• Anthes, R. A., 1977: A cumulus parameterization scheme utilizing a one-dimensional cloud model. Mon. Wea. Rev.,105, 270–286.

  • ——, 1983: Regional models of the atmosphere in middle latitudes. Mon. Wea. Rev.,111, 1306–1335.

  • ——, 1986: The general question of predictability. Mesoscale Meteorology and Forecasting, P. S. Ray, Ed., Amer. Meteor. Soc., 636–656.

  • ——, and T. T. Warner, 1978: Development of hydrostatic models suitable for air pollution and other mesometeorological studies. Mon. Wea. Rev.,106, 1045–1078.

  • ——, E.-Y. Hsie, and Y.-H. Kuo, 1987: Description of the Penn State/NCAR Mesoscale Model Version 4 (MM4). NCAR Tech. Note NCAR/TN 282+STR, 66 pp. [Available from NCAR, P.O. Box 3000, Boulder, CO 80303.].

  • Arakawa, A., and W. H. Schubert, 1974: Interaction of a cumulus cloud ensemble with the large-scale environment. Part I. J. Atmos. Sci.,31, 674–701.

  • ——, and V. R. Lamb, 1977: Computational design of the basic dynamical process of the UCLA general circulation model. Methods Comput. Phys.,17, 173–265.

  • Augustine S. J., S. L. Mullen, and D. P. Baumhefner, 1991: Examination of actual analysis differences for use in Monte Carlo forecasting. Preprints, 16th Annual Climate Diagnostics Workshop, Los Angeles, CA, NOAA/NWS/NMC/CAC, 375–378.

  • Barnes, S. L., 1964: A technique for maximizing details in numerical weather map analyses. J. Appl. Meteor.,3, 396–409.

  • ——, 1973: Mesoscale objective analysis using weighted time-series observations. NOAA Tech. Memo. ERL NSSL-62, 60 pp. [NTIS COM-73-10781.].

  • Baumhefner, D. P., 1984: Analysis and forecast intercomparisons using the FGGE SOP1 data base. Proceedings of the First National Workshop on the Global Weather Experiment, Vol. 2, Part 1, National Academy Press, 228–246.

  • ——, and D. J. Perkey, 1982: Evaluation of lateral boundary errors in a limited-domain model. Tellus,34, 409–428.

  • Blackadar, A. K., 1979: High resolution models of the planetary boundary layer. Adv. Environ. Sci. Eng.,1(1), 50–85.

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

  • Brooks, H. E., and C. A. Doswell III, 1993: New technology and numerical weather prediction wasted opportunity? Weather,48, 173–177.

  • ——, M. S. Tracton, D. J. Stensrud, G. J. DiMego, and Z. Toth, 1995: Short-range ensemble forecasting (SREF): Report from a workshop. Bull. Amer. Meteor. Soc.,76, 1617–1624.

  • Daley, R., and T. Mayer, 1986: Estimates of global analysis error from the global weather experiment observational network. Mon. Wea. Rev.,114, 1642–1653.

  • EarthInfo, Inc., 1990: Climatedata—Hourly precipitation over the United States. EarthInfo, Inc.

  • Epstein, E. S., 1969: A scoring system for probability forecasts of ranked categories. J. Appl. Meteor.,8, 985–987.

  • Errico, R. M., 1983: A guide to transform software for non-linear normal-mode initialization of the NCAR Community Forecast Model. NCAR Tech. Note NCAR/TN-217+IA. 86 pp. [Available from NCAR, P.O. Box 3000, Boulder, CO 80303.].

  • ——, and D. P. Baumhefner, 1987: Predictability experiments using a high-resolution and limited-area model. Mon. Wea. Rev.,115, 488–504.

  • Grell, G., Y.-H. Kuo, and R. Pasch, 1991: Semiprognostic tests of cumulus parameterization schemes in the middle latitudes. Mon. Wea. Rev.,119, 5–31.

  • Grumm, R. H., 1993: Characteristics of surface cyclone forecasts in the aviation run of the global spectral model. Wea. Forecasting,8, 87–112.

  • Hamill, T. M., and S. J. Colucci, 1996: Random and systematic error in NMC’s ETA short-range ETA ensembles. Preprints, 13th Conf. on Probabililty and Statistics in the Atmospheric Sciences, San Francisco, CA, Amer. Meteor. Soc., 51–56.

  • ——, and ——, 1997: Verification of Eta–RSM short-range ensemble forecasts. Mon. Wea. Rev.,125, 1312–1327.

  • Hoffman, R. N., and E. Kalnay, 1983: Lagged-average forecasting, an alternative to Monte Carlo forecasting. Tellus,35, 100–118.

  • Hoke, J. E., N. A. Phillips, G. J. DiMego, J. J. Tucillo, and J. G. Sela, 1989: The regional analysis and forecast system of the National Meterological Center. Wea. Forecasting,4, 323–334.

  • Holloway, J. L., Jr., 1958: Smoothing and filtering of time series and space fields. Advances in Geophysics, Vol. 4, Academic Press, 351–389.

  • Hsie, E.-Y., R. A. Anthes, and D. Keyser, 1984: Numerical simulation of frontogenesis in a moist atmosphere. J. Atmos. Sci.,41, 2581–2594.

  • Kallen, E., and X.-Y. Huang, 1988: The influence of isolated observations on short-range numerical weather forecasts. Tellus,40A, 324–336.

  • Kuo, H. L., 1974: Further studies of the parameterization of the effect of cumulus convection on large-scale flow. J. Atmos. Sci.,31, 1232–1240.

  • Kuo, Y.-H., and S. Low-Nam, 1990: Prediction of nine explosive cyclones over the western Atlantic with a regional model. Mon. Wea. Rev.,118, 3–25.

  • Leith, C. E., 1974: Theoretical skill of Monte Carlo forecasts. Mon. Wea. Rev.,102, 409–418.

  • Lindzen, R. S., and M. Fox-Rabinowitz, 1989: Consistent vertical and horizontal resolution. Mon. Wea. Rev.,117, 2575–2583.

  • Lorenz, E. N., 1963: Deterministic nonperiodic flow. J. Atmos. Sci.,20, 130–141.

  • Manning, K. W., and P. L. Haagenson, 1992: Data ingest and objectives analysis for the PSU/NCAR modeling system: Programs DATAGRID and RAWINS. NCAR. Tech. Note NCAR/TN 376+STR+IA, 209 pp. [Available from NCAR, P.O. Box 3000, Boulder, CO 80303.].

  • Mass, C. F., and D. M. Schultz, 1993: The structure and evolution of a simulated midlatitude cyclone over land. Mon. Wea. Rev.,121, 889–917.

  • Molteni, F., T. N. Palmer, R. Buizza, and T. Pertroliagis, 1996: The ECMWF ensemble prediction system: Methodology and verification. Quart. J. Roy. Meteor. Soc.,122, 73–121.

  • Mullen, S. L., and D. P. Baumhefner, 1989: The impact of initial condition uncertainty on numerical simulations of large scale explosive cyclogenesis. Mon. Wea. Rev.,117, 2289–2329.

  • ——, and ——, 1991: Monte Carlo simulations of explosive cyclogenesis using a low-resolution, global spectral model. Preprints, Ninth Conf. on Numerical Weather Prediction, Denver, CO, Amer. Meteor. Soc., 750–751.

  • ——, and ——, 1994: Monte Carlo simulations of explosive cyclogenesis. Mon. Wea. Rev.,122, 1548–1567.

  • ——, and J. Du, 1994: Monte Carlo forecasts of explosive cyclogenesis with a limited-area, mesoscale model. Preprints, 10th Conf. on Numerical Weather Prediction, Portland, OR, Amer. Meteor. Soc., 638–640.

  • Mureau, R., F. Molteni, and T. N. Palmer, 1993: Ensemble prediction using dynamically conditioned perturbations. Quart. J. Roy. Meteor. Soc.,119, 299–323.

  • Murphy, A. H., 1971: A note on the ranked probability score. J. Appl. Meteor.,10, 155–156.

  • Murphy, J. M., 1988: The impact of ensemble forecasts on predictability. Quart. J. Roy. Meteor. Soc.,114, 463–494.

  • NOAA, 1987: Monthly relative frequencies of precipitation for the United States for 6-, 12-, and 24-h periods. NOAA Tech. Rep. No. 39, NWS/NOAA/US Department of Commerce, 262 pp.

  • NOAA/NCDC, 1987: Storm Data. Vol. 29, No. 12, National Oceanic and Atmospheric Administration/National Climate Data Center, 47 pp.

  • Palmer, T. N., R. Mureau, and F. Molteni, 1990: The Monte Carlo forecast. Weather,45, 198–207.

  • Persson, P. O. G., and T. T. Warner, 1991: Model generation of spurious gravity waves due to inconsistency of the vertical and horizontal resolution. Mon. Wea. Rev.,119, 917–935.

  • Powers, J. G., and R. J. Reed, 1993: Numerical simulation of the large-amplitude mesoscale gravity-wave event of 15 December 1987 in central United States. Mon. Wea. Rev.,121, 2285–2308.

  • Prager, T., T. Vukicevic, J.-N. Thepaut, J.-F. Louis, P. Gauthier, R. Errico, J. Derber, and P. Courtier, 1995: Second workshop on adjoint applications in dynamic meteorology, Visegrad, Hungary, 2–6 May 1994. Bull. Amer. Meteor. Soc.,76, 375–379.

  • Roebber, P. J., 1990: Variability in successive operational model forecasts of maritime cyclogenesis. Wea. Forecasting,5, 586–595.

  • ——, 1993: A case study of self-development as an antecedent conditioning process in explosive cyclogenesis. Mon. Wea. Rev.,121, 976–1006.

  • Sanders, F., 1971: Analytic solutions of the non-linear omega and vorticity equations for a structurally simple model of disturbances in the baroclinic westerlies. Mon. Wea. Rev.,99, 393–408.

  • ——, 1986: Trends in skill of Boston forecasts made at MIT 1966–84. Bull. Amer. Meteor. Soc.,67, 170–176.

  • ——, 1992: Skill of operational models in cyclone prediction out to five-days during ERICA. Wea. Forecasting,7, 3–25.

  • ——, and J. R. Gyakum, 1980: Synoptic-dynamic climatology of the “bomb.” Mon. Wea. Rev.,108, 1589–1606.

  • Schaefer, J. T., 1990: The critical success index as an indicator of warning skill. Wea. Forecasting,5, 570–575.

  • Schneider, R. S., 1990: Large-amplitude mesoscale wave disturbances with the intense Midwestern extratropical cyclone of 15 December 1987. Wea. Forecasting,5, 533–558.

  • Smith, B. B., and S. L. Mullen, 1993: An evaluation of sea-level cyclone forecasts produced by NMC’s Nested Grid Model and Global Spectral Model. Wea. Forecasting,8, 37–56.

  • Stensrud, D. J., and J. M. Fritsch, 1994a: Mesoscale convective systems in weakly forced large-scale environment. Part II: Generation of a mesoscale initial condition. Mon. Wea. Rev.,122, 2068–2083.

  • ——, and ——, 1994b: Mesoscale convective systems in weakly forced large-scale environment. Part III: Numerical simulations and implications for operational forecasting. Mon. Wea. Rev.,122, 2084–2104.

  • Toth, Z., and E. Kalnay, 1993: Ensemble forecasting at NMC: The generation of perturbations. Bull. Amer. Meteor. Soc.,74, 2317–2330.

  • Tracton, M. S., and E. Kalnay, 1993: Operational ensemble prediction at the National Meteorological Center: Practical aspects. Wea. Forecasting,8, 378–398.

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

  • Williamson, D. L., J. T. Kiehl, V. Ramanathan, R. E. Dickinson, and J. J. Hack, 1987: Description of the NCAR Community Climate Model (CCM1). NCAR Tech. Note NCAR/TN-285+STR, 112 pp. [Available from NCAR, P.O. Box 3000, Boulder, CO 80303.].

  • Zhang, D.-L., E.-Y. Hsie, and M. W. Moncrieff, 1988: A comparison of explicit and implicit prediction of convective and stratiforrn precipitating weather systems with a meso-β scale numerical model. Quart. J. Roy. Meteor. Soc.,114, 31–60.

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Short-Range Ensemble Forecasting of Quantitative Precipitation

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  • 1 Institute of Atmospheric Physics, The University of Arizona, Tucson, Arizona
  • | 2 Marblehead, Massachusetts
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Abstract

The impact of initial condition uncertainty (ICU) on quantitative precipitation forecasts (QPFs) is examined for a case of explosive cyclogenesis that occurred over the contiguous United States and produced widespread, substantial rainfall. The Pennsylvania State University–National Center for Atmospheric Research (NCAR) Mesoscale Model Version 4 (MM4), a limited-area model, is run at 80-km horizontal resolution and 15 layers to produce a 25-member, 36-h forecast ensemble. Lateral boundary conditions for MM4 are provided by ensemble forecasts from a global spectral model, the NCAR Community Climate Model Version 1 (CCM1). The initial perturbations of the ensemble members possess a magnitude and spatial decomposition that closely match estimates of global analysis error, but they are not dynamically conditioned. Results for the 80-km ensemble forecast are compared to forecasts from the then operational Nested Grid Model (NGM), a single 40-km/15-layer MM4 forecast, a single 80-km/29-layer MM4 forecast, and a second 25-member MM4 ensemble based on a different cumulus parameterization and slightly different unperturbed initial conditions.

Large sensitivity to ICU marks ensemble QPF. Extrema in 6-h accumulations at individual grid points vary by as much as 3.00". Ensemble averaging reduces the root-mean-square error (rmse) for QPF. Nearly 90% of the improvement is obtainable using ensemble sizes as small as 8–10. Ensemble averaging can adversely affect the bias and equitable threat scores, however, because of its smoothing nature. Probabilistic forecasts for five mutually exclusive, completely exhaustive categories are found to be skillful relative to a climatological forecast. Ensemble sizes of approximately 10 can account for 90% of improvement in categorical forecasts relative to that for the average of individual forecasts. The improvements due to short-range ensemble forecasting (SREF) techniques exceed any due to doubling the resolution, and the error growth due to ICU greatly exceeds that due to different resolutions.

If the authors’ results are representative, they indicate that SREF can now provide useful QPF guidance and increase the accuracy of QPF when used with current analysis–forecast systems.

Corresponding author address: Dr. Steven L. Mullen, Department of Atmospheric Sciences, PAS Building 81, The University of Arizona, Tucson, AZ 85721.

Abstract

The impact of initial condition uncertainty (ICU) on quantitative precipitation forecasts (QPFs) is examined for a case of explosive cyclogenesis that occurred over the contiguous United States and produced widespread, substantial rainfall. The Pennsylvania State University–National Center for Atmospheric Research (NCAR) Mesoscale Model Version 4 (MM4), a limited-area model, is run at 80-km horizontal resolution and 15 layers to produce a 25-member, 36-h forecast ensemble. Lateral boundary conditions for MM4 are provided by ensemble forecasts from a global spectral model, the NCAR Community Climate Model Version 1 (CCM1). The initial perturbations of the ensemble members possess a magnitude and spatial decomposition that closely match estimates of global analysis error, but they are not dynamically conditioned. Results for the 80-km ensemble forecast are compared to forecasts from the then operational Nested Grid Model (NGM), a single 40-km/15-layer MM4 forecast, a single 80-km/29-layer MM4 forecast, and a second 25-member MM4 ensemble based on a different cumulus parameterization and slightly different unperturbed initial conditions.

Large sensitivity to ICU marks ensemble QPF. Extrema in 6-h accumulations at individual grid points vary by as much as 3.00". Ensemble averaging reduces the root-mean-square error (rmse) for QPF. Nearly 90% of the improvement is obtainable using ensemble sizes as small as 8–10. Ensemble averaging can adversely affect the bias and equitable threat scores, however, because of its smoothing nature. Probabilistic forecasts for five mutually exclusive, completely exhaustive categories are found to be skillful relative to a climatological forecast. Ensemble sizes of approximately 10 can account for 90% of improvement in categorical forecasts relative to that for the average of individual forecasts. The improvements due to short-range ensemble forecasting (SREF) techniques exceed any due to doubling the resolution, and the error growth due to ICU greatly exceeds that due to different resolutions.

If the authors’ results are representative, they indicate that SREF can now provide useful QPF guidance and increase the accuracy of QPF when used with current analysis–forecast systems.

Corresponding author address: Dr. Steven L. Mullen, Department of Atmospheric Sciences, PAS Building 81, The University of Arizona, Tucson, AZ 85721.

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