Editorial Type:
Article
Article Type:
Research Article

Multiresolution Ensemble Forecasts of an Observed Tornadic Thunderstorm System. Part II: Storm-Scale Experiments

Fanyou Kong Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, Oklahoma

Search for other papers by Fanyou Kong in
Current site
Google Scholar
PubMed Close
,
Kelvin K. Droegemeier Center for Analysis and Prediction of Storms, and School of Meteorology, University of Oklahoma, Norman, Oklahoma

Search for other papers by Kelvin K. Droegemeier in
Current site
Google Scholar
PubMed Close
, and
Nicki L. Hickmon NOAA/Warning Decision Training Branch, Norman, Oklahoma

Search for other papers by Nicki L. Hickmon in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Mar 2007
DOI:
https://doi.org/10.1175/MWR3323.1
Page(s):
759–782
Restricted access

Abstract

In Part I, the authors used a full physics, nonhydrostatic numerical model with horizontal grid spacing of 24 km and nested grids of 6- and 3-km spacing to generate the ensemble forecasts of an observed tornadic thunderstorm complex. The principal goal was to quantify the value added by fine grid spacing, as well as the assimilation of Doppler radar data, in both probabilistic and deterministic frameworks. The present paper focuses exclusively on 3-km horizontal grid spacing ensembles and the associated impacts on the forecast quality of temporal forecast sequencing, the construction of initial perturbations, and data assimilation. As in Part I, the authors employ a modified form of the scaled lagged average forecasting technique and use Stage IV accumulated precipitation estimates for verification. The ensemble mean and spread of accumulated precipitation are found to be similar in structure, mimicking their behavior in global models. Both the assimilation of Doppler radar data and the use of shorter (1–2 versus 3–5 h) forecast lead times improve ensemble precipitation forecasts. However, even at longer lead times and in certain situations without assimilated radar data, the ensembles are able to capture storm-scale features when the associated control forecast in a deterministic framework fails to do so. This indicates the potential value added by ensembles although this single case is not sufficient for drawing general conclusions. The creation of initial perturbations using forecasts of the same grid spacing shows no significant improvement over simply extracting perturbations from forecasts made at coarser spacing and interpolating them to finer grids. However, forecast quality is somewhat dependent upon perturbation amplitude, with smaller scaling values leading to significant underdispersion. Traditional forecast skill scores show somewhat contradictory results for accumulated precipitation, with the equitable threat score most consistent with qualitative performance.

* Current affiliation: Oklahoma Climatological Survey, Norman, Oklahoma

Corresponding author address: Dr. Fanyou Kong, Center for Analysis and Prediction of Storms, University of Oklahoma, 120 David L. Boren Blvd., National Weather Center, Suite 2500, Norman, OK 73072. Email: fkong@ou.edu

Abstract

In Part I, the authors used a full physics, nonhydrostatic numerical model with horizontal grid spacing of 24 km and nested grids of 6- and 3-km spacing to generate the ensemble forecasts of an observed tornadic thunderstorm complex. The principal goal was to quantify the value added by fine grid spacing, as well as the assimilation of Doppler radar data, in both probabilistic and deterministic frameworks. The present paper focuses exclusively on 3-km horizontal grid spacing ensembles and the associated impacts on the forecast quality of temporal forecast sequencing, the construction of initial perturbations, and data assimilation. As in Part I, the authors employ a modified form of the scaled lagged average forecasting technique and use Stage IV accumulated precipitation estimates for verification. The ensemble mean and spread of accumulated precipitation are found to be similar in structure, mimicking their behavior in global models. Both the assimilation of Doppler radar data and the use of shorter (1–2 versus 3–5 h) forecast lead times improve ensemble precipitation forecasts. However, even at longer lead times and in certain situations without assimilated radar data, the ensembles are able to capture storm-scale features when the associated control forecast in a deterministic framework fails to do so. This indicates the potential value added by ensembles although this single case is not sufficient for drawing general conclusions. The creation of initial perturbations using forecasts of the same grid spacing shows no significant improvement over simply extracting perturbations from forecasts made at coarser spacing and interpolating them to finer grids. However, forecast quality is somewhat dependent upon perturbation amplitude, with smaller scaling values leading to significant underdispersion. Traditional forecast skill scores show somewhat contradictory results for accumulated precipitation, with the equitable threat score most consistent with qualitative performance.

* Current affiliation: Oklahoma Climatological Survey, Norman, Oklahoma

Corresponding author address: Dr. Fanyou Kong, Center for Analysis and Prediction of Storms, University of Oklahoma, 120 David L. Boren Blvd., National Weather Center, Suite 2500, Norman, OK 73072. Email: fkong@ou.edu

Save
Cover Monthly Weather Review
Monthly Weather Review

  • Brewster, K. , 1996: Implementation of a Bratseth analysis scheme including Doppler radar. Preprints, 15th Conf. on Weather Analysis and Forecasting, Norfolk, VA, Amer. Meteor. Soc., 92–95.

  • Brier, G. W. , 1950: Verification of forecasts expressed in terms of probability. Mon. Wea. Rev., 78 , 1–3.

  • Brooks, H. E. , C. A. Doswell III , and R. A. Maddox , 1992: On the use of mesoscale and cloud-scale models in operational forecasting. Wea. Forecasting, 7 , 120–132.

    • Search Google Scholar
    • Export Citation

  • Du, J. , S. L. Mullen , and F. Sanders , 1997: Short-range ensemble forecasting of quantitative precipitation. Mon. Wea. Rev., 125 , 2427–2459.

    • Search Google Scholar
    • Export Citation

  • Ebisuzaki, W. , and E. Kalnay , 1991: Ensemble experiments with a new lagged analysis forecasting scheme. Research Activities in Atmospheric and Oceanic Modelling. WMO Rep. 15, 423 pp. [Available from WMO, C. P. No. 2300, CH1211, Geneva, Switzerland.].

  • Elmore, K. L. , D. J. Stensrud , and K. C. Crawford , 2002a: Ensemble cloud model applications to forecasting thunderstorms. J. Appl. Meteor., 41 , 363–383.

    • Search Google Scholar
    • Export Citation

  • Elmore, K. L. , D. J. Stensrud , and K. C. Crawford , 2002b: Explicit cloud-scale models for operational forecasts: A note of caution. Wea. Forecasting, 17 , 873–884.

    • Search Google Scholar
    • Export Citation

  • Elmore, K. L. , S. J. Weiss , and P. C. Banacos , 2003: Operational ensemble cloud model forecasts: Some preliminary results. Wea. Forecasting, 18 , 953–964.

    • Search Google Scholar
    • Export Citation

  • Hou, D. , E. Kalnay , and K. K. Droegemeier , 2001: Objective verification of the SAMEX’98 ensemble forecasts. Mon. Wea. Rev., 129 , 73–91.

    • Search Google Scholar
    • Export Citation

  • Kalnay, E. , 2003: Atmospheric Modeling, Data Assimilation and Predictability. Cambridge University Press, 341 pp.

  • Kong, F. , K. K. Droegemeier , and N. L. Hickmon , 2006: Multiresolution ensemble forecasts of an observed tornadic thunderstorm system. Part I: Comparison of coarse and fine-grid experiments. Mon. Wea. Rev., 134 , 807–833.

    • Search Google Scholar
    • Export Citation

  • Lin, Y. L. , R. D. Farley , and H. D. Orville , 1983: Bulk parameterization of the snow field in a cloud model. J. Climate Appl. Meteor., 22 , 1065–1092.

    • Search Google Scholar
    • Export Citation

  • Marshall, S. F. , P. J. Sousounis , and T. A. Hutchinson , 2004: Verifying mesoscale model precipitation forecasts using an acuity-fidelity approach. Preprints, 20th Conf. on Weather Analysis and Forecasting, Seattle, WA, Amer. Meteor. Soc., CD-ROM, J13.3.

  • Mass, C. F. , D. Ovens , K. J. Westrick , and B. A. Colle , 2002: Does increasing horizontal resolution produce better forecasts? The results of two years of real-time numerical weather prediction in the Pacific Northwest. Bull. Amer. Meteor. Soc., 83 , 407–430.

    • Search Google Scholar
    • Export Citation

  • Mueller, C. , T. Saxen , R. Roberts , J. Wilson , T. Betancourt , S. Dettling , N. Oien , and J. Yee , 2003: NCAR Auto-Nowcast System. Wea. Forecasting, 18 , 545–561.

    • Search Google Scholar
    • Export Citation

  • Murphy, A. H. , 1973: A new vector partition of the probability score. J. Appl. Meteor., 12 , 595–600.

  • Murphy, J. M. , 1988: The impact of ensemble forecasts on predictability. Quart. J. Roy. Meteor. Soc., 114 , 463–493.

  • Nutter, P. , D. Stensrud , and M. Xue , 2004: Effects of coarsely resolved and temporally interpolated lateral boundary conditions on the dispersion of limited-area ensemble forecasts. Mon. Wea. Rev., 132 , 2358–2377.

    • Search Google Scholar
    • Export Citation

  • Patil, D. J. S. , B. P. Hunt , E. Kalnay , J. A. Yorke , and E. Ott , 2001: Local low dimensionality of atmospheric dynamics. Phys. Rev. Lett., 86 , 5878–5881.

    • Search Google Scholar
    • Export Citation

  • Sousounis, P. J. , T. A. Hutchinson , and S. F. Marshall , 2004: A comparison of MM5, WRF, RUC, and ETA performance for Great Plains heavy precipitation events during the spring of 2003. Preprints, 20th Conf. on Weather Analysis and Forecasting, Seattle, WA, Amer. Meteor. Soc., CD-ROM, 24.6.

  • Stensrud, D. J. , J-W. Bao , and T. T. Warner , 2000: Using initial condition and model physics perturbations in short-range ensembles. Mon. Wea. Rev., 128 , 2077–2107.

    • Search Google Scholar
    • Export Citation

  • Tao, W. K. , and J. Simpson , 1993: Goddard Cumulus Ensemble Model. Part I: Model description. Terr. Atmos. Oceanic Sci., 4 , 35–72.

    • Search Google Scholar
    • Export Citation

  • Toth, Z. , and E. Kalnay , 1997: Ensemble forecasting at NCEP: The breeding method. Mon. Wea. Rev., 125 , 3297–3318.

  • Tracton, M. S. , and E. Kalnay , 1993: Operational ensemble prediction at the National Meteorological Center: Practical aspects. Wea. Forecasting, 8 , 379–398.

    • Search Google Scholar
    • Export Citation

  • Wilks, D. S. , 1995: Statistical Methods in the Atmospheric Sciences. Academic Press, 467 pp.

  • Wilson, J. W. , N. A. Crook , C. K. Mueller , J. Sun , and M. Dixon , 1998: Nowcasting thunderstorms: A status report. Bull. Amer. Meteor. Soc., 79 , 2079–2099.

    • Search Google Scholar
    • Export Citation

  • Xue, M. , K. K. Droegemeier , and V. Wong , 2000: The Advanced Regional Prediction System (ARPS)—A multiscale nonhydrostatic atmospheric simulation and prediction model. Part I: Model dynamics and verification. Meteor. Atmos. Phys., 75 , 161–193.

    • Search Google Scholar
    • Export Citation

  • Xue, M. , and Coauthors , 2001: The Advanced Regional Prediction System (ARPS)—A multiscale nonhydrostatic atmospheric simulation and prediction tool. Part II: Model physics and applications. Meteor. Atmos. Phys., 76 , 143–165.

    • Search Google Scholar
    • Export Citation

  • Xue, M. , D. Wang , J. Gao , K. Brewster , and K. K. Droegemeier , 2003: The Advanced Regional Prediction System (ARPS), storm-scale numerical weather prediction and data assimilation. Meteor. Atmos. Phys., 82 , 139–170.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 159 53 8
PDF Downloads 56 31 6