Abstract
Kalman filtering has become a powerful framework for solving data assimilation problems. Of interest here are the low-rank filters which are computationally efficient for solving large-scale data assimilation problems. Together with theoretical aspects on the basis of which some common low-rank filters are designed, the paper also presents numerically comparative results of data assimilation using an air pollution model. The performance of such filters, as depending on the distance between the measurement locations and emission points, is investigated.
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