Comparison of error breeding, singular vectors, random perturbations and ensemble Kalman filter perturbation strategies on a simple model

Tellus, Series A: Dynamic Meteorology and Oceanography

Volumn 58, Issue 5, 2006, Pages 538-548

  Bowler, Neill E ^a  

^a MET OFFICE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

COVARIANCE ANALYSIS; ENSEMBLE FORECASTING; ERROR ANALYSIS; KALMAN FILTER; NUMERICAL MODEL; PERTURBATION; PREDICTION; WEATHER FORECASTING;

EID: 33748658227     PISSN: 02806495     EISSN: 16000870     Source Type: Journal    
DOI: 10.1111/j.1600-0870.2006.00197.x     Document Type: Article

References (31)

1. Anderson, J. L. 1996. Selection of initial conditions for ensemble forecasts in a simple perfect model framework. J. Atmos. Sci. 53, 22-36.
2. Anderson, J. L. 2001. An ensemble adjustment kalman filter for data assimilation. Mon. Wea. Rev. 129, 2884-903.
3. Barkmeijer, J., Buizza, R. and Palmer, T. N. 1999. 3D-Var Hessian singular vectors and their potential use in the ECMWF ensemble prediction system. Q. J. R. Meteorol. Soc. 125, 2333-2351.
4. Bishop, C. H., Etherton, B. J. and Majumdar, S. J. 2001 Adaptive sampling with the ensemble transform kalman filter. Part 1: Theoretical aspects Mon. Wea. Rev. 129, 420-436.
5. Bozic, S. M. 1979. Digital and Kalman Filtering. Edward Arnold (Publishers) Ltd., London.
6. Buizza, R., Tribbia, J., Molteni, F. and Palmer, T. 1993. Computation of optimal unstable structures for a numerical weather prediction model. Tellus 45A, 388-407.
7. Buizza, R., Houtekamer, P. L., Toth, Z., Pellerin, G., Wei, M. and co-authors. 2005. Assessment of the status of global ensemble prediction. Mon. Wea. Rev. 133, 1076-1097.
8. Corazza, M., Kalnay, E., Patil, D. J., Ott, E., Szunyogh, J. and co-authors 2002. Use of the breeding technique in the estimation of the background error covariance matrix for a quasi-geostrophic model. In: Symposium on Observations, Data Assimilation, and Probabilistic Prediction, 13-17 January 2002, Orlando, Florida AMS, Boston, MA, 154-157.
9. Du, J., Mullen, S. L. and Sanders, F. 1997. Short-range ensemble forecasting of quantitative precipitation. Mon. Wea. Rev. 125, 2427-2459.
10. Evensen, G. 1994. Sequential data assimilation with a nonlinear quasi-geostrophic model using monte-carlo methods to forecast error statistics. J. Geophys. Res.-Oceans 99 (C5), 10143-10162.
11. Evensen, G. 2004. Sampling strategies and square root analysis schemes for the EnKF. http://www.nersc.no/~geir/EnKF/Publications/eve04a.pdf
12. Fisher, M. and Andersson, E. 2001. Developments in 4D-Var and Kalman filtering. ECMWF technical memo no. 347 ECMWF.
13. Gaspari, G. and Cohn, S. E. 1999 Construction of correlation functions in two and three dimensions. Q. J. R. Meteorol. Soc. 125, 723-757.
14. Hamill, T. M., Snyder, C. and Morss, R. E. 2000 A comparison of probabilistic forecasts from bred, singular-vector, and perturbed observation ensembles. Mon. Wea. Rev. 128, 1835-1851.
15. Houtekamer, P. L. and Derome, J. 1995. Methods for ensemble prediction. Mon. Wea. Rev. 123, 2181-2196.
16. Houtekamer, P. L., Lefaivre, L. and Derome, J. 1995 The RPN ensemble prediction system. In: Proceedings of the seminar on predictability (Reading, Berkshire, UK, 1995), vol. II, ECMWF, pp. 121-146.
17. Houtekamer, P. L. and Mitchell, H. L. 1998. Data assimilation using an ensemble Kalman filter technique. Mon. Wea. Rev. 126, 796-811.
18. Kalman, R. E. 1960. A new approach to linear filtering and prediction problems. Trans. AMSE - J. Basic Eng. 82 (D), 35-45.
19. Lorenz, E. N. 1965. A study of the predictability of a 28-variable atmospheric model. Tellus 17, 321-333.
20. Lorenz, E. N. 1995. Predictability: A problem partly solved. In: In Proceedings of the seminar on predictability. Volume I, ECMWF, Reading, Berkshire, UK., pp. 1-18.
21. Lorenz, E. N. and Emanuel, K. A. 1998. Optimal sites for supplementary weather observations: Simulation with a small model. J. Atmos. Sci. 55, 399-414.
22. Mitchell, H. L., Houtekamer, P. L. and Pellerin, G. 2002. Ensemble size, balance, and model-error representation in an ensemble Kalman filter. Mon. Wea. Rev. 130, 2791-2808.
23. Molteni, F., Buizza, R., Palmer, T. N. and Petroliagis, T. 1996. The ECMWF ensemble prediction system: Methodology and validation. Q. J. R. Meteorol. Soc. 122, 73-119.
24. Ott, E., Hunt, B. R., Szunyogh, I., Zimin, A. V., Kostelich, E., and co-authors. 2004. A local ensemble kalman filter for atmospheric data assimilation. Tellus 56A, 415-428.
25. Szunyogh, I., Kostelich, E., Gyarmati, G., Patil, D., Hunt, B., 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.
26. Toth, Z. and Kalnay, E. 1993. Ensemble forecasting at NMC: The generation of perturbations. Bull. Am. Meteorol. Soc. 74, 2317-2330.
27. Toth, Z. and Kalnay, E. 1997. Ensemble forecasting at NCEP and the breeding method. Mon. Wea. Rev. 125, 3297-3319.
28. Wang, X. and Bishop, C. H. 2003. A comparison of breeding and ensemble transform kalman filter ensemble forecast schemes. J. Atmos. Sci. 60, 1140-1158.
29. 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, 1590-605.
30. Wei, M. and Toth, Z. 2003. A new measure of ensemble performance: perturbation versus error correlation analysis (PECA) Mon. Wea. Rev. 131, 1549-1565.
31. Whitaker, J. S. and Hamill, T. M. 2002. Ensemble data assimilation without perturbed observations. Mon. Wea. Rev. 130, 1913-1924.