Ensemble covariances adaptively localized with ECO-RAP. Part 2: A strategy for the atmosphere

Tellus, Series A: Dynamic Meteorology and Oceanography

Volumn 61 A, Issue 1, 2009, Pages 97-111

  Bishop, Craig H ^a   Hodyss, Daniel ^a  

^a NAVAL RESEARCH LABORATORY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COVARIANCE ANALYSIS; DATA ASSIMILATION; ENSEMBLE FORECASTING; KALMAN FILTER; WEATHER FORECASTING;

EID: 58049115522     PISSN: 02806495     EISSN: 16000870     Source Type: Journal    
DOI: 10.1111/j.1600-0870.2008.00372.x     Document Type: Article

References (27)

1. Bishop, C. H. and Hodyss, D. 2007. Flow adaptive moderation of spurious ensemble correlations and its use in ensemble-based data assimilation. Quart. J. Roy. Meteor. Soc. 133, 2029-2044.
2. Bishop, C. H. and Hodyss, D. 2009. Ensemble covariances adaptively localized with ECO-RAP. Part 1: Tests on simple error models. Tellus 61A, 84-96.
3. Bishop, C. H. and Toth, Z. 1999. Ensemble transformation and adaptive observations. J. Atmos. Sci. 56, 1748-1765.
4. Bishop, C. H., Etherton, B. J. and Majumdar, S. J. 2001. Adaptive sampling with the ensemble transform Kalman filter. Part I: Theoretical aspects. Mon. Wea. Rev. 129, 420-436.
5. Bowler, N. E. 2006. Comparison of error breeding, singular vectors, random perturbations and ensemble Kalman filter perturbation strategies on a simple model. Tellus 58 A, 538-548.
6. Daley, R. and Barker, E. 2001. NAVDAS: Formulation and diagnostics. Mon. Wea. Rev. 129, 869-883.
7. Fertig, E. J., Hunt, B. R., Ott, E. and Szunyogh, I. 2007. Assimilating non-local observations with a local ensemble Kalman filter. Tellus 59 A, 719-730.
8. Hogan, T. F. and Rosmond, T. E. 1991. The description of the Navy Operational Global Atmospheric Prediction System's spectral forecast model. Mon. Wea. Rev. 119, 1786-1815.
9. Holton, J. R. 2004. An Introduction to Dynamic Meteorology. 4th Edition, Academic Press, San Diego, pp. 511.
10. Houtekamer, P. L., Mitchell, H. L., Pellerin, G., Buehner, M., Charron, M. and co-authors. 2005. Atmospheric data assimilation with an ensemble Kalman filter: Results with real observations. Mon. Wea. Rev. 133, 604-620.
11. Hunt, B. R., Kostelich, E. J. and Szunyogh, I. 2007. Efficient data assimilation for a spatiotemporal chaos: A local ensemble transform Kalman filter. Physica D 230, 112-126.
12. Khare, S. P., Anderson, J. L., Hoar, T. H. and Nychka, D. 2008. An investigation into the application of an ensemble Kalman smoother to high-dimensional geophysical systems. Tellus 60A, 97-112.
13. McLay, J. G., Bishop, C. H. and Reynolds, C. A. 2007. The ensemble transform scheme adapted for the generation of stochastic perturbations. Quart. J. Roy. Meteor. Soc. 133, 1257-1266.
14. McLay, J. G., Bishop, C. H. and Reynolds, C. A. 2008. Evaluation of the ensemble transform analysis perturbation scheme at NRL. Mon. Wea. Rev. 136, 1093-1108.
15. Miyoshi, T. and Sato, Y. 2007. Assimilating satellite radiances with a local ensemble transform kalman filter (LETKF) applied to the JMA global model (GMS). SOLA 2007, Vol. 3, 37-40. Available at http://www.jstage.jst.go.jp/article/sola/3/0/3_37/_article
16. Miyoshi, T. and Yamane, S. 2007. Local Ensemble Transform Kalman Filtering with an AGCM at a T159/L48 Resolution. Mon. Wea. Rev. 135, 3841-3861.
17. Miyoshi, T., Yamane, S. and Enomoto, T. 2007. Localizing the error covariance by physical distances within a local ensemble transform kalman filter (LETKF). SOLA 2007, Vol. 3, 37-40. Available at http://www.jstage.jst.go.jp/article/sola/3/0/3_37/_article
18. Ott, E., Hunt, B. H., Szunyogh, I., Zimin, A. V., Kostelich, E. J. and co-authors 2004. A local ensemble Kalman filter for atmospheric data assimilation. Tellus 56A, 415-428.
19. Patil, D. J., Hunt, B. R., Kalnay, E., Yorke, J. A. and Ott, E. 2001. Local low dimensionality of atmospheric dynamics. Phys. Rev. Lett. 86, 5878-5881.
20. Press, W. H., Teukolsky, S. A., Vetterling, W. T. and Flannery, B. P. 2007. Numerical Recipes: The Art of Scientific Computing, 3rd Edition. Cambridge University Press, Cambridge, pp. 1256.
21. Reynolds, C. A., Teixeira, J. A. and McLay, J. G. 2008. Impact of stochastic convection on the Ensemble Transform. Mon. Wea. Rev. 136, 4517-4526.
22. Szunyogh, I., Kostelich, E. J., Gyarmati, G., Patil, D. J., Hunt, B. R. and co-authors. 2005. Assessing a local ensemble Kalman filter: Perfect model experiments with the National Centers for Environmental Prediction global model. Tellus 57A, 528-545.
23. Szunyogh, I., Kostelic, E. J., Gyarmati, G., Kalnay, E., Hunt, B. R. and co-authors. 2008. A local ensemble transform Kalman filter data assimilation system for the NCEP global model. Tellus 60 A, 113-130.
24. Wang, X. and Bishop, C. H. 2003. A comparison of breeding and ensemble transform kalman filter forecast schemes. J. Atmos. Sci. 60, 1140-1158.
25. Wang, X., Bishop, C. H. and Julier, S. J. 2004. Which is better, an ensemble of positive/negative pairs or a centered spherical simplex ensemble? Mon. Wea. Rev. 132, 1590-1605.
26. Whitaker, J. S., Compo, G. P., Wei, X. and Hamill, T. M. 2004. Reanalysis without radiosondes using ensemble data assimilation. Mon. Wea. Rev. 132, 1190-1200.
27. Whitaker, J. S., Hamill, T. M., Wei, X., Song, Y. and Toth, Z. 2008. Ensemble data assimilation with the NCEP global forecast system. Mon. Wea. Rev. 136, 463-482.