Ensemble-based Kalman filters in strongly nonlinear dynamics |
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Authors: | Zhaoxia Pu Joshua Hacker |
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Institution: | Department of Atmospheric Sciences, University of Utah, USA;National Center for Atmospheric Research, USA |
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Abstract: | This study examines the effectiveness of ensemble Kalman filters in data assimilation
with the strongly nonlinear dynamics of the Lorenz-63 model, and in particular their use in
predicting the regime transition that occurs when the model jumps from one basin of attraction
to the other. Four configurations of the ensemble-based Kalman filtering data assimilation techniques,
including the ensemble Kalman filter, ensemble adjustment Kalman filter, ensemble square root filter
and ensemble transform Kalman filter, are evaluated with their ability in predicting the regime
transition (also called phase transition) and also are compared in terms of their sensitivity to
both observational and sampling errors. The sensitivity of each ensemble-based filter to the size
of the ensemble is also examined. |
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Keywords: | ensemble Kalman filter nonlinear data assimilation Lorenz model |
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