4-D-Var or ensemble Kalman filter?

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Kalnay, Eugenia ; Hong, Li ; Miyoshi, Takemasa ; Yang, Shu-Chih ; Ballabrera-Poy, Joaquim (2007)

We consider the relative advantages of two advanced data assimilation systems, 4-D-Var and ensemble Kalman filter (EnKF), currently in use or under consideration for operational implementation. With the Lorenz model, we explore the impact of tuning assimilation parameters such as the assimilation window length and background error covariance in 4-D-Var, variance inflation in EnKF, and the effect of model errors and reduced observation coverage. For short assimilation windows EnKF gives more accurate analyses. Both systems reach similar levels of accuracy if long windows are used for 4-D-Var. For infrequent observations, when ensemble perturbations grow non-linearly and become non-Gaussian, 4-D-Var attains lower errors than EnKF. If the model is imperfect, the 4-D-Var with long windows requires weak constraint. Similar results are obtained with a quasi-geostrophic channel model. EnKF experiments made with the primitive equations SPEEDY model provide comparisons with 3-D-Var and guidance on model error and ‘observation localization’. Results obtained using operational models and both simulated and real observations indicate that currently EnKF is becoming competitive with 4-D-Var, and that the experience acquired with each of these methods can be used to improve the other. A table summarizes the pros and cons of the two methods.
  • References (69)
    69 references, page 1 of 7

    Anderson, J. L., 2001. An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev. 129, 2884-2903.

    Andersson, E., Fisher, M., Holm, E., Isaksen, L., Radnoti, G. and coauthors. 2005. Will the 4D-Var approach be defeated by nonlinearity? ECMWF Tech Memo 479. Available at: www.ecmwf.int/publications.

    Baek, S.-J., Hunt, B. R., Kalnay, E., Ott, E. and Szunyogh, I., 2006. Local ensemble Kalman filtering in the presence of model bias. Tellus 58, 293-306.

    Bengtsson, L. and Hodges, K. I., 2005. On the impact of humidity observations in numerical weather prediction. Tellus 57A, 701-708.

    Bishop, C. H., Etherton, B. J. and Majumdar, S. J., 2001. Adaptive sampling with ensemble transform Kalman filter. Part I: theoretical aspects. Mon. Wea. Rev. 129, 420-436.

    Burgers, G., van Leeuwen, P. J. and Evensen, G., 1998. On the analysis scheme in the ensemble Kalman filter. Mon. Wea. Rev. 126, 1719- 1724.

    Cohn, S., Da Silva, A., Guo, J., Sienkiewicz, M. and Lamich, D., 1998. Assessing the effects of data selection with the DAO physical-space statistical analysis system. Mon. Wea. Rev. 126, 2913-2926.

    Corazza, M., Kalnay, E., Patil, D. J., Ott, E., Yorke, J. A. and coauthors. 2002. Use of the breeding technique in the estimation of the background error covariance matrix for a quasi-geostrophic model. Paper 6.4 in the AMS Symposium on Observations, Data Assimilation and Probabilistic Prediction, Orlando, FA, January 14-17 2002. Available at: ams.confex.com/ams/pdfpapers/28755.pdf.

    Corazza, M., Kalnay, E., Patil, D. J., Yang, S.-C., Morss, R. and coauthors. 2003. Use of the breeding technique to estimate the structure of the analysis “error of the day”. Nonlinear Processes in Geophysics 10, 233-243.

    Corazza, M., Kalnay, E. and Yang, S.-C., 2007. An implementation of the local ensemble Kalman filter in a quasigeostrophic model and comparison with 3D-Var. Nonlinear Proc. Phys. 14, 89-101.

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