ac.uk/id/eprint/123327
Identification Number/DOI	10.5194/os-21-1709-2025
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > National Centre for Earth Observation (NCEO) Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	European Geosciences Union
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