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

In the last two decades UK research institutes have led a wide range of developments in marine data assimilation (MDA), covering areas from the MDA applications in physics and biogeochemistry, to MDA theory. We review the progress over this period and formulate our MDA vision for both the short-term...

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Bibliographic Details
Main Authors: Skakala, Jozef, Ford, David, Haines, Keith, Lawless, Amos, Martin, Matthew, Browne, Philip, Chrust, Marcin, Ciavatta, Stefano, Fowler, Alison, Lea, Daniel, Palmer, Matthew, Rochner, Andrea, Waters, Jennifer, Zuo, Hao, Bell, Mike, Carneiro, Davi, Chen, Yumeng, Kay, Susan, Partridge, Dale, Price, Martin, Renshaw, Richard, Shapiro, Georgy, While, James
Format: Text
Language:English
Published: 2024
Subjects:
Sea ice
Online Access:https://doi.org/10.5194/egusphere-2024-1737
https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1737/
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Summary:In the last two decades UK research institutes have led a wide range of developments in marine data assimilation (MDA), covering areas from the MDA applications in physics and biogeochemistry, to MDA theory. We review the progress over this period and formulate our MDA vision for both the short-term and the longer-term future. We focus on identifying the MDA stakeholder community and current/future areas of impact, as well as the current trends and the future opportunities. This includes rapid growth of machine learning (ML) / artificial intelligence (AI) and digital twin applications. We articulate the MDA needs for future types of observational data (whether planned missions, or hypothetical) and what should be the response of the MDA community to the increase in computational power and new computer architectures (e.g. exascale computing). Although the specifics depend on the MDA area, we advocate for balanced redistribution of the 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, 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).

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