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A. W. Heemink A. J. Segers 《Stochastic Environmental Research and Risk Assessment (SERRA)》2002,16(3):225-240
The Kalman filter is used in this paper as a framework for space time data analysis. Using Kalman filtering it is possible
to include physically based simulation models into the data analysis procedure. Attention is concentrated on the development
of fast filter algorithms to make Kalman filtering feasible for high dimensional space time models. The ensemble Kalman filter
and the reduced rank square root filter algorithm are briefly summarized. A new algorithm, the partially orthogonal ensemble
Kalman filter is introduced too. We will illustrate the performance of the Kalman filter algorithms with a real life air pollution
problem. Here ozone concentrations in a part of North West Europe are estimated and predicted. 相似文献
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