• \
    Start Submission Become a Reviewer

    Reading: Ensemble Transform Kalman Filter-based ensemble perturbations in an operational global predi...

    Download

     A- A+
    Alt. Display

    Original Research Papers

    Ensemble Transform Kalman Filter-based ensemble perturbations in an operational global prediction system at NCEP

    Authors:

    Mozheng Wei ,

    SAIC at NOAA/NWS/NCEP, Camp Springs, MD, US
    X close

    Zoltan Toth,

    NOAA/NWS/NCEP, Camp Springs, MD, US
    X close

    Richard Wobus,

    SAIC at NOAA/NWS/NCEP, Camp Springs, MD, US
    X close

    Yuejian Zhu,

    NOAA/NWS/NCEP, Camp Springs, MD, US
    X close

    Craig H. Bishop,

    Naval Research Laboratory, Monterey, CA, US
    X close

    Xuguang Wang

    NOAA-CIRES/CDC, Boulder, CO, US
    X close

    Abstract

    The initial perturbations used for the operational global ensemble prediction system of the National Centers for Environmental Prediction are generated through the breeding method with a regional rescaling mechanism. Limitations of the system include the use of a climatologically fixed estimate of the analysis error variance and the lack of an orthogonalization in the breeding procedure. The Ensemble Transform Kalman Filter (ETKF) method is a natural extension of the concept of breeding and, as shown byWang and Bishop, can be used to generate ensemble perturbations that can potentially ameliorate these shortcomings. In the present paper, a spherical simplex 10-member ETKF ensemble, using the actual distribution and error characteristics of real-time observations and an innovation-based inflation, is tested and compared with a 5-pair breeding ensemble in an operational environment.

    The experimental results indicate only minor differences between the performances of the operational breeding and the experimental ETKF ensemble and only minor differences to Wang and Bishop’s earlier comparison studies. As for the ETKF method, the initial perturbation variance is found to respond to temporal changes in the observational network in the North Pacific. In other regions, however, 10 ETKF perturbations do not appear to be enough to distinguish spatial variations in observational network density. As expected, the whitening effect of the ETKF together with the use of the simplex algorithm that centres a set of quasi-orthogonal perturbations around the best analysis field leads to a significantly higher number of degrees of freedom as compared to the use of paired initial perturbations in operations. As a new result, the perturbations generated through the simplex method are also shown to exhibit a very high degree of consistency between initial analysis and short-range forecast perturbations, a feature that can be important in practical applications. Potential additional benefits of the ETKF and Ensemble Transform methods when using more ensemble members and a more appropriate inflation scheme will be explored in follow-up studies.

    How to Cite: Wei, M., Toth, Z., Wobus, R., Zhu, Y., Bishop, C.H. and Wang, X., 2006. Ensemble Transform Kalman Filter-based ensemble perturbations in an operational global prediction system at NCEP. Tellus A: Dynamic Meteorology and Oceanography, 58(1), pp.28–44. DOI: http://doi.org/10.1111/j.1600-0870.2006.00159.x
      Published on 01 Jan 2006
    Peer Reviewed
     CC BY 4.0
     Accepted on 2 Sep 2005            Submitted on 14 Jan 2005

    References

    1. Anderson , J. L. 2001 . An ensemble adjustment Kalman filter for data assimilation . Mon. Weather Rev . 129 , 2884 – 2903 .  

    2. 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 . J. R. Meteorol. Soc . 125 , 2333 – 2351 .  

    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 . 2001 . Adaptive sampling with the ensemble transform Kalman filter. Part I: theoretical aspects . Mon. Weather Rev . 129 , 420 – 436 .  

    5. Bretherton , C. S. , Widmann , M. , Dymnilcov , V. P. , Wallace , J. M. and Blade , I. 1999 . The effective number of spatial degrees of freedom of a time-varying field . J. Clim . 12 , 1990 – 1999 .  

    6. Buizza , R. and Palmer , T. N. 1995 . The singular-vector structure of the atmospheric global circulation . J. Atmos. Sci . 52 , 1434 – 1456 .  

    7. Buizza , R. , Houtelcamer , P. L. , Toth , Z. , Pellerin , P. , Wei , M. and Zhu , Y. 2005 . A comparison of the ECMWF, MSC and NCEP global ensemble prediction systems . Mon. Weather Rev . 133 , 1076 – 1097 .  

    8. Burgers , G. , van Leeuwen , P. J. and Evensen , G. 1998 . On the analysis scheme in the ensemble Kalman filter . Mon. Weather Rev . 123 , 1719 – 1724 .  

    9. Etherton , B. J. and Bishop , C. H. 2004 . Resilience of hybrid ensemble/3D-var analysis schemes to model error and ensemble co-variance error . Mon Weather Rev . 130 , 1065 – 1080 .  

    10. Houtelcamer , P. L. and Mitchell , H. L. 1998 . Data assimilation using an ensemble Kalman filter technique . Mon. Weather Rev . 126 , 796 – 811 .  

    11. Houtelcamer , P. L. , Lefaivrem , L. , Derome , J. , Ritchie , H. and Mitchell , H. L. 1996 . A system simulation approach to ensemble prediction . Mon. Weather Rev . 124 , 1225 – 1242 .  

    12. Julier , S. J. and Uhlmann , J. K. 2002 . Reduced sigma point filters for propagation of means and covariances through nonlinear transformations . Proc. IEEE American Control Conf , Anchorage, AK , IEEE , 887 – 892 .  

    13. Lorene , A. C. 2003 . The potential of the ensemble Kalman filter for NWP - a comparison with 4D-var . J. R. Meteorol. Soc . 129 , 3183 – 3203 .  

    14. Majumdar , S. J. , Bishop , C. H. , Szunyogh , I. and Toth , Z . 2001 . Can an Ensemble Transform Kalman Filter predict the reduction in forecast error variance produced by targeted observations? . Q. J. R. Meteorol. Soc . 127 , 2803 – 2820 .  

    15. Majumdar , S. J. , Bishop , C. H. and Etherton , B. J. 2002 . Adaptive sam-pling with Ensemble Transform Kalman Filter. Part II: field program implementation . Mon. Weather Rev . 130 , 1356 – 1369 .  

    16. Molteni , E , Buizza , R. Palmer , T. and Petroliagis , T. 1996 . The ECMWF ensemble prediction system: methodology and validation. Q. J. R. Meteorol. Soc . 122 , 73 – 119 .  

    17. Oczkowslci , M. , Szunyogh , I. and Patil , D. J. 2005 . Mechanism for the development of locally low-dimensional atmospheric dynamics . J. Atmos. Sci . 62 , 1135 – 1156 .  

    18. Ott , E. , Hunt , B. R. , Szunyogh , I. , Zimin , A. V , Kostelich , E. J. , and co-authors . 2004 . A local ensemble Kalman filter or atmospheric data assimilation. Tellus 56A , 415 – 428 .  

    19. Palmer , T. N. , Gelaro , R. , Barlcmeijer , J. and Buizza , R. 1998 . Singular vectors, metrics, and adaptive observations . J. Atmos. Sci . 55 , 633 – 653 .  

    20. Parrish , D. E and Derber , J. 1992 . The National Meteorological Center’s spectral statistical-interpolation analysis system . Mon. Weather Rev . 120 , 1747 – 1763 .  

    21. 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 .  

    22. Purser , R. J. 1996 . Arrangement of ensemble in a simplex to produce given first and second-moments, NCEP Internal Report (available from the author at Jim.Purser@noaa.gov) .  

    23. Rabier , F. , Klinker , E. , Courtier , P. and Hollingsworth , A. 1996 . Sensitivity of forecast errors to initial conditions . Q. J. R. Meteorol. Soc . 122 , 121 – 150 .  

    24. 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 NCEP global model. Tellus 57A , 528 – 545 .  

    25. Tippett , M. K. , Anderson , J. L. , Bishop , C. H. , Hamill , T. and Whitaker , J. S. 2003 . Ensemble square root filters . Mon. Weather Rev . 131 , 1485 – 1490 .  

    26. Toth , Z. and Kalnay , E. 1993 . Ensemble forecasting at NMC: the generation of perturbations . Bull. Am. Meteorol. Soc . 174 , 2317 – 2330 .  

    27. Toth , Z. and Kalnay , E. 1997 . Ensemble forecasting at NCEP and the breeding method . Mon. Weather Rev . 125 , 3297 – 3319 .  

    28. Toth , Z. , Kalnay , E. , Tracton , S. M. , Wobus , R. and Irwin , J. 1997 . A synoptic evaluation of the NCEP ensemble . Weather Forecast . 12 , 140 – 153 .  

    29. Toth , Z. , Talagrand , O. , Candille , G. and Zhu , Y . 2003 . Probability and ensemble forecasts. In: Forecast Verification: A Practitioner’s Guide in Atmospheric Science (eds Ian T. Jolliffe and David B. Stephenson ). John Wiley & Sons Ltd., England , 137 – 163 .  

    30. Wang , X. and Bishop , C. H. 2003 . A comparison of breeding and en-semble transform Kalman filter ensemble forecast schemes . J. Atmos. Sci . 60 , 1140 – 1158 .  

    31. 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. Weather Rev . 132 , 1590 – 1605 .  

    32. Wei , M. 2000 . Quantifying local instability and predictability of chaotic dynamical systems by means of local, metric entropy . Int. J. Bifurca-tion Chaos 10 , 135 – 154 .  

    33. Wei , M. and Frederiksen , J. S. 2004 . Error growth and dynamical vec-tors during southern hemisphere blocking . NonL Proc. Geophys . 11 , 99 – 118 .  

    34. Wei , M. and Toth , Z. 2003 . A new measure of ensemble performance: perturbations versus error correlation analysis (PECA) . Mon. Weather Rev . 131 , 1549 – 1565 .  

    35. Whitaker , J. S. and Hamill , T. M. 2002 . Ensemble data assimilation without perturbed observations . Mon. Weather Rev . 130 , 1913 – 1924 .  

    Jump to Discussions Related content
    comments powered by Disqus