Comparison of Statistical Dynamical, Square Root and Ensemble Kalman Filters
نویسندگان
چکیده
منابع مشابه
Comparison of Statistical Dynamical, Square Root and Ensemble Kalman Filters
We present a statistical dynamical Kalman filter and compare its performance to deterministic ensemble square root and stochastic ensemble Kalman filters for error covariance modeling with applications to data assimilation. Our studies compare assimilation and error growth in barotropic flows during a period in 1979 in which several large scale atmospheric blocking regime transitions occurred i...
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The use of perturbed observations in the traditional ensemble Kalman filter (EnKF) results in a suboptimal filter behaviour, particularly for small ensembles. In this work, we propose a simple modification to the traditional EnKF that results in matching the analysed error covariance given by Kalman filter in cases when the correction is small; without perturbed observations. The proposed filte...
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Ensemble Kalman filters are developed for turbulent dynamical systems where the forecast model does not resolve all the active scales of motion. These filters are based on the assumption that a coarse-resolution model is intended to predict the large-scale part of the true dynamics; since observations invariably include contributions from both the resolved large scales and the unresolved small ...
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Recently various versions of ensemble Kalman filters (EnKF) has been proposed and studied. This work concerns, in a mathematically rigorous manner, the relative performance of two major versions of EnKF when the forecast ensemble is non-Gaussian. The approach is based on the stability of the filtering methods against small model violations, using the expected squared L2 distance as a measure of...
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ژورنال
عنوان ژورنال: Entropy
سال: 2008
ISSN: 1099-4300
DOI: 10.3390/e10040684