Coupling ensemble Kalman filter with four-dimensional variational data assimilation

Fuqing Zhang, Meng Zhang, James A. Hansen

Research output: Contribution to journalArticlepeer-review

81 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1-8
Number of pages8
JournalAdvances in Atmospheric Sciences
Volume26
Issue number1
DOIs
StatePublished - 2009

All Science Journal Classification (ASJC) codes

  • Atmospheric Science

Fingerprint Dive into the research topics of 'Coupling ensemble Kalman filter with four-dimensional variational data assimilation'. Together they form a unique fingerprint.

Cite this