Comparison of ensemble Kalman filters under non-Gaussianity

Monthly Weather Review

Volumn 138, Issue 4, 2010, Pages 1293-1306

  Lei, Jing ^a   Bickel, Peter ^a   Snyder, Chris ^b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b NATIONAL CENTER FOR ATMOSPHERIC RESEARCH   (United States)

Author keywords

Ensembles; Kalman filters

Indexed keywords

EMPIRICAL STUDIES; ENSEMBLE KALMAN FILTER; ENSEMBLES; FILTERING METHOD; FREE PARAMETERS; GAUSSIAN ASSUMPTION; NON-GAUSSIAN; NONGAUSSIANITY; RELATIVE PERFORMANCE; SQUARE ROOT KALMAN FILTER; STOCHASTIC FILTERS;

STOCHASTIC SYSTEMS;

KALMAN FILTERS;

ALGORITHM; COMPARATIVE STUDY; ENSEMBLE FORECASTING; NUMERICAL MODEL; PERFORMANCE ASSESSMENT; STOCHASTICITY; WEATHER FORECASTING;

EID: 77955540665     PISSN: 00270644     EISSN: None     Source Type: Journal    
DOI: 10.1175/2009MWR3133.1     Document Type: Article

References (21)

1. Anderson, J. L., 2001: An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev., 129, 2884-2903.
2. Anderson, J. L., 2003: A local least squares framework for ensemble filtering. Mon. Wea. Rev., 131, 634-642.
3. Anderson, J. L., 2007: Exploring the need for localization in ensemble data assimilation using a hierarchical ensemble filter. Physica D, 230, 99-111.
4. 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.
5. Bengtsson, T., C. Snyder, and D. Nychka, 2003: Toward a nonlinear ensemble filter for high-dimensional systems. J. Geophys. Res., 108, 8775, doi:10.1029/2002JD002900.
6. Bishop, C. H., B. Etherton, and S. J. Majumdar, 2001: Adaptive sampling with the ensemble transformation Kalman filter. Part I: Theoretical aspects. Mon. Wea. Rev., 129, 420-436.
7. Evensen, G., 1994: Sequential data assimilation with a non-linear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99 (C5), 10 143-10 162.
8. Evensen, G., 2003: The ensemble Kalman filter: Theoretical formulation and practical implementation. Ocean Dyn., 53, 343-367.
9. Evensen, G., 2007: Data Assimilation: The Ensemble Kalman Filter. Springer, 279 pp.
10. Furrer, R., and T. Bengtsson, 2007: Estimation of high-dimensional prior and posterior covariance matrices in Kalman filter variants. J. Multivar. Anal., 98, 227-255.
11. Hampel, F., E. Ronchetti, P. Rousseeuw, and W. Stahel, 1986: Robust Statistics: The Approach Based on Influence Functions. Wiley, 502 pp.
12. Houtekamer, P. L., and H. L. Mitchell, 1998: Data assimilation using an ensemble Kalman filter technique. Mon. Wea. Rev., 126, 796-811.
13. Lawson, G. W., 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.
14. Livings, D. M., S. L. Dance, and N. K. Nicols, 2008: Unbiased ensemble square root filters. Physica D, 237, 1021-1028.
15. Lorenz, E. N., 1963: Deterministic nonperiodic flow. J. Atmos. Sci., 20, 130-141.
16. Ott, E., and Coauthors, 2004: A local ensemble Kalman filter for atmospheric data assimilation. Tellus, 56A, 415-428.
17. Sacher, W., and P. Bartello, 2008: Sampling errors in ensemble Kalman filtering. Part I: Theory. Mon. Wea. Rev., 136, 3035-3049.
18. Sacher, W., and P. Bartello, 2009: Sampling errors in ensemble Kalman filtering. Part II: Application to a barotropic model. Mon. Wea. Rev., 137, 1640-1654.
19. Sakov, P., and P. R. Oke, 2008: Implications of the form of the ensemble transformation in the ensemble square root filters. Mon. Wea. Rev., 136, 1042-1053.
20. Tippett, M. K., J. L. Anderson, C. H. Bishop, T. M. Hamill, and J. S. Whitaker, 2003: Ensemble square root filters. Mon. Wea. Rev., 131, 1485-1490.
21. Whitaker, J. S., and T. M. Hamill, 2002: Ensemble data assimilation without perturbed observations. Mon. Wea. Rev., 130, 1913-1924.