JP1.21
A Comparison of Ensemble Based Data Assimilation Schemes
Brian J. Etherton, Penn State University, University Park, PA; and C. H. Bishop
The ability of various ensemble based approximations to the extended Kalman Filter to estimate the state of a two dimensional turbulent flow with two differing types of model error is explored. Several ensemble generation techniques are investigated in the framework of the simple barotropic model: Perturbed Observations, Singular Vectors, Bred Vectors, Monte Carlo methods, as well as using the Ensemble Transform Kalman Filter (ET KF) to produce the perturbations.
The different perturbation methods are compared to determine which approach yields the smallest vorticity forecast errors. In all cases, it is found that hybrid ensemble Kalman filters out perform 3D-Var. Further, the hybrid ensemble Kalman filter with a forecast error covariance model produced by an ET KF generated ensemble is competitive with other more computationally expensive hybrid ensemble Kalman filters.
Joint Poster Session 1, Ensemble Forecasting and Other Topics in Probability and Statistics (Joint with the 16th Conference on Probability and Statistics in the Atmospheric Sciences and the Symposium onObservations, Data Assimilation,and Probabilistic Prediction)
Wednesday, 16 January 2002, 1:30 PM-3:00 PM
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