Accessibility navigation

Parameter estimation in land surface models: challenges and opportunities with data assimilation and machine learning

Tools
Lists

Raoult, N. ORCID: https://orcid.org/0000-0003-2907-9456, Douglas, N. ORCID: https://orcid.org/0000-0002-3404-8761, MacBean, N. ORCID: https://orcid.org/0000-0001-6797-4836, Kolassa, J. ORCID: https://orcid.org/0000-0001-6644-8789, Quaife, T. ORCID: https://orcid.org/0000-0001-6896-4613, Roberts, A. G. ORCID: https://orcid.org/0009-0002-4274-7914, Fisher, R. ORCID: https://orcid.org/0000-0003-3260-9227, Fer, I., Bacour, C. ORCID: https://orcid.org/0000-0002-1913-3722, Dagon, K. ORCID: https://orcid.org/0000-0002-4518-8225, Hawkins, L., Carvalhais, N. ORCID: https://orcid.org/0000-0003-0465-1436, Cooper, E. ORCID: https://orcid.org/0000-0002-1575-4222, Dietze, M. C., Gentine, P. ORCID: https://orcid.org/0000-0002-0845-8345, Kaminski, T., Kennedy, D. ORCID: https://orcid.org/0000-0001-9494-3509, Liddy, H. M. ORCID: https://orcid.org/0000-0002-8666-0805, Moore, D. J. P. ORCID: https://orcid.org/0000-0002-6462-3288, Peylin, P. ORCID: https://orcid.org/0000-0001-9335-6994 et al (2025) Parameter estimation in land surface models: challenges and opportunities with data assimilation and machine learning. Journal of Advances in Modeling Earth Systems, 17 (11). e2024MS004733. ISSN 1942-2466

[thumbnail of J Adv Model Earth Syst - 2025 - Raoult - Parameter Estimation in Land Surface Models Challenges and Opportunities With.pdf] Text - Published Version
· Restricted to Repository staff only
· The Copyright of this document has not been checked yet. This may affect its availability.
· Available under License Creative Commons Attribution.
1MB

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

To link to this item DOI: 10.1029/2024MS004733

Abstract/Summary

Key Points Data assimilation (DA) has been shown to be a powerful tool for reducing land surface model (LSM) parametric uncertainty Machine learning can facilitate parameter estimation by enhancing computational efficiency and replacing poorly represented processes Collaboration is key to advancing LSM calibration and DA, promoting knowledge exchange and standard methods

Item Type:Article
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 Meteorology
ID Code:125433
Publisher:American Geophysical Union
Altmetric
Deposit Details

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation

See also