Coupling ensemble Kalman filter with four-dimensional variational data assimilation

Advances in Atmospheric Sciences

Volumn 26, Issue 1, 2009, Pages 1-8

  Zhang, Fuqing ^a   Zhang, Meng ^a   Hansen, James A ^b  

^a PENNSYLVANIA STATE UNIVERSITY   (United States)

^b Navy Research Laboratory   (United States)

Author keywords

Data assimilation; Ensemble Kalman filter; Four dimensional variational data assimilation; Hybrid method; Lorenz model

Indexed keywords

EID: 67649373088     PISSN: 02561530     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00376-009-0001-8     Document Type: Article

References (33)

1. Anderson, J. L., 2001: An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev., 129, 2284-2903.
2. Bishop, C. H., B. J. Etherton, and S. J. Majumdar, 2001: Adaptive sampling with the ensemble transform Kalman filter. Part I: Theoretical aspects. Mon. Wea. Rev., 129, 420-436.
3. Caya, A., J. Sun, and C. Snyder, 2005: A comparison between the 4Dvar and the ensemble Kalman filter for radar data assimilation. Mon. Wea. Rev., 133, 3081-3094.
4. Courtier, P., J.-N. Thepaut, and A. Hollingsworth, 1994: A strategy for operational implementation of 4D-Var using an incremental approach. Quart. J. Roy. Meteor. Soc., 120, 1367-1387.
5. Evensen, G., 1994: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99, 10143-10162.
6. Evensen, G., and P. J. van Leeuwen, 2000: An ensemble Kalman smoother for nonlinear dynamics. Mon. Wea. Rev., 128, 1852-1867.
7. Fertig, E., J. Harlim, and B. Hunt, 2007. A comparative study of 4DVar and 4D ensemble Kalman filter: Perfect model simulations with Lorenz-96. Tellus, 59, 96-101.
8. Gaspari, G., and S. Cohn, 1999: Construction of correlation functions in two and three dimensions. Quart. J. Roy. Meteor. Soc., 125, 723-757.
9. Hamill, T. M., and C. Snyder, 2000: A hybrid ensemble Kalman filter-3D variational analysis scheme. Mon. Wea. Rev., 128, 2905-2919.
10. Hansen, J. A., 2002: Accounting for model error in ensemble-based state estimation and forecasting. Mon. Wea. Rev., 130, 2373-2391.
11. Houtekamer, P. L., and H. L. Mitchell, 1998: Data assimilation using an ensemble Kalman filter technique. Mon. Wea. Rev., 126, 796-811.
12. Houtekamer, P. L., and H. L. Mitchell, 2001: A sequential ensemble Kalman filter for atmospheric data assimilation. Mon. Wea. Rev., 129, 123-137.
13. Hunt, B. R., and Coauthors, 2004: Four-dimensional ensemble Kalman filtering. Tellus, 56A, 273-277.
14. Kalnay, E., 2003: Atmospheric Modeling, Data Assimilation and Predictability. Cambridge University Press, New York, 341pp.
15. Lewis, J. M., and J. C. Derber, 1985: The use of adjoint equations to solve a variational adjustment problem with advective constraints. Tellus, 37A, 309-322.
16. Liu, C., Q. Xiao, and B. Wang, 2008: An ensemblebased four-dimensional variational data assimilation scheme. Part I: Technical formulation and preliminary test. Mon. Wea. Rev., 136(9), 3367-3376.
17. Liu, D. C., and J. Nocedal, 1989: On the limited memory BFGS method for large scale optimization. Mathematical Programming, 45, 503-528.
18. Lorenc, A., 2003: The potential of the Ensemble Kalman Filter for NWP: A comparison with 4DVar. Quart. J. Roy. Meteor. Soc., 129, 3183-3203.
19. Lorenz, E. N., 1996: Predictability - A problem partly solved. In "Predictability", ECMWF, Seminar Proceedings, Shinfield Park, Reading, RG2 9AX, 1-18.
20. Meng, Z., and F. Zhang, 2007: Test of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part II: Imperfect model experiments. Mon. Wea. Rev., 135, 1403-1423.
21. Meng, Z., and F. Zhang, 2008a: Test of an ensemble-Kalman filter for mesoscale and regional-scale data assimilation. Part III: Comparison with 3Dvar in a real-data case study. Mon. Wea. Rev., 136, 522-540.
22. Meng, Z., and F. Zhang, 2008b: Test of an ensemble-Kalman filter for mesoscale and regional-scale data assimilation. Part IV: Performance over a warmseason month of June 2003. Mon. Wea. Rev., 136, 3671-3682.
23. Navon, I. M., D. N. Daescu, and Z. Liu, 2005: The impact of background error on incomplete observations for 4D-Var data assimilation with the FSU GSM. Computational Science-ICCS 2005, PT 2, 3515, 837-844.
24. Rabier, F., J. N. Thepaut, and P. Courtier, 1998: Extended assimilation and forecast experiments with a four-dimensional variational assimilation system. Quart. J. Roy. Meteor. Soc., 124, 1861-1887.
25. Snyder, C., and F. Zhang, 2003: Assimilation of simulated Doppler radar observations with an ensemble Kalman filter. Mon. Wea. Rev., 131, 1663-1677.
26. Sun, J., and N. A. Crook, 1997: Dynamical and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint. Part I: Model development and simulated data experiments. J. Atmos. Sci., 54, 1642-1661.
27. Talagrand, O., 1997: Assimilation of observations, an introduction. J. Meteor. Soc. Japan, 75, 191-209.
28. Wang, X., C. Snyder, and T. M. Hamill, 2007: On the theoretical equivalence of differently proposed ensemble-3DVAR hybrid analysis schemes. Mon. Wea. Rev., 135, 222-227.
29. Whitaker, J. S., and T. M. Hamill, 2002: Ensemble data assimilation without perturbed observations. Mon. Wea. Rev., 130, 1923.
30. Zhang, F., C. Snyder, and J. Sun, 2004: Tests of an ensemble Kalman filter for convective-scale data assimilation: Impact of initial estimate and observations. Mon. Wea. Rev., 132, 1238-1253.
31. Zhang, F., Z. Meng, and A. Aksoy, 2006: Test of an ensemble-Kalman filter for mesoscale and regionalscale data assimilation. Part I: Perfect-model experiments. Mon. Wea. Rev., 134, 722-736.
32. Zou, X., 1997: Tangent linear and adjoint of "on/off" processes and their feasibility for use in 4-dimensional variational data assimilation. Tellus, 49A, 3-31.
33. Zupanski, M., 2005: Maximum likelihood ensemble filter: Theoretical aspects. Mon. Wea. Rev., 133, 1710-1726.