Jonathan Rougier, Aoibheann Brady, Jonathan Bamber, Stephen Chuter, Sam Royston, Bramha Dutt Vishwakarma, Richard Westaway, Yann Ziegler
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引用次数: 2
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.
期刊介绍：
Environmetrics, the official journal of The International Environmetrics Society (TIES), an Association of the International Statistical Institute, is devoted to the dissemination of high-quality quantitative research in the environmental sciences.
The journal welcomes pertinent and innovative submissions from quantitative disciplines developing new statistical and mathematical techniques, methods, and theories that solve modern environmental problems. Articles must proffer substantive, new statistical or mathematical advances to answer important scientific questions in the environmental sciences, or must develop novel or enhanced statistical methodology with clear applications to environmental science. New methods should be illustrated with recent environmental data.
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