Coupling Ensemble Kalman Filter with Four-dimensional               Variational Data Assimilation

Fuqing ZHANG; Meng ZHANG; James A. HANSEN

doi:10.1007/s00376-009-0001-8

Impact Factor: 5.8

Volume 26 Issue 1

Jan. 2009

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Article > ADVANCES IN ATMOSPHERIC SCIENCES > 2009 > 26(1): 1-8

Coupling Ensemble Kalman Filter with Four-dimensional Variational Data Assimilation

1.
Department of Meteorology, Pennsylvania State University, University Park, Pennsylvania, USA;Department of Meteorology, Pennsylvania State University, University Park, Pennsylvania, USA;Navy Research Laboratory, Monterrey, California, USA

doi: 10.1007/s00376-009-0001-8

Abstract
References
Related papers
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Abstract:
This study examines the performance of coupling the deterministic four-dimensional variational assimilation system (4DVAR) with an ensemble Kalman filter (EnKF) to produce a superior hybrid approach for data assimilation. The coupled assimilation scheme (E4DVAR) benefits from using the state-dependent uncertainty provided by EnKF while taking advantage of 4DVAR in preventing filter divergence: the 4DVAR analysis produces posterior maximum likelihood solutions through minimization of a cost function about which the ensemble perturbations are transformed, and the resulting ensemble analysis can be propagated forward both for the next assimilation cycle and as a basis for ensemble forecasting. The feasibility and effectiveness of this coupled approach are demonstrated in an idealized model with simulated observations. It is found that the E4DVAR is capable of outperforming both 4DVAR and the EnKF under both perfect- and imperfect-model scenarios. The performance of the coupled scheme is also less sensitive to either the ensemble size or the assimilation window length than those for standard EnKF or 4DVAR implementations.
- data assimilation,
- four-dimensional variational data assimilation,
- ensemble Kalman filter,
- Lorenz model,
- hybrid method
References

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Manuscript History

Manuscript received: 10 January 2009

Manuscript revised: 10 January 2009

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Coupling Ensemble Kalman Filter with Four-dimensional Variational Data Assimilation

1. Department of Meteorology, Pennsylvania State University, University Park, Pennsylvania, USA;Department of Meteorology, Pennsylvania State University, University Park, Pennsylvania, USA;Navy Research Laboratory, Monterrey, California, USA

Manuscript received: 2009-01-10
Manuscript revised: 2009-01-10

Abstract: This study examines the performance of coupling the deterministic four-dimensional variational assimilation system (4DVAR) with an ensemble Kalman filter (EnKF) to produce a superior hybrid approach for data assimilation. The coupled assimilation scheme (E4DVAR) benefits from using the state-dependent uncertainty provided by EnKF while taking advantage of 4DVAR in preventing filter divergence: the 4DVAR analysis produces posterior maximum likelihood solutions through minimization of a cost function about which the ensemble perturbations are transformed, and the resulting ensemble analysis can be propagated forward both for the next assimilation cycle and as a basis for ensemble forecasting. The feasibility and effectiveness of this coupled approach are demonstrated in an idealized model with simulated observations. It is found that the E4DVAR is capable of outperforming both 4DVAR and the EnKF under both perfect- and imperfect-model scenarios. The performance of the coupled scheme is also less sensitive to either the ensemble size or the assimilation window length than those for standard EnKF or 4DVAR implementations.

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