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| Titel |
Improving the ensemble transform Kalman filter using a second-order Taylor approximation of the nonlinear observation operator |
| VerfasserIn |
G. Wu, X. Yi, L. Wang, X. Liang, S. Zhang, X. Zhang, X. Zheng |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 21, no. 5 ; Nr. 21, no. 5 (2014-09-23), S.955-970 |
| Datensatznummer |
250120941
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| Publikation (Nr.) |
 copernicus.org/npg-21-955-2014.pdf |
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| Zusammenfassung |
The ensemble transform Kalman filter (ETKF) assimilation scheme has recently
seen rapid development and wide application. As a specific implementation of
the ensemble Kalman filter (EnKF), the ETKF is computationally more efficient
than the conventional EnKF. However, the current implementation of the ETKF
still has some limitations when the observation operator is strongly
nonlinear. One problem in the minimization of a nonlinear objective function
similar to 4D-Var is that the nonlinear operator and its tangent-linear
operator have to be calculated iteratively if the Hessian is not
preconditioned or if the Hessian has to be calculated several times. This may
be computationally expensive. Another problem is that it uses the
tangent-linear approximation of the observation operator to estimate the
multiplicative inflation factor of the forecast errors, which may not be
sufficiently accurate.
This study attempts to solve these problems. First, we apply the second-order
Taylor approximation to the nonlinear observation operator in which the
operator, its tangent-linear operator and Hessian are calculated only once.
The related computational cost is also discussed. Second, we propose a scheme
to estimate the inflation factor when the observation operator is strongly
nonlinear. Experimentation with the Lorenz 96 model shows that using the
second-order Taylor approximation of the nonlinear observation operator leads
to a reduction in the analysis error compared with the traditional linear
approximation method. Furthermore, the proposed inflation scheme leads to a
reduction in the analysis error compared with the procedure using the
traditional inflation scheme. |
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