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 journalArticle (Academic Journal)peer-review

2 Citations (Scopus)
54 Downloads (Pure)


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 languageEnglish
Article numbere2773
Number of pages19
Early online date17 Nov 2022
Publication statusE-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 ( ). 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.

Structured keywords

  • GlobalMass


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