The DOI matches its Crossref record; 232 of the 232 references it cites were found in a registry or an index.
Data assimilation in the geosciences: An overview of methods, issues, and perspectives
Open accessAbstract
We commonly refer to state estimation theory in geosciences as data assimilation (DA). This term encompasses the entire sequence of operations that, starting from the observations of a system, and from additional statistical and dynamical information (such as a dynamical evolution model), provides an estimate of its state. DA is standard practice in numerical weather prediction, but its application is becoming widespread in many other areas of climate, atmosphere, ocean, and environment modeling; in all circumstances where one intends to estimate the state of a large dynamical system based on limited information. While the complexity of DA, and of the methods thereof, stands on its interdisciplinary nature across statistics, dynamical systems, and numerical optimization, when applied to geosciences, an additional difficulty arises by the continually increasing sophistication of the environmental models. Thus, in spite of DA being nowadays ubiquitous in geosciences, it has so far remained a topic mostly reserved to experts. We aim this overview article at geoscientists with a background in mathematical and physical modeling, who are interested in the rapid development of DA and its growing domains of application in environmental science, but so far have not delved into its conceptual and methodological complexities. This article is categorized under: Climate Models and Modeling > Knowledge Generation with Models
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Cited by
Cheng, S., Quilodrán-Casas, C., Ouala, S., Farchi, A., Liu, C., Tandeo, P., Fablet, R., Lucor, D., Iooss, B., Brajard, J., et al. (2023) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2023.123537 · Cited by 303
Self-citationBiferale, L., Bonaccorso, F., Buzzicotti, M., Clark Di Leoni, P., & Gustavsson, K. (2019) · Chaos: An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.5120370 · Cited by 121
Machine learning for the physics of climate
Bracco, A., Brajard, J., Dijkstra, H. A., Hassanzadeh, P., Lessig, C., & Monteleoni, C. (2024) · Nature Reviews Physics · doi:10.1038/s42254-024-00776-3 · Cited by 79
Modelling considerations for research on ocean alkalinity enhancement (OAE)
Fennel, K., Long, M. C., Algar, C., Carter, B., Keller, D., Laurent, A., Mattern, J. P., Musgrave, R., Oschlies, A., Ostiguy, J., et al. (2023) · State of the Planet · doi:10.5194/sp-2-oae2023-9-2023 · Cited by 64
Metadata
- Authors
- Carrassi, A., Bocquet, M., Bertino, L., & Evensen, G.
- Venue
- WIREs Climate Change
- Publisher
- Wiley
- Published
- 2018-07-09
- Volume
- 9
- Issue
- 5
- ISSN
- 1757-7780 (ISSN-L)
- 1757-7799
- OpenAlex
- W2752514830
- Cited by
- 867
- References
- 232
- FWCI
- 28.07
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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