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

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