8B.3 Comparison of different ensemble-based Kalman filters in data assimilation for strongly nonlinear dynamics

Thursday, 28 June 2007: 8:30 AM
Summit B (The Yarrow Resort Hotel and Conference Center)
Zhaoxia Pu, University of Utah, Salt Lake City, UT

This study examines different ensemble based Kalman filters in data assimilation with highly nonlinear Lorenz equations. Four configurations of the ensemble-based Kalman filtering data assimilation techniques, including ensemble Kalman filter (Evensen 1994), ensemble adjustment Kalman filter (Anderson 2001), ensemble square root filter (Whitaker and Hamill 2002) and ensemble transform Kalman filter (Bishop 2001), are compared in terms of their sensitivity to both observational and model errors. The sensitivity of each ensemble-based filters to the size of the ensemble is also discussed. Further comparison is made by comparing the ensemble-based Kalman filters with both 3-diemnsiaonal and 4-dimensioanl variation data assimilation methods.
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