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Estimating and Correcting Model Errors in the Ensemble Kalman Filter

Estimating and Correcting Model Errors in the Ensemble Kalman Filter

Hong Li , Eugenia Kalnay, Takemasa Miyoshi and Christopher M. Danforth

Department of Atmospheric and Oceanic Science, University of Maryland

The main goal of this work is to investigate techniques for treating model errors in the ensemble Kalman filter, and to develop a data assimilation system capable of assimilating real weather observations. An ensemble based data assimilation scheme - local ensemble transform Kalman filter (LETKF, Hunt et al. 2006) is applied to the SPEEDY primitive equation global model (Molteni 2003). The model errors are introduced by assimilating observations from the NCEP/NCAR reanalysis data. The effect of model errors on LETKF is investigated. To deal with the model error, several model error correction methods are tested, including the 'covariance inflation', the Danforth et al(2006) low-order method, the Dee and da Silva method (1998) and its simplified version (Radakovich et al 2001). The performances of these methods are investigated and compared under the different observational networks.