Remember Me
Or use your Academic/Social account:


Or use your Academic/Social account:


You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.


Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Wei, Mozheng; Toth, Zoltan; Wobus, Richard; Zhu, Yuejian (2008)
Publisher: Co-Action Publishing
Journal: Tellus A
Languages: English
Types: Article
Since modern data assimilation (DA) involves the repetitive use of dynamical forecasts, errors in analyses share characteristics of those in short-range forecasts. Initial conditions for an ensemble prediction/forecast system (EPS or EFS) are expected to sample uncertainty in the analysis field. Ensemble forecasts with such initial conditions can therefore (a) be fed back to DA to reduce analysis uncertainty, as well as (b) sample forecast uncertainty related to initial conditions. Optimum performance of both DA and EFS requires a careful choice of initial ensemble perturbations. DA can be improved with an EFS that represents the dynamically conditioned part of forecast error covariance as accurately as possible, while an EFS can be improved by initial perturbations reflecting analysis error variance. Initial perturbation generation schemes that dynamically cycle ensemble perturbations reminiscent to how forecast errors are cycled in DA schemes may offer consistency between DA and EFS, and good performance for both. In this paper, we introduce an EFS based on the initial perturbations that are generated by the Ensemble Transform (ET) and ET with rescaling (ETR) methods to achieve this goal. Both ET and ETR are generalizations of the breeding method (BM). The results from ensemble systems based on BM, ET, ETR and the Ensemble Transform Kalman Filter (ETKF) method are experimentally compared in the context of ensemble forecast performance. Initial perturbations are centred around a 3D-VAR analysis, with a variance equal to that of estimated analysis errors. Of the four methods, the ETR method performed best in most probabilistic scores and in terms of the forecast error explained by the perturbations. All methods display very high time consistency between the analysis and forecast perturbations. It is expected that DA performance can be improved by the use of forecast error covariance from a dynamically cycled ensemble either with a variational DA approach (coupled with an ETR generation scheme), or with an ETKF-type DA scheme.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Anderson, J. L. 2001. An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev., 129, 2884-2903.
    • Barkmeijer, J., Buizza, R. and Palmer, T. N. 1999. 3D-Var Hessian singular vectors and their potential use in the ECMWF ensemble prediction system. J. Roy. Meteor. Soc., 125, 2333-2351.
    • Bishop, C. H. and Toth, Z. 1999. Ensemble transformation and adaptive observations. J. Atmos. Sci., 56, 1748-1765.
    • Bishop, C. H., Etherton, B. J. and Majumdar, S. 2001. Adaptive sampling with the ensemble transform Kalman filter. Part I: theoretical aspects. Mon. Wea. Rev., 129, 420-436.
    • Bowler, N. E. 2006. Comparison of error breeding, singular vectors, random perturbations and ensemble Kalman filter perturbation strategies on a simple model. Tellus, 58A, 538-548.
    • Buizza, R. and Palmer, T. N. 1995. The singular-vector structure of the atmospheric global circulation. J. Atmos. Sci. 52, 1434-1456.
    • Buizza, R., Houtekamer, 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. Wea. Rev. 133, 1076-1097.
    • Errico, R., Yang, R., Masutani, M. and Woollen, J. 2007. The use of an OSSE to estimate characteristics of analysis error. Meteorologische Zeitschrift, in press.
    • Ferro, C. A. T. 2007. Comparing probabilistic forecasting systems with the Brier Score. Wea. Forecast., in press.
    • Fisher, M. and Andersson, E. 2001. Development in 4D-Var and Kalman Filtering. ECMWF Technical Memorandum, No. 347. 36 pp.
    • 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.
    • Houtekamer, P. L., Lefaivrem, L., Derome, J., Ritchie, H. and Mitchell, H. L. 1996. A sytem simulation approach to ensemble prediction. Mon. Wea. Rev., 124, 1225-1242.
    • Julier, S. J. and Uhlmann, J. K. 2002. Reduced sigma point filters for propagation of means and covariances through nonlinear transformations. In: Proc. IEEE American Control Conf., Anchorage, AK, IEEE, 887-892.
    • Lorenc, A. C. 2003. The potential of ensemble Kalman filter for NWP-a comparison with 4D-Var. Quart. J. Roy. Met. Soc. 129, 3183-3203.
    • 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? Quart. J. Roy. Met. Soc. 127, 2803-2820.
    • Majumdar, S. J., Bishop, C. H. and Etherton, B. J. 2002. Adaptive sampling with Ensemble Transform Kalman Filter. Part II: field program implementation. Mon. Wea. Rev., 130, 1356-1369.
    • Mason, I. B. 2003. Binary Events. In: Forecast Verifiction: A Practitioner's Guid in Atmospheric Science (eds Ian T. Jolliffe and David B. Stephenson). John Wiley & Sons Ltd., England, 37-76.
    • Molteni, F., Buizza, R. Palmer, T. and Petroliagis, T. 1996. The ECMWF ensemble prediction system: methodology and validation. Quart. J. Roy. Meteor. Soc., 122, 73-119.
    • Ott, E., Hunt, B. R., Szunyogh, I., Zimin, A. V., Kostelich, E. J. and coauthors. 2004. A Local ensemble Kalman filter for atmospheric data assimilation. Tellus, 56A, 415-428.
    • Parrish, D. F. and Derber, J. 1992. The National Meteorological Center's spectral statistical-interpolation analysis system. Mon. Wea. Rev., 120, 1747-1763.
    • 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 ).
    • Richardson, D. S. 2000. Skill and relative economic value of the ECMWF ensemble prediction system. Quart. J. Roy. Meteor. Soc. 126, 649-667.
    • 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.
    • Tippett, M. K., Anderson, J. L., Bishop, C. H., Hamill, T. and Whitaker, J. S. 2003. Ensemble square root filters. Mon. Wea. Rev., 131, 1485- 1490.
    • Toth, Z. and Kalnay, E. 1993. Ensemble forecasting at NMC: the generation of perturbations. Bull. Amer. Meteror. Soc., 174, 2317-2330.
    • Toth, Z. and Kalnay, E. 1997. Ensemble forecasting at NCEP and the breeding method. Mon. Wea. Rev., 125, 3297-3319.
    • Toth, Z., Talagrand, O., Candille, G. and Zhu, Y. 2003. Probability and ensemble forecasts. In: Forecast Verifiction: A Practitioner's Guid in Atmospheric Science (eds Ian T. Jolliffe and David B. Stephenson). John Wiley & Sons Ltd., England, 137-163.
    • Toth, Z. and Pena, M. 2006. Data assimilation and numerical forecasting with imperfect models: the Mapping Paradigm. Physica D, 13 p., doi:10.1016/j.physd.2006.08.016.
    • 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.
    • 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.
    • Wei, M. 2000. Quantifying local instability and predictability of chaotic dynamical systems by means of local metric entropy. Int. J. Bifurcation Chaos, 10, 135-154.
    • Wei, M. and Toth, Z. 2003. A new measure of ensemble performance: perturbations versus Error Correlation Analysis (PECA). Mon. Wea. Rev., 131, 1549-1565.
    • Wei, M. and Frederiksen, J. S. 2004. Error growth and dynamical vectors during southern hemisphere blocking. Nonl. Proc. Geoph., 11, 99- 118.
    • Wei, M., Toth, Z., Wobus, R., Zhu, Y. and Bishop, C. H. 2005a. Initial perturbations for NCEP Ensemble Forecast System. In: Thorpex Symposium Proceedings for the First THORPEX Internal Science Symposium 6-10 December 2004, Montreal, Canada. The Symposium Proceedings in a WMO Publication 2005, WMO TD No.1237, WWRP THORPEX No. 6, 2005. p227-230.
    • Wei, M., Toth, Z., Wobus, R., Zhu, Y., Hou, D. and coauthors. 2005b. NCEP Global Ensemble: recent developments and plans. In: 2nd SRNWP Workshop on Short Range Ensemble, Bologna, Italy, 7-8 April, 2005. Available at http://smwp.cscs.ch/ Lead Centres/2005Bologna/Agenda.htm
    • Wei, M., Toth, Z., Wobus, R., Zhu, Y., Bishop, C. H. and Wang, X. 2006a. Ensemble Transform Kalman Filter-based ensemble perturbations in an operational global prediction system at NCEP. Tellus, 58A, 28-44.
    • Wei, M., Toth, Z., Wobus, R., Zhu, Y. and Bishop, C. H. 2006b. The Use of Ensemble Transform Technique for Generating Initial Ensemble Perturbations. NOAA Thorpex PI Workshop at NCEP, Camp Springs, Maryland. Jan. 17-19, 2006. Available at http://www. emc.ncep.noaa.gov/gmb/ens/THORPEX/PI-shop-2006.html Wei, M., Toth, Z., Wobus, R. and Zhu, Y. 2006c. Initial Perturbations based on the Ensemble Transform (ET) Technique in the NCEP global Ensemble Forecast System. US Department of Commerce, NOAA/NCEP Office Note No. 453, 33 pp.
    • Whitaker, J. S. and Hamill, T. M. 2002. Ensemble data assimilation without perturbed observations. Mon. Wea. Rev., 130, 1913- 1924.
    • Wilks, D. S. 1995. Statistical Methods in the Atmospheric Sciences. Academic Press, London, 464 p.
    • Wilson, L. J. 2000. Comments on “Probabilistic Predictions of Precipitation Using the ECMWF ensemble prediction system”. Wea. Forecast., 15, 361-369.
    • Zhu, Y., Toth, T., Wobus, R., Richardson, D. and Mylne, K. 2002. The economic value of ensemble-based weather forecasts. Bull.Amer. Meteor. Soc. 83, 73-83.
    • Zupanski, M. 2005. Maximum likelihood ensemble filter: theoretical aspects. Mon. Wea. Rev., 133, 1710-1726.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article

Collected from