84th AMS Annual Meeting

Monday, 12 January 2004: 12:00 PM
Evaluation of reduced-rank Kalman filters (RRKF)
Room 6A
Michael Fisher, ECMWF, Reading, Berks., United Kingdom; and A. Hollingsworth
A reduced rank Kalman filter has been developed and tested in the context of the 4D-Var data assimilation system at ECMWF. The RRKF modifies the 4D-Var background constraint by propagating covariances within a small subspace, according to the Kalman filter equations. The subspace can be defined either by a set of Hessian singular vectors, or as a balanced-truncation subspace, as proposed by Farrell and Ioannou (2001). It is demonstrated that the RRKF is beneficial in the context of a low resolution, quasi-geostrophic model. However, no significant benefit was apparent, for affordable subspace size, when the method was applied to a high resolution NWP system.

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