Sampling errors in ensemble Kalman filtering. Part II: Application to a barotropic model

Monthly Weather Review

Volumn 137, Issue 5, 2009, Pages 1640-1654

  Sacher, William ^a   Bartello, Peter ^a  

^a MCGILL UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ANALYTIC EXPRESSIONS; BAROTROPIC; BAROTROPIC MODELS; COMPUTATIONAL COSTS; ENSEMBLE KALMAN FILTER; ENSEMBLE KALMAN FILTERING; QUASI-GEOSTROPHIC MODEL; SAMPLING ERRORS; THEORETICAL RESULT;

CONTROL THEORY;

KALMAN FILTERS;

ACCURACY ASSESSMENT; BAROTROPIC MODE; COMPUTER SIMULATION; ENSEMBLE FORECASTING; ERROR ANALYSIS; KALMAN FILTER; NUMERICAL MODEL; QUASI-GEOSTROPHIC FLOW; SAMPLING; WEATHER FORECASTING;

EID: 68249140015     PISSN: 00270644     EISSN: None     Source Type: Journal    
DOI: 10.1175/2008MWR2685.1     Document Type: Article

References (29)

1. Anderson, J. L., 2001: An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev., 129, 2884-2903.
2. Anderson, J. L., and S. L. Anderson, 1999: A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts. Mon. Wea. Rev., 127, 2741-2758.
3. Bouttier, F., 1994: A dynamical estimation of forecast error covariances in an assimilation system. Mon. Wea. Rev., 122, 2376-2390.
4. Charron, M., P. L. Houtekamer, and P. Bartello, 2006: Assimilation with an ensemble Kalman filter of synthetic radial wind data in anisotropic turbulence: Perfect model experiments. Mon. Wea. Rev., 134, 618-637.
5. Evensen, G., 1992: Using the extended Kalman filter with a multilayer quasi-geostrophic ocean model. J. Geophys. Res., 97 (C11), 17 905-17 924.
6. Evensen, G., 2003: The ensemble Kalman filter:Theoretical formulation and practical implementation. Ocean Dyn., 53, 343-367.
7. Furrer, R., and T. Bengtsson, 2007: Estimation of high-dimensional prior and posterior covariance matrices in Kalman filter variants. J. Multivariate Anal., 98, 227-255.
8. Gauthier, P., P. Courtier, and P. Moll, 1993: Assimilation of simulated wind lidar data with a Kalman filter. Mon. Wea. Rev., 121, 1803-1820.
9. Hamill, T. M., and C. Snyder, 2000: A hybrid ensemble Kalman filter - 3D variational analysis scheme. Mon. Wea. Rev., 128, 2905-2919.
10. Hamill, T. M., and J. S. Whitaker, 2005: Accounting for the error due to unresolved scales in ensemble data assimilation: A comparison of different approaches. Mon. Wea. Rev., 133, 3132-3147.
11. Hamill, T. M., J. S. Whitaker, and C. Snyder, 2001: Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter. Mon. Wea. Rev., 129, 2776-2790.
12. Houtekamer, P. L., and H. L. Mitchell, 1998: Data assimilation using an ensemble Kalman filter technique. Mon. Wea. Rev., 126, 796-811.
13. Houtekamer, P. L., and H. L. Mitchell, 2001: A sequential ensemble Kalman filter for atmospheric data assimilation. Mon. Wea. Rev., 129, 123-137.
14. Houtekamer, P. L., and H. L. Mitchell, 2005: Ensemble Kalman filtering. Quart. J. Roy. Meteor. Soc., 131, 3269-3289.
15. Houtekamer, P. L., H. L. Mitchell, G. Pellerin, M. Buehner, M. Charron, L. Spacek, and B. Hansen, 2005: Atmospheric data assimilation with an ensemble Kalman filter: Results with real observations. Mon. Wea. Rev., 133, 604-620.
16. Karspeck, A. R., and J. L. Anderson, 2007: Experimental implementation of an ensemble adjustment filter for an intermediate ENSO model. J. Climate, 20, 4638-4658.
17. Lawson, W. G., and J. A. Hansen, 2004: Implications of stochastic and deterministic filters as ensemble-based data assimilation methods in varying regimes of error growth. Mon. Wea. Rev., 132, 1966-1981.
18. Mitchell, H. L., P. L. Houtekamer, and G. Pellerin, 2002: Ensemble size, balance, and model-error representation in an ensemble Kalman filter. Mon. Wea. Rev., 130, 2791-2808.
19. Orszag, S. A., 1971: Numerical simulation of incompressible flow within simple boundaries (I). Galerkin (spectral) representations. Stud. Appl. Math., 50, 293-327.
20. Ott, E., and Coauthors, 2004: A local ensemble Kalman filter for atmospheric data assimilation. Tellus, 56A, 415-428.
21. Pham, D. T., J. Verron, and M. C. Roubaud, 1998: A singular evolutive extended Kalman filter for data assimilation in oceanography. J. Mar. Syst., 16, 323-340.
22. Sacher, W., and P. Bartello, 2008: Sampling errors in ensemble Kalman filtering. Part I: Theory. Mon. Wea. Rev., 136, 3035-3049.
23. Smith, L. A., and J. A. Hansen, 2004: Extending the limits of ensemble forecast verification with the minimum spanning tree. Mon. Wea. Rev., 132, 1522-1528.
24. Snyder, C., and F. Zhang, 2003: Assimilation of simulated Doppler radar observations with an ensemble Kalman filter. Mon. Wea. Rev., 131, 1663-1677.
25. Tanguay, M., P. Bartello, and P. Gauthier, 1995: Four-dimensional data assimilation with awide range of scales. Tellus, 47A, 974-997.
26. Tippett, M. K., J. L. Anderson, C. H. Bishop, T. M. Hamill, and J. Whitaker, 2003: Ensemble square root filters. Mon. Wea. Rev., 131, 1485-1490.
27. Tong, M., and M. Xue, 2005: Ensemble Kalman filter assimilation of Doppler radar data with a compressible nonhydrostatic model: OSS experiments. Mon. Wea. Rev., 133, 1789-1807.
28. van Leeuwen, P. J., 1999: Comments on "Data assimilation using an ensemble Kalman filter technique." Mon. Wea. Rev., 127, 1374-1377.
29. Whitaker, J. S., and T. M. Hamill, 2002: Ensemble data assimilation without perturbed observations. Mon. Wea. Rev., 130, 1913-1924.