The scope of the Kalman filter for spatio‐temporal applications in environmental science. Issue 1 (17th November 2022)

• Record Type: Journal Article
• Authors: Rougier, Jonathan; Brady, Aoibheann; Bamber, Jonathan; Chuter, Stephen; Royston, Sam; Vishwakarma, Bramha Dutt; Westaway, Richard; Ziegler, Yann
• Other Names: Zammit‐Mangion Andrew guestEditor.; Newlands Nathaniel K. guestEditor.; Burr Wesley S. guestEditor.
• Is Part Of: Environmetrics. Volume 34:Issue 1(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-11-17
• Subjects: areal observations -- basis expansion -- local linear trend -- sequential updating -- truncation error -- upscaling; Environmental sciences -- Statistical methods -- Periodicals; 550.72
• DOI: 10.1002/env.2773
• Languages: English
• ISSNs: 1180-4009

Abstract

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.