• \
    Start Submission Become a Reviewer

    Reading: Ensemble prediction for nowcasting with a convection-permitting model – II: forecast error s...

    Download

     A- A+
    Alt. Display

    Original Research Papers

    Ensemble prediction for nowcasting with a convection-permitting model – II: forecast error statistics

    Authors:

    R. N. Bannister ,

    National Centre for Earth Observation, Department of Meteorology, University of Reading, Earley Gate, Reading, RG6 6BB, GB
    X close

    S. Migliorini,

    National Centre for Earth Observation, Department of Meteorology, University of Reading, Earley Gate, Reading, RG6 6BB, GB
    X close

    M. A. G. Dixon

    Met Office Joint Centre for Mesoscale Meteorology, Department of Meteorology, University of Reading, Earley Gate, Reading, RG6 6BB, GB
    X close

    Abstract

    A 24-member ensemble of 1-h high-resolution forecasts over the Southern United Kingdom is used to study shortrange forecast error statistics. The initial conditions are found from perturbations from an ensemble transform Kalman filter. Forecasts from this system are assumed to lie within the bounds of forecast error of an operational forecast system. Although noisy, this system is capable of producing physically reasonable statistics which are analysed and compared to statistics implied from a variational assimilation system. The variances for temperature errors for instance show structures that reflect convective activity. Some variables, notably potential temperature and specific humidity perturbations, have autocorrelation functions that deviate from 3-D isotropy at the convective-scale (horizontal scales less than 10 km). Other variables, notably the velocity potential for horizontal divergence perturbations, maintain 3-D isotropy at all scales. Geostrophic and hydrostatic balances are studied by examining correlations between terms in the divergence and vertical momentum equations respectively. Both balances are found to decay as the horizontal scale decreases. It is estimated that geostrophic balance becomes less important at scales smaller than 75 km, and hydrostatic balance becomes less important at scales smaller than 35 km, although more work is required to validate these findings. The implications of these results for high-resolution data assimilation are discussed.

    How to Cite: Bannister, R.N., Migliorini, S. and Dixon, M.A.G., 2011. Ensemble prediction for nowcasting with a convection-permitting model – II: forecast error statistics. Tellus A: Dynamic Meteorology and Oceanography, 63(3), pp.497–512. DOI: http://doi.org/10.1111/j.1600-0870.2010.00500.x
      Published on 01 Jan 2011
    Peer Reviewed
     CC BY 4.0
     Accepted on 29 Dec 2010            Submitted on 14 Apr 2010

    References

    1. Bannister , R. N . 2008a . A review of forecast error covariance statistics in atmospheric variational data assimilation — I: characteristics and measurements of forecast error covariances . Q. J. R. Meteorol. Soc . 134 , 1951 – 1970 .  

    2. Bannister , R. N . 2008b . A review of forecast error covariance statistics in atmospheric variational data assimilation — H: modelling the forecast error covariance statistics . Q. J. R. MeteoroL Soc . 134 , 1971 – 1996 .  

    3. Bannister , R. N. , Katz , D. , Cullen , M. J. P. , Lawless , A. S. and Nichols , N. K . 2008 . Modelling of forecast errors in geophysical fluid flows . Int. J. Numer Meth. Fluids 56 , 1147 – 1153 .  

    4. Bartell° , P. and Mitchell , H. L . 1992 . A continuous three-dimensional model of short-range forecast error covariances . Tellus 44A , 217 – 235 .  

    5. Berre , L . 2000 . Estimation of synoptic and mesoscale forecast error covariances in a limited area model. Mon . Wea. Re v . 128 , 644 – 667 .  

    6. Berre , L ., tefänescu , S. E. and Pereira , M. B. 2006. The representation of the analysis effect in three error simulation techniques. Tellus 58A , 196 - 209 .  

    7. Bishop , C. H. , Etherton , B. J. and Majumdar , S. J . 2001 . Adaptive sam-pling with the ensemble transform Kalman filter - Part I: theoretical aspects. Mon . Wea. Re v . 129 , 420 – 436 .  

    8. Bowler , N. E. , Arribas , A. , Mylne , K. R. , Robertson , K. B. and Beare , S. E . 2008 . The MOGREPS short-range ensemble prediction system . Q. J. R. MeteoroL Soc . 134 , 703 – 722 .  

    9. Buehner , M . 2005 . Ensemble derived stationary and flow dependent background error covariances: evaluation in a quasi-operational NWP setting . Q. J. R. MeteoroL Soc . 131 , 1013 – 1043 .  

    10. Dance , S. L . 2004 . Issues in high resolution limited area data assimilation for quantitative precipitation forecasting . Physica D 196 , 1 – 27 .  

    11. Davies , T. , Cullen , M . J. R, Malcolm, A. J., Mawson, M. H., Staniforth, A. and co-authors. 2005. A new dynamical core for the Met Office’s global and regional modelling of the atmosphere . Q. J. R. MeteoroL Soc . 131 , 1759– 1782 .  

    12. Dixon , M. A. G. , Li , Z. , Lean , H. , Roberts , N. and Ballard , S . 2009 . Impact of data assimilation on forecasting convection over the United Kingdom using a high-resolution version of the Met Office unified model. Mon . Wea. Re v . 137 , 1562 – 1584 .  

    13. Ehrendorfer , M . 2007 . A review of issues in ensemble-based Kalman filtering . MeteoroL Z . 16 , 795 – 818 .  

    14. Fisher , M . 2003 . Background error covariance modelling. In: ECMWF Seminar on Recent Developments in Data Assimilation for Atmo-sphere and Ocean, 8-12 September 2003, ECMWF, Reading UK , 45 - 64 .  

    15. Houtelcamer , P. L. and Mitchell , H. L . 2001 . A sequential ensemble Kalman filter for atmospheric data assimiltion. Mon . Wea. Re v . 129 , 123 – 137 .  

    16. Ingleby , N. B . 2001 . The statistical structure of forecast errors and its representation in the Met Office global 3-dimensional variational data assimilation scheme . Q. J. R.Meteor Soc . 127 , 209 – 231 .  

    17. Kalnay , E . 2002 . Atmospheric Modelling, Data Assimilation and Pre-dictability . Cambridge University Press , Cambridge , UK . 364 pp .  

    18. Kepert , J. D. 2009. Covariance localisation and balance in an ensemble Kalman filter. Q. J. R. MeteoroL Soc . 135 , 1157-117 6 .  

    19. Lean , H. W. , Clark , P. A. , Dixon , M. , Roberts , N. M. , Fitch , A. and co-authors . 2008. Characteristics of high-resolution versions of the Met Office unified model for forecasting convection over the United Kingdom. Mon. Wea. Rev . 136 , 3408-342 4 .  

    20. Lorene , A. C . 2003a . Modelling of error covariances by four-dimensional variational assimilation . Q. J. R. MeteoroL Soc . 129 , 3167 – 3182 .  

    21. Lorene , A. C . 2003b . The potential of the ensemble Kalman filter for NWP: a comparison with 4d-Var . Q. J. R. Meteorol. Soc . 129 , 3183 – 3203 .  

    22. Migliorini , S. , Dixon , M. A. G. , Bannister , R. N. and Ballard , S. P . 2011 . Ensemble prediction for nowcasting with a convection-permitting model - Part I: description of the system and the impact of radar-derived surface precipitation rates. Tellus 63A , https://doi.org/10.1111/j.1600-0870.2010.00503.x .  

    23. Parrish , D. F. and Derber , J. C. 1992 . The National Meteorological Center’s spectral statistical-interpolation analysis system . Mon. Wea. Rev . 120 , 1747– 1793 .  

    24. Salby , M . 1996 . Atmospheric Physics . Academic Press, San Diego, Cal-ifornia . 627 pp . Cambridge University Press , Cambridge, UK. 364  

    25. Vetra-Carvalho , S. , Dixon , M. A. G. , Migliorini , S. , Nichols , N. K. and Ballard , S. P . 2010 . Breakdown of hydrostatic balance at convective scales in the forecast errors in the Met Office Unified Model. Q. J. R. MeteoroL Soc . submitted.  

    26. Zhang , F . 2005 . Dynamics and structure of mesoscale error covariance of a winter cyclone estimated through short-range ensemble forecasts. Mon . Wea. Re v . 133 , 2876 – 2893 .  

    Jump to Discussions Related content
    comments powered by Disqus