Multiscale Characteristics and Evolution of Perturbations for Warm Season Convection-Allowing Precipitation Forecasts: Dependence on Background Flow and Method of Perturbation

Aaron Johnson School of Meteorology and Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, Oklahoma

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Xuguang Wang School of Meteorology and Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, Oklahoma

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Ming Xue School of Meteorology and Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, Oklahoma

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

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Gang Zhao School of Meteorology and Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, Oklahoma

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Yunheng Wang Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, Oklahoma

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Kevin W. Thomas Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, Oklahoma

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Keith A. Brewster Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, Oklahoma

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Jidong Gao NOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma

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Abstract

Multiscale convection-allowing precipitation forecast perturbations are examined for two forecasts and systematically over 34 forecasts out to 30-h lead time using Haar Wavelet decomposition. Two small-scale initial condition (IC) perturbation methods are compared to the larger-scale IC and physics perturbations in an experimental convection-allowing ensemble. For a precipitation forecast driven primarily by a synoptic-scale baroclinic disturbance, small-scale IC perturbations resulted in little precipitation forecast perturbation energy on medium and large scales, compared to larger-scale IC and physics (LGPH) perturbations after the first few forecast hours. However, for a case where forecast convection at the initial time grew upscale into a mesoscale convective system (MCS), small-scale IC and LGPH perturbations resulted in similar forecast perturbation energy on all scales after about 12 h. Small-scale IC perturbations added to LGPH increased total forecast perturbation energy for this case. Averaged over 34 forecasts, the small-scale IC perturbations had little impact on large forecast scales while LGPH accounted for about half of the error energy on such scales. The impact of small-scale IC perturbations was also less than, but comparable to, the impact of LGPH perturbations on medium scales. On small scales, the impact of small-scale IC perturbations was at least as large as the LGPH perturbations. The spatial structure of small-scale IC perturbations affected the evolution of forecast perturbations, especially at medium scales. There was little systematic impact of the small-scale IC perturbations when added to LGPH. These results motivate further studies on properly sampling multiscale IC errors.

Corresponding author address: Dr. Xuguang Wang, School of Meteorology, University of Oklahoma, 120 David L. Boren Blvd., Norman, OK 73072. E-mail: xuguang.wang@ou.edu

Abstract

Multiscale convection-allowing precipitation forecast perturbations are examined for two forecasts and systematically over 34 forecasts out to 30-h lead time using Haar Wavelet decomposition. Two small-scale initial condition (IC) perturbation methods are compared to the larger-scale IC and physics perturbations in an experimental convection-allowing ensemble. For a precipitation forecast driven primarily by a synoptic-scale baroclinic disturbance, small-scale IC perturbations resulted in little precipitation forecast perturbation energy on medium and large scales, compared to larger-scale IC and physics (LGPH) perturbations after the first few forecast hours. However, for a case where forecast convection at the initial time grew upscale into a mesoscale convective system (MCS), small-scale IC and LGPH perturbations resulted in similar forecast perturbation energy on all scales after about 12 h. Small-scale IC perturbations added to LGPH increased total forecast perturbation energy for this case. Averaged over 34 forecasts, the small-scale IC perturbations had little impact on large forecast scales while LGPH accounted for about half of the error energy on such scales. The impact of small-scale IC perturbations was also less than, but comparable to, the impact of LGPH perturbations on medium scales. On small scales, the impact of small-scale IC perturbations was at least as large as the LGPH perturbations. The spatial structure of small-scale IC perturbations affected the evolution of forecast perturbations, especially at medium scales. There was little systematic impact of the small-scale IC perturbations when added to LGPH. These results motivate further studies on properly sampling multiscale IC errors.

Corresponding author address: Dr. Xuguang Wang, School of Meteorology, University of Oklahoma, 120 David L. Boren Blvd., Norman, OK 73072. E-mail: xuguang.wang@ou.edu
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  • Benjamin, S. G., G. A. Grell, J. M. Brown, T. G. Smirnova, and R. Bleck, 2004: Mesoscale weather prediction with the RUC hybrid isentropic-terrain-following coordinate model. Mon. Wea. Rev., 132, 473494.

    • Search Google Scholar
    • Export Citation
  • Berner, J., S.-Y. Ha, J. P. Hacker, A. Fournier, and C. Snyder, 2011: Model uncertainty in a mesoscale ensemble prediction system: Stochastic versus multiphysics representations. Mon. Wea. Rev., 139, 19721995.

    • Search Google Scholar
    • Export Citation
  • Brooks, H. E., J. W. Lee, and J. P. Craven, 2003: The spatial distribution of severe thunderstorm and tornado environments from global reanalysis data. Atmos. Res.,67–68, 73–94.

  • Buizza, R., and T. N. Palmer, 1995: The singular-vector structure of the atmospheric global circulation. J. Atmos. Sci., 52, 14341456.

    • Search Google Scholar
    • Export Citation
  • Casati, B., G. Ross, and D. B. Stephenson, 2004: A new intensity-scale approach for the verification of spatial precipitation forecasts. Meteor. Appl., 11, 141154.

    • Search Google Scholar
    • Export Citation
  • Clark, A. J., and Coauthors, 2012: An overview of the 2010 Hazardous Weather Testbed Experimental Forecast Program Spring Experiment. Bull. Amer. Meteor. Soc., 93, 5574.

    • Search Google Scholar
    • Export Citation
  • Done, J. M., G. C. Craig, S. L. Gray, and P. A. Clark, 2012: Case-to-case variability of predictability of deep convection in a mesoscale model. Quart. J. Roy. Meteor. Soc., 138, 638648.

    • 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, 24272459.

    • Search Google Scholar
    • Export Citation
  • Du, J., G. Dimego, Z. Toth, D. Jovic, B. Zhou, J. Zhu, J. Wang, and H. Juang, 2009: Recent upgrade of NCEP short-range ensemble forecast (SREF) system. Extended Abstracts, 23rd Conf. on Weather Analysis and Forecasting/19th Conf. on Numerical Weather Prediction, Omaha, NE, Amer. Meteor. Soc., 4A.4. [Available online at http://ams.confex.com/ams/23WAF19NWP/techprogram/paper_153264.htm.]

  • Eckel, F. A., and C. F. Mass, 2005: Aspects of effective mesoscale, short-range ensemble forecasting. Wea. Forecasting, 20, 328350.

  • Ek, M. B., K. E. Mitchell, Y. Lin, P. Grunmann, E. Rogers, G. Gayno, and V. Koren, 2003: Implementation of the upgraded Noah land-surface model in the NCEP operational mesoscale Eta model. J. Geophys. Res., 108, 8851, doi:10.1029/2002JD003296.

    • Search Google Scholar
    • Export Citation
  • Elmore, K. L., D. J. Stensrud, and K. C. Crawford, 2002: Explicit cloud-scale models for operational forecasts: A note of caution. Wea. Forecasting, 17, 873884.

    • Search Google Scholar
    • Export Citation
  • Fritsch, J. M., and R. E. Carbone, 2004: Improving quantitative precipitation forecasts in the warm season: A USWRP research and development strategy. Bull. Amer. Meteor. Soc., 85, 955965.

    • Search Google Scholar
    • Export Citation
  • Gao, J.-D., M. Xue, K. Brewster, and K. K. Droegemeier, 2004: A three-dimensional variational data analysis method with recursive filter for Doppler radars. J. Atmos. Oceanic Technol., 21, 457469.

    • Search Google Scholar
    • Export Citation
  • Grimit, E. P., and C. F. Mass, 2002: Initial results of a mesoscale short-range ensemble forecasting system over the Pacific Northwest. Wea. Forecasting, 17, 192205.

    • Search Google Scholar
    • Export Citation
  • Hamill, T. M., 1999: Hypothesis tests for evaluating numerical precipitation forecasts. Wea. Forecasting, 14, 155167.

  • Hohenegger, C., and C. Schär, 2007a: Predictability and error growth dynamics in cloud-resolving models. J. Atmos. Sci., 64, 44674478.

    • Search Google Scholar
    • Export Citation
  • Hohenegger, C., and C. Schär, 2007b: Atmospheric predictability at synoptic versus cloud-resolving scales. Bull. Amer. Meteor. Soc., 88, 17831793.

    • Search Google Scholar
    • Export Citation
  • Hohenegger, C., D. Lüthi, and C. Schär, 2006: Predictability mysteries in cloud-resolving models. Mon. Wea. Rev., 134, 20952107.

  • Houtekamer, P. L., L. Lefaivre, J. Derome, H. Ritchie, and H. L. Mitchell, 1996: A system simulation approach to ensemble prediction. Mon. Wea. Rev., 124, 12251242.

    • Search Google Scholar
    • Export Citation
  • Hu, M., M. Xue, and K. Brewster, 2006: 3DVAR and cloud analysis with WSR-88D level-II data for the prediction of Fort Worth tornadic thunderstorms. Part I: Cloud analysis and its impact. Mon. Wea. Rev., 134, 675698.

    • Search Google Scholar
    • Export Citation
  • Janjic´, Z. I., 1994: The step-mountain eta coordinate model: Further developments of the convection, viscous sublayer, and turbulence closure schemes. Mon. Wea. Rev., 122, 927945.

    • Search Google Scholar
    • Export Citation
  • Johnson, A., and X. Wang, 2012: Verification and calibration of neighborhood and object-based probablistic precipitation forecasts from a multimodel convection-allowing ensemble. Mon. Wea. Rev., 140, 30543077.

    • Search Google Scholar
    • Export Citation
  • Johnson, A., and X. Wang, 2013: Object-based evaluation of a storm scale ensemble during the 2009 NOAA Hazardous Weather Testbed Spring Experiment. Mon. Wea. Rev., 141, 10791098.

    • Search Google Scholar
    • Export Citation
  • Johnson, A., X. Wang, F. Kong, and M. Xue, 2011a: Hierarchical cluster analysis of a convection-allowing ensemble during the Hazardous Weather Testbed 2009 Spring Experiment. Part I: Development of object-oriented cluster analysis method for precipitation fields. Mon. Wea. Rev., 139, 36733693.

    • Search Google Scholar
    • Export Citation
  • Johnson, A., X. Wang, M. Xue, and F. Kong, 2011b: Hierarchical cluster analysis of a convection-allowing ensemble during the Hazardous Weather Testbed 2009 Spring Experiment. Part II: Season-long ensemble clustering and implication for optimal ensemble design. Mon. Wea. Rev., 139, 36943710.

    • Search Google Scholar
    • Export Citation
  • Kong, F., and Coauthors, 2010: Evaluation of CAPS multi-model storm-scale ensemble forecast for the NOAA HWT 2010 Spring Experiment. Preprints, 25th Conf. on Severe Local Storms, Denver, CO, Amer. Meteor. Soc., P4.18. [Available online at https://ams.confex.com/ams/25SLS/techprogram/paper_175822.htm.]

  • Leith, C. E., 1974: Theoretical skill of Monte Carlo forecasts. Mon. Wea. Rev., 102, 409418.

  • Li, X., M. Charron, L. Spacek, and G. Candille, 2008: A regional ensemble prediction system based on moist targeted singular vectors and stochastic parameter perturbations. Mon. Wea. Rev., 136, 443462.

    • Search Google Scholar
    • Export Citation
  • Lorenz, E. N., 1963: Deterministic nonperiodic flow. J. Atmos. Sci., 20, 130141.

  • Lorenz, E. N., 1969: The predictability of a flow which possesses many scales of motion. Tellus, 21, 289307.

  • Lu, C., H. Yuan, B. E. Schwartz, and S. G. Benjamin, 2007: Short-range numerical weather prediction using time-lagged ensembles. Wea. Forecasting, 22, 580595.

    • Search Google Scholar
    • Export Citation
  • Luo, Y., and L. Zhang, 2011: A case study of the error growth and predictability of a Meiyu frontal precipitation event. Acta Meteor. Sin., 25 (4), 430440.

    • Search Google Scholar
    • Export Citation
  • Marsigli, C., A. Montani, F. Nerozzi, T. Paccagnella, S. Tibaldi, F. Molteni, and R. Buizza, 2001: A strategy for high-resolution ensemble prediction. II: Limited area experiments in four Alpine flood events. Quart. J. Roy. Meteor. Soc., 127, 20952115.

    • Search Google Scholar
    • Export Citation
  • Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res., 102 (D14), 16 6631616 682.

    • Search Google Scholar
    • Export Citation
  • Morrison, H., and W. W. Grabowski, 2008: A novel approach for representing ice microphysics in models: Description and tests using a kinematic framework. J. Atmos. Sci., 65, 15281548.

    • Search Google Scholar
    • Export Citation
  • Noh, Y., W. G. Cheon, S. Y. Hong, and S. Raasch, 2003: Improvement of the K-profile model for the planetary boundary layer based on large eddy simulation data. Bound.-Layer Meteor., 107, 421427.

    • Search Google Scholar
    • Export Citation
  • Palmer, R. D., and Coauthors, 2011: Observations of the 10 May 2010 tornado outbreak using OU-PRIME: Potential for new science with high-resolution polarimetric radar. Bull. Amer. Meteor. Soc., 92, 871891.

    • Search Google Scholar
    • Export Citation
  • Rogers, E., and Coauthors, 2009: The NCEP North American mesoscale modeling system: Recent changes and future plans. Extended Abstacts, 23rd Conf. on Weather Analysis and Forecasting/19th Conf. on Numerical Weather Prediction, Omaha, NE, Amer. Meteor. Soc., 2A.4. [Available online at https://ams.confex.com/ams/23WAF19NWP/techprogram/paper_154114.htm.]

  • Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, W. Wang, and J. G. Powers, 2005: A description of the advanced research WRF version 2. NCAR Tech. Note NCAR/TN-468_STR, 88 pp. [Available from UCAR Communications, P.O. Box 3000, Boulder, CO 80307.]

  • Stensrud, D. J., and J. M. Fritsch, 1993: Mesoscale convective systems in weakly forced large-scale environments. Part I: Observations. Mon. Wea. Rev., 121, 33263344.

    • Search Google Scholar
    • Export Citation
  • Stensrud, D. J., H. Brooks, J. Du, M. S. Tracton, and E. Rogers, 1999: Using ensembles for short-range forecasting. Mon. Wea. Rev., 127, 433446.

    • Search Google Scholar
    • Export Citation
  • Tao, W.-K., and Coauthors, 2003: Microphysics, radiation, and surface processes in the Goddard Cumulus Ensemble (GCE) model. Meteor. Atmos. Phys., 82, 97137.

    • Search Google Scholar
    • Export Citation
  • Thompson, G., P. R. Field, R. M. Rasmussen, and W. D. Hall, 2008: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization. Mon. Wea. Rev., 136, 50955115.

    • Search Google Scholar
    • Export Citation
  • Thompson, P., 1957: Uncertainty in the initial state as a factor in the predictability of large scale atmospheric flow patterns. Tellus, 9, 275295.

    • Search Google Scholar
    • Export Citation
  • Toth, Z., and E. Kalnay, 1997: Ensemble forecasting at NCEP and the breeding method. Mon. Wea. Rev., 125, 32973319.

  • Walser, A., D. Lüthi, and C. Schär, 2004: Predictability of precipitation in a cloud-resolving model. Mon. Wea. Rev., 132, 560577.

  • Wang, X., and C. H. Bishop, 2003: A comparison of breeding and ensemble transform Kalman filter ensemble forecast schemes. J. Atmos. Sci., 60, 11401158.

    • Search Google Scholar
    • Export Citation
  • Wang, X., C. H. Bishop, and S. J. Julier, 2004: Which is better, an ensemble of positive–negative pairs or a centered spherical simplex ensemble? Mon. Wea. Rev., 132, 15901605.

    • Search Google Scholar
    • Export Citation
  • Wang, X., D. Barker, C. Snyder, and T. M. Hamill, 2008: A hybrid ETKF-3DVAR data assimilation scheme for the WRF model. Part I: Observing system simulation experiment. Mon. Wea. Rev., 136, 51165131.

    • Search Google Scholar
    • Export Citation
  • Xu, M., D. J. Stensrud, J.-W. Bao, and T. T. Warner, 2001: Applications of the adjoint technique to short-range ensemble forecasting of mesoscale convective systems. Mon. Wea. Rev., 129, 13951418.

    • Search Google Scholar
    • Export Citation
  • Xue, M., D.-H. Wang, J.-D. 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, 139170.

    • Search Google Scholar
    • Export Citation
  • Xue, M., and Coauthors, 2010: CAPS realtime storm scale ensemble and high resolution forecasts for the NOAA Hazardous Weather Testbed 2010 Spring Experiment. Preprints, 25th Conf. on Severe Local Storms, Denver, CO, Amer. Meteor. Soc., 7B.3. [Available online at https://ams.confex.com/ams/25SLS/techprogram/paper_176056.htm.]

  • Zhang, D.-L., and J. M. Fritsch, 1988: Numerical sensitivity experiments of varying model physics on the structure, evolution, and dynamics of two mesoscale convective systems. J. Atmos. Sci., 45, 261293.

    • Search Google Scholar
    • Export Citation
  • Zhang, F., C. Snyder, and R. Rotunno, 2003: Effects of moist convection on mesoscale predictability. J. Atmos. Sci., 60, 11731185.

  • Zhang, F., A. M. Odins, and J. W. Nielsen-Gammon, 2006: Mesoscale predictability of an extreme warm-season precipitation event. Wea. Forecasting, 21, 149166.

    • Search Google Scholar
    • Export Citation
  • Zhang, J., and Coauthors, 2011: National Mosaic and Multi-Sensor QPE (NMQ) system: Description, results, and future plans. Bull. Amer. Meteor. Soc., 92, 13211338.

    • Search Google Scholar
    • Export Citation
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