Skip to main content
Have a personal or library account? Click to login
Comparison of error breeding, singular vectors, random perturbations and ensemble Kalman filter perturbation strategies on a simple model Cover

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

By:   
Open Access
|Jan 2006

References

  1. Anderson , J. L. 1996 . Selection of initial conditions for ensemble forecasts in a simple perfect model framework. J. Atmos. Sc i . 53 , 22 36 .
  2. Anderson , J. L. 2001 . An ensemble adjustment kalman filter for data assimilation. Mon. Wea. Re v . 129 , 2884 903 .
  3. Barkmeijer , J. , Buizza , R. and Palmer , T. N. 1999 . 3D-Var Hessian singu-lar 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 sam-pling with the ensemble transform kalman filter. Part 1: theoretical aspects Mon. Wea. Re v . 129 , 420 436 .
  5. Bozic , S. M. 1979 . Digital and Kalman Filtering . Edward Arnold (Pub-lishers) Ltd ., London .
  6. Buizza , R. , Tribbia , J., Molteni , E 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 back-ground error covariance matrix for a quasi-geostrophic model. In: Symposium on Observations, Data Assimilation, and Probabilistic Prediction, 13-17 January 2002, Orlando, Florida ANIS, Boston, MA , 154 157 .
  9. Du , J. , Mullen , S. L. and Sanders , E 1997 . Short-range ensemble fore-casting of quantitative precipitation. Mon. Wea. Re v . 125 , 2427 2459 .
  10. Evensen , G. 1994 . Sequential data assimilation with a nonlinear quasi-geostrophic model using monte-carlo methods to forecast error statis-tics . J. Geophys. Res.-Oceans 99 ( C5 ), 10 143 - 10 162 .
  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 func-tions 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 proba-bilistic forecasts from bred, singular-vector, and perturbed observation ensembles. Mon. Wea. Re v . 128 , 1835 1851 .
  15. Houtekamer , P. L. and Derome , J. 1995 . Methods for ensemble predic-tion. Mon. Wea. Re v . 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. H, ECMWF , pp. 121 146 .
  17. Houtekamer , P. L. and Mitchell , H. L. 1998 . Data assimilation using an ensemble Kalman filter technique. Mon. Wea. Re v . 126 , 796 811 .
  18. Kalman , R. E. 1960 . A new approach to linear filtering and prediction problems. Trans. AMSE - .1. Basic Eng . 82 (D) , 35 45 .
  19. Lorenz , E. N. 1965 . A study of the predictability of a 28-variable atmo-spheric model . Tellus 17 , 321 333 .
  20. Lorenz , E. N. 1995 . Predictability: a problem partly solved. In: In Pro-ceedings of the seminar on predictability . Volume I, ECMWF, Read-ing, 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. Sc i . 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. Re v . 130 , 2791 2808 .
  23. Molteni , E , 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 NNIC: 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. Re v . 125 , 3297 3319 .
  28. Wang , X. and Bishop , C. H. 2003 . A comparison of breeding and en-semble transform kalman filter ensemble forecast schemes. J. Atmos. Sc i . 60 , 1140 1158 .
  29. Wang , X. , Bishop , C. H. and killer , S. J . 2004 . Which is better, an ensemble of positive-negative pairs or a centered spherical simplex ensemble? Mon. Wea. Re v . 132 , 1590 605 .
  30. Wei , M. and Toth , Z. 2003 . A new measure of ensemble performance: perturbation versus error correlation analysis (PECA) Mon. Wea. Re v . 131 , 1549 1565 .
  31. Whitaker , J. S. and Hamill , T. M. 2002 . Ensemble data assimilation without perturbed observations. Mon. Wea. Re v . 130 , 1913 1924 .
Language: English
Page range: 538 - 548
Submitted on: Aug 15, 2005
Accepted on: Jun 2, 2006
Published on: Jan 1, 2006
Published by: Stockholm University Press
In partnership with: Paradigm Publishing Services

© 2006 Neill E. Bowler, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.