Balance-aware covariance localisation for atmospheric and oceanic ensemble Kalman filters

Computational Geosciences

Volumn 15, Issue 2, 2011, Pages 239-250

  Kepert, Jeffrey David ^a  

^a BUREAU OF METEOROLOGY   (Australia)

Author keywords

Balance; Covariance localisation; Ensemble Kalman filter

Indexed keywords

ATMOSPHERE; COVARIANCE ANALYSIS; DATA ASSIMILATION; KALMAN FILTER; NUMERICAL METHOD; OCEAN;

EID: 79952453006     PISSN: 14200597     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10596-010-9188-0     Document Type: Article

References (23)

1. Anderson, J. L.: A local least squares framework for ensemble filtering. Mon. Weather Rev. 131, 634-642 (2003).
2. Bourke, W. P.: An efficient, one-level, primitive-equation spectral model. Mon. Weather Rev. 100, 683-689 (1972).
3. Cullen, M. J. P.: Four-dimensional variational data assimilation: a new formulation of the background-error covariance matrix based on a potential-vorticity representation. Q. J. R. Meteorol. Soc. 129, 2777-2796 (2003).
4. Daley, R.: The analysis of synoptic scale divergence by a statistical interpolation procedure. Mon. Weather Rev. 113, 1066-1079 (1985).
5. Daley, R.: The application of variational methods to initialisation on the sphere. In: Sasaki, Y. K. (ed.) Variational Methods in Geosciences, pp. 3-12. Elsevier, Amsterdam (1986).
6. Daley, R.: Atmospheric data analysis. Cambridge University Press, 457 pp. (1991).
7. Fillion, L., Tanguay, M., Ek, N., Pagé, C., Pellerin, S.: Balanced coupling between vertical motion and diabatic heating for variational data assimilation. In: Int. Symp. on Nowcasting and Very Short Range Forecasting. World Meteorological Organization, Toulouse, France. Available online at: http://www. meteo. fr/cic/wsn05/DVD/resumes/longs/3. 10-89. pdf (2005).
8. Fisher, M.: Background error covariance modeling. In: Proc. ECMWF Seminar on Recent Developments in Data Assimilation for Atmosphere and Ocean, Reading, UK, pp. 45-63 (2003).
9. Gaspari, G., Cohn, S. E.: Construction of correlation functions in two and three dimensions. Q. J. R. Meteorol. Soc. 125, 723-757 (1999).
10. Hamill, T. M., Whitaker, J. S., Snyder, C.: Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter. Mon. Weather Rev. 129, 2776-2790 (2001).
11. Houtekamer, P. L., Mitchell, H. L.: Data assimilation using an ensemble Kalman filter technique. Mon. Weather Rev. 126, 796-811 (1998).
12. Houtekamer, P. L., Mitchell, H. L.: A sequential ensemble Kalman filter for atmospheric data assimilation. Mon. Weather Rev. 129, 123-137 (2001).
13. Houtekamer, P. L., Mitchell, H. L.: Ensemble Kalman filtering. Q. J. R. Meteorol. Soc. 131, 3269-3290 (2005).
14. Ide, K., Courtier, P., Ghil, M., Lorenc, A. C.: Unified notation for data assimilation: operational, sequential and variational. J. Meteorol. Soc. Jpn. 75, 181-189 (1997).
15. Kepert, J. D.: Covariance localisation and balance in an ensemble Kalman filter. Q. J. R. Meteorol. Soc. 135, 1157-1176 (2009).
16. Lorenc, A. C.: A global three-dimensional multivariate statistical interpolation scheme. Mon. Weather Rev. 109, 701-721 (1981).
17. Lorenc, A. C.: The potential of the ensemble Kalman filter for NWP-a comparison with 4D-Var. Q. J. R. Meteorol. Soc. 129, 3183-3203 (2003).
18. Mitchell, H. L., Houtekamer, P. L., Pellerin, G.: Ensemble size, balance, and model-error representation in an ensemble Kalman filter. Mon. Weather Rev. 130, 2791-2808 (2002).
19. Ott, E., Hunt, B. R., Szunyogh, I., Zimin, A. V., Kostelich, E. J., Corazza, M., Kalnay, E., Patil, D. J., Yorke, J. A.: A local ensemble Kalman filter for atmospheric data assimilation. Tellus 56A, 415-428 (2004).
20. Pagé, C., Fillion, L., Zwack, P.: Diagnosing summertime mesoscale vertical motion: implications for atmospheric data assimilation. Mon. Weather Rev. 135, 2076-2094 (2007).
21. Parrish, D. F., Derber, J. C.: The national meteorological center's spectral statistical-interpolation scheme. Mon. Weather Rev. 120, 1747-1763 (1992).
22. Williamson, D. L., Daley, R., Schlatter, T. W.: The balance between mass and wind fields resulting from multivariate optimal interpolation. Mon. Weather Rev. 109, 2357-2376 (1981).
23. Zupanski, M., Navonb, I. M., Zupanski, D.: The maximum likelihood ensemble filter as a non-differentiable minimization algorithm. Q. J. R. Meteorol. Soc. 134, 1039-1050 (2008).