The scope of the Kalman filter for spatio-temporal applications in environmental science

Jonathan Rougier; Aoibheann Brady; Jonathan L Bamber; Stephen J Chuter; Sam J Royston; Bramha Dutt Vishwakarma; Richard M Westaway; Yann Ziegler

doi:10.1002/env.2773

The scope of the Kalman filter for spatio-temporal applications in environmental science

Jonathan Rougier^*, Aoibheann Brady, Jonathan L Bamber, Stephen J Chuter, Sam J Royston, Bramha Dutt Vishwakarma, Richard M Westaway, Yann Ziegler

^*Corresponding author for this work

Research output: Contribution to journal › Article (Academic Journal) › peer-review

6 Citations (Scopus)

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Abstract

The Kalman filter is a workhorse of dynamical modeling. But there are challenges when using the Kalman filter in environmental science: the complexity of environmental processes, the complicated and irregular nature of many environmental datasets, and the scale of environmental datasets, which may comprise many thousands of observations per time-step. We show how these challenges can be met within the Kalman filter, identifying some situations which are relatively easy to handle, such as datasets which are high-resolution in time, and some which are hard, like areal observations on small contiguous polygons. Overall, we conclude that many applications in environmental science are within the scope of the Kalman filter, or its generalizations.

Original language	English
Article number	e2773
Number of pages	19
Journal	Environmetrics
Early online date	17 Nov 2022
DOIs	https://doi.org/10.1002/env.2773
Publication status	E-pub ahead of print - 17 Nov 2022

Bibliographical note

Funding Information:
All authors were supported by the European Research Council (ERC) under the European Union's Horizon 2020—Research and Innovation Framework Programme under grant agreement number 694188, the GlobalMass project ( https://www.globalmass.eu/ ). We would like to thank Andrew Zammit‐Mangion, the Editor, and two reviewers, for their detailed comments on various versions of this paper, which lead to major improvements in focus and clarity.

Publisher Copyright:
© 2022 John Wiley & Sons Ltd.

Research Groups and Themes

GlobalMass

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10.1002/env.2773Licence: CC BY-NC-ND

Full-text PDF (final published version)
This is the final published version of the article (version of record). It first appeared online via Wiley at https://doi.org/10.1002/env.2773 .Please refer to any applicable terms of use of the publisher.
Final published version, 1.36 MBLicence: CC BY-NC-ND

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GlobalMass: Global ice and ocean mass trends
Bates, P. D. (Co-Principal Investigator), Bingham, R. J. (Co-Principal Investigator), Chuter, S. J. (Researcher), Hofer, S. (Student), Llovell, W. (Researcher), Martin Espanol, A. (Researcher), Rougier, J. C. (Co-Principal Investigator), Schumacher, M. (Researcher), Sha, Z. (Researcher), Westaway, R. M. (Manager), Royston, S. J. (Researcher), Vishwakarma, B. D. (Researcher), Bamber, J. L. (Principal Investigator), Brady, A. (Researcher), Ziegler, Y. (Researcher), Abele, A. K. (Student) & Lehmann, F. (Student)
1/08/16 → 30/11/22
Project: Research

Cite this

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title = "The scope of the Kalman filter for spatio-temporal applications in environmental science",

abstract = "The Kalman filter is a workhorse of dynamical modeling. But there are challenges when using the Kalman filter in environmental science: the complexity of environmental processes, the complicated and irregular nature of many environmental datasets, and the scale of environmental datasets, which may comprise many thousands of observations per time-step. We show how these challenges can be met within the Kalman filter, identifying some situations which are relatively easy to handle, such as datasets which are high-resolution in time, and some which are hard, like areal observations on small contiguous polygons. Overall, we conclude that many applications in environmental science are within the scope of the Kalman filter, or its generalizations.",

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note = "Funding Information: All authors were supported by the European Research Council (ERC) under the European Union's Horizon 2020—Research and Innovation Framework Programme under grant agreement number 694188, the GlobalMass project ( https://www.globalmass.eu/ ). We would like to thank Andrew Zammit‐Mangion, the Editor, and two reviewers, for their detailed comments on various versions of this paper, which lead to major improvements in focus and clarity. Publisher Copyright: {\textcopyright} 2022 John Wiley & Sons Ltd.",

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AU - Vishwakarma, Bramha Dutt

AU - Westaway, Richard M

AU - Ziegler, Yann

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AB - The Kalman filter is a workhorse of dynamical modeling. But there are challenges when using the Kalman filter in environmental science: the complexity of environmental processes, the complicated and irregular nature of many environmental datasets, and the scale of environmental datasets, which may comprise many thousands of observations per time-step. We show how these challenges can be met within the Kalman filter, identifying some situations which are relatively easy to handle, such as datasets which are high-resolution in time, and some which are hard, like areal observations on small contiguous polygons. Overall, we conclude that many applications in environmental science are within the scope of the Kalman filter, or its generalizations.

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The scope of the Kalman filter for spatio-temporal applications in environmental science

Abstract

Bibliographical note

Research Groups and Themes

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Projects

GlobalMass: Global ice and ocean mass trends

Cite this