The Data Assimilation Approach in a Multilayered Uncertainty Space
Martin Drieschner,
Clemens Herrmann,
Yuri Petryna
Abstract:The simultaneous consideration of a numerical model and of different observations can be achieved using data-assimilation methods. In this contribution, the ensemble Kalman filter (EnKF) is applied to obtain the system-state development and also an estimation of unknown model parameters. An extension of the Kalman filter used is presented for the case of uncertain model parameters, which should not or cannot be estimated due to a lack of necessary measurements. It is shown that incorrectly assumed probability … Show more
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