Scalable implementations of ensemble filter algorithms for data assimilation

Journal of Atmospheric and Oceanic Technology

Volumn 24, Issue 8, 2007, Pages 1452-1463

  Anderson, Jeffrey L ^a   Collins, Nancy ^a  

^a NATIONAL CENTER FOR ATMOSPHERIC RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DATA PROCESSING; KALMAN FILTERS; LEAST SQUARES APPROXIMATIONS; OPTIMIZATION; PARALLEL PROCESSING SYSTEMS;

DATA ASSIMILATION; ENSEMBLE KALMAN FILTER; PARALLEL FILTER; SEQUENTIAL FILTER;

METEOROLOGY;

ALGORITHMS; DATA PROCESSING; KALMAN FILTERS; LEAST SQUARES APPROXIMATIONS; METEOROLOGY; OPTIMIZATION; PARALLEL PROCESSING SYSTEMS;

ALGORITHM; DATA ASSIMILATION; ESTIMATION METHOD; KALMAN FILTER; LEAST SQUARES METHOD; MODELING; PARALLEL COMPUTING;

EID: 34548625197     PISSN: 07390572     EISSN: None     Source Type: Journal    
DOI: 10.1175/JTECH2049.1     Document Type: Article

References (29)

1. Anderson, J. L., 2001: An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev., 129, 2894-2903.
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 an 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. Bormann, N., S. Saarinen, G. Kelly, and J.-N. Thépaut, 2003: The spatial stucture of observation errors in atmospheric motion vectors from geostationary satellite data. Mon. Wea. Rev., 131, 706-718.
6. Burgers, G., P. J. van Leeuwen, and G. Evensen, 1998: Analysis scheme in the ensemble Kalman filter. Mon. Wea. Rev., 126, 1719-1724.
7. Chui, C. K., and G. Chen, 1987: Kalman Filtering: With Real-Time Applications. Springer, 191 pp.
8. Collins, W. D., and Coauthors, 2004: Description of the NCAR Community Atmosphere Model (CAM 3.0). NCAR Tech. Note NCARITN-464+STR, 226 pp.
9. Evensen, G., 1994: Sequential data assimilation with a nonlinear quasigeostrophic model using Monte Carlo methods to do forecast error statistics. J. Geophys. Res., 99 (C5), 10 143-10 162.
10. Gaspari, G., and S. E. Cohn, 1999: Construction of correlation functions in two and three dimensions. Quart. J. Roy. Meteor. Soc., 125, 723-757.
11. 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.
12. 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.
13. Houtekamer, P. L., and H. L. Mitchell, 1998: Data assimilation using an ensemble Kalman filter technique. Mon. Wea. Rev., 126, 796-811.
14. Houtekamer, P. L., and H. L. Mitchell, 2001: A sequential ensemble Kalman filter for atmospheric data assimilation. Mon. Wea. Rev., 129, 123-137.
15. Houtekamer, P. L., and H. L. Mitchell, 2005: Ensemble Kalman filtering. Quart. J. Roy. Meteor. Soc., 131, 3269-3289.
16. Houtekamer, P. L., H. L. Mitchell, G. Pellerin, M. Buehner, M. Charron, L. Spacek, and B. Hansen, 2005: Atmospheric data assimilation with the ensemble Kalman filter: Results with real observations. Mon. Wea. Rev., 133, 604-620.
17. Ide, K., P. Courtier, M. Ghil, and A. C. Lorenc, 1997: Unified notation for data assimilation: Operational sequential and variational. J. Meteor. Soc. Japan, 75B, 181-189.
18. Jazwinski, A. H., 1970: Stochastic Processes and Filtering Theory. Academic Press, 376 pp.
19. Kalman, R. E., 1960: A new approach to linear filtering and prediction problems. Trans. AMSE J. Basic Eng., 82D, 35-45.
20. Keppenne, C. L., and M. M. Rienecker, 2002: Initial testing of a massively parallel ensemble Kalman filter with the Poseidon isopycnal ocean general circulation model. Mon. Wea. Rev., 130, 2951-2965.
21. Kistler, R., W. Collins, S. Saha, G. White, and J. Woolen, 2001: The NCEP-NCAR 50-Year Reanalysis: Monthly means CD-ROM and documentation. Bull. Amer. Meteor. Soc., 82, 247-267.
22. Mitchell, H. L., and P. L. Houtekamer, 2000: An adaptive ensemble Kalman filter. Mon. Wea. Rev., 128, 416-433.
23. 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.
24. Ott, E., and Coauthors, 2004: A local ensemble Kalman filter for atmospheric data assimilation. Tellus, A56, 415-428.
25. Pham, D. T., 2001: Stochastic methods for sequential data assimilation in strongly nonlinear systems. Mon. Wea. Rev., 129, 1194-1207.
26. Tarantola, A., 1987: Inverse Problem Theory. Elsevier, 613 pp.
27. 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.
28. Whitaker, J. S., and T. M. Hamill, 2002: Ensemble data assimilation without perturbed observations. Mon. Wea. Rev., 130, 1913-1924.
29. Zhang, S., M. J. Harrison, A. T. Wittenberg, A. Rosati, J. L. Anderson, and V. Balaji, 2005: Initialization of an ENSO forecast system using a parallelized ensemble filter. Mon. Wea. Rev., 133, 3176-3201.