J1.10
Evaluation of reduced-rank Kalman filters (RRKF)
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.
Joint Session 1, Data Assimilation and Observational Network Design. Part I (Joint between the Symposium on Forecasting the Weather and Climate of the Atmosphere and Ocean and the 20th Conference on Weather Analysis and Forecasting/16th Conference on Numerical Weather Prediction) (ROOM 6A)
Monday, 12 January 2004, 9:00 AM-12:15 PM, Room 6A
Previous paper