• Alhamed, A., S. Lakshmivarahan, and D. J. Stensrud, 2002: Cluster analysis of multimodel ensemble data from SAMEX. Mon. Wea. Rev., 130, 226256.

    • Search Google Scholar
    • Export Citation
  • Anderberg, M. R., 1973: Cluster Analysis for Applications. Academic Press, 359 pp.

  • Baldwin, M. E., S. Lakshmivarahan, and J. S. Kain, 2001: Verification of mesoscale features in NWP models. Preprints, Ninth Conf. on Mesoscale Processes, Ft. Lauderdale, FL, Amer. Meteor. Soc., 255–258.

    • Search Google Scholar
    • Export Citation
  • 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
  • Bowler, N. E., and K. R. Mylne, 2009: Ensemble transform Kalman filter perturbations for a regional ensemble prediction system. Quart. J. Roy. Meteor. Soc., 135, 757766.

    • Search Google Scholar
    • Export Citation
  • Bryan, G. H., J. C. Wyngaard, and J. M. Fritsch, 2003: Resolution requirements for the simulation of deep moist convection. Mon. Wea. Rev., 131, 23942416.

    • Search Google Scholar
    • Export Citation
  • Clark, A. J., W. A. Gallus Jr., and T. C. Chen, 2008: Contributions of mixed physics versus perturbed initial/lateral boundary conditions to ensemble-based precipitation forecast skill. Mon. Wea. Rev., 136, 21402156.

    • Search Google Scholar
    • Export Citation
  • Clark, A. J., W. A. Gallus Jr., M. Xue, and F. Kong, 2009: A comparison of precipitation forecast skill between small convection-allowing and large convection-parameterizing ensembles. Wea. Forecasting, 24, 11211140.

    • Search Google Scholar
    • Export Citation
  • Clark, A. J., W. A. Gallus Jr., M. Xue, and F. Kong, 2010a: Growth of spread in convection-allowing and convection-parameterizing ensembles. Wea. Forecasting, 25, 594612.

    • Search Google Scholar
    • Export Citation
  • Clark, A. J., W. A. Gallus Jr., M. Xue, and F. Kong, 2010b: Convection-allowing and convection-parameterizing ensemble forecasts of a mesoscale convective vortex and associated severe weather environment. Wea. Forecasting, 25, 10521081.

    • Search Google Scholar
    • Export Citation
  • Coniglio, M. C., K. L. Elmore, J. S. Kain, S. J. Weiss, M. Xue, and M. L. Weisman, 2010: Evaluation of WRF model output for severe weather forecasting from the 2008 NOAA Hazardous Weather Testbed Spring Experiment. Wea. Forecasting, 25, 408427.

    • Search Google Scholar
    • Export Citation
  • Davis, C., B. Brown, and R. Bullock, 2006: Object-based verification of precipitation forecasts. Part I: Methodology and application to mesoscale rain areas. Mon. Wea. Rev., 134, 17721784.

    • Search Google Scholar
    • Export Citation
  • Davis, C., B. Brown, R. Bullock, and J. Halley-Gotway, 2009: The Method for Object-Based Diagnostic Evaluation (MODE) applied to numerical forecasts from the 2005 NSSL/SPC Spring Program. Wea. Forecasting, 24, 12521267.

    • 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., J. McQueen, G. DiMego, Z. Toth, D. Jovic, B. Zhou, and H. Chuang, 2006: New dimension of NCEP Short-Range Ensemble Forecasting (SREF) system: Inclusion of WRF members. Preprints, WMO Expert Team Meeting on Ensemble Prediction System, Exeter, United Kingdom, WMO, 5 pp.

    • Search Google Scholar
    • Export Citation
  • Dudhia, J., 1989: Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J. Atmos. Sci., 46, 30773107.

    • Search Google Scholar
    • Export Citation
  • Ebert, E. E., 2008: Fuzzy verification of high resolution gridded forecasts: A review and proposed framework. Meteor. Appl., 15, 5164.

    • Search Google Scholar
    • Export Citation
  • 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, E. Rogers, P. Grunmann, V. Koren, G. Gayno, and J. D. Tarpley, 2003: Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. J. Geophys. Res., 108, 8851, doi:10.1029/2002JD003296.

    • Search Google Scholar
    • Export Citation
  • Ferrier, B. S., 1994: A double-moment multiple-phase four-class bulk ice scheme. Part I: Description. J. Atmos. Sci., 51, 249280.

  • Fovell, R. G., and M. Y. C. Fovell, 1993: Climate zones of the conterminous United States defined using cluster analysis. J. Climate, 6, 21032135.

    • Search Google Scholar
    • Export Citation
  • Gilleland, E., D. Ahijevych, B. G. Brown, B. Casati, and E. E. Ebert, 2009: Intercomparison of spatial forecast verification methods. Wea. Forecasting, 24, 14161430.

    • Search Google Scholar
    • Export Citation
  • Gong, X., and M. B. Richman, 1995: On the application of cluster analysis to growing season precipitation data in North America east of the Rockies. J. Climate, 8, 897931.

    • Search Google Scholar
    • Export Citation
  • Hacker, J. P., and Coauthors, 2011: The U.S. Air Force Weather Agency’s mesoscale ensemble: Scientific description and performance results. Tellus, 63A, 117.

    • Search Google Scholar
    • Export Citation
  • Hamill, T. M., C. Snyder, and R. E. Morss, 2000: A comparison of probabilistic forecasts from bred, singular vector, and perturbed observation ensembles. Mon. Wea. Rev., 128, 18351851.

    • Search Google Scholar
    • Export Citation
  • Hohenegger, C., and C. Schär, 2007: Predictability and error growth dynamics in cloud-resolving models. J. Atmos. Sci., 64, 44674478.

    • Search Google Scholar
    • Export Citation
  • Hong, S.-Y., J. Dudhia, and S.-H. Chen, 2004: A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Mon. Wea. Rev., 132, 103120.

    • 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, 7391.

    • Search Google Scholar
    • Export Citation
  • 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
  • Jain, A. K., and R. C. Dubes, 1988: Algorithms for Clustering Data. Prentice Hall, 304 pp.

  • 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
  • Janjic´, Z. I., 2003: A nonhydrostatic model based on a new approach. Meteor. Atmos. Phys., 82, 271285.

  • Janjic, T., L. Nerger, A. Albertella, J. Schröter, and S. Skachko, 2011: On domain localization in ensemble-based Kalman filter algorithms. Mon. Wea. Rev., 139, 20462060.

    • Search Google Scholar
    • Export Citation
  • Jankov, I., W. A. Gallus, M. Segal, B. Shaw, and S. E. Koch, 2005: The impact of different WRF model physical parameterizations and their interactions on warm season MCS rainfall. Wea. Forecasting, 20, 10481060.

    • Search Google Scholar
    • Export Citation
  • Johnson, A., X. Wang, M. Xue, and F. Kong, 2011: Hierarchical cluster analysis of a convection-allowing ensemble during the Hazardous Weather Testbed 2009 Spring Experiment. Part II: Ensemble clustering over the whole experiment period. Mon. Wea. Rev., 139, 36943710.

    • Search Google Scholar
    • Export Citation
  • Kain, J. S., and Coauthors, 2010: Assessing advances in the assimilation of radar data and other mesoscale observations within a collaborative forecasting–research environment. Wea. Forecasting, 25, 15101521.

    • Search Google Scholar
    • Export Citation
  • Kong, F., and Coauthors, 2007: Preliminary analysis on the real-time storm-scale ensemble forecasts produced as a part of the NOAA Hazardous Weather Testbed 2007 Spring Experiment. Preprints, 22nd Conf. on Weather Analysis and Forecasting/18th Conf. on Numerical Weather Prediction, Park City, UT, Amer. Meteor. Soc., 3B.2. [Available online at http://ams.confex.com/ams/22WAF18NWP/techprogram/paper_124667.htm.]

    • Search Google Scholar
    • Export Citation
  • Kong, F., and Coauthors, 2008: Real-time storm-scale ensemble forecast experiment—Analysis of 2008 Spring Experiment data. Preprints, 24th Conf. on Severe Local Storms, Savannah, GA, Amer. Meteor. Soc., 12.3.

    • Search Google Scholar
    • Export Citation
  • Kong, F., and Coauthors, 2009: A real-time storm-scale ensemble forecast system: 2009 Spring Experiment. Preprints, 10th WRF Users’ Workshop, Boulder, CO, NCAR, 3B.7.

    • 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 ams.confex.com/ams/25SLS/techprogram/paper_175822.htm.]

    • Search Google Scholar
    • Export Citation
  • Lacis, A. A., and J. E. Hansen, 1974: A parameterization for the absorption of solar radiation in the earth’s atmosphere. J. Atmos. Sci., 31, 118133.

    • Search Google Scholar
    • Export Citation
  • Leith, C., 1974: Theoretical skill of Monte Carlo forecasts. Mon. Wea. Rev., 102, 409418.

  • Lin, Y.-L., R. D. Farley, and H. D. Orville, 1983: Bulk parameterization of the snow yield in a cloud model. J. Climate Appl. Meteor., 22, 10651092.

    • Search Google Scholar
    • Export Citation
  • Molteni, F., R. Buizza, T. N. Palmer, and T. Petroliagis, 1996: The ECMWF ensemble prediction system: Methodology and validation. Quart. J. Roy. Meteor. Soc., 122, 73119.

    • 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
  • Petch, J. C., 2006: Sensitivity studies of developing convection in a cloud-resolving model. Quart. J. Roy. Meteor. Soc., 132, 345358.

    • Search Google Scholar
    • Export Citation
  • Rutledge, G. K., J. Alpert, and W. Ebisuzaki, 2006: NOMADS: A climate and weather model archive at the National Oceanic and Atmospheric Administration. Bull. Amer. Meteor. Soc., 87, 327341.

    • Search Google Scholar
    • Export Citation
  • Schwartz, C. S., and Coauthors, 2010: Toward improved convection-allowing ensembles: Model physics sensitivities and optimizing probabilistic guidance with small ensemble membership. Wea. Forecasting, 25, 263280.

    • Search Google Scholar
    • Export Citation
  • 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.]

    • Search Google Scholar
    • Export Citation
  • Skok, G., J. Tribbia, J. Rakovec, and B. Brown, 2009: Object-based analysis of satellite-derived precipitation systems over the low- and midlatitude Pacific Ocean. Mon. Wea. Rev., 137, 31963218.

    • Search Google Scholar
    • Export Citation
  • Stensrud, D. J., and N. Yussouf, 2003: Short-range ensemble predictions of 2-m temperature and dewpoint temperature over New England. Mon. Wea. Rev., 131, 25102524.

    • Search Google Scholar
    • Export Citation
  • Stensrud, D. J., J.-W. Bao, and T. T. Warner, 2000: Using initial condition and model physics perturbations in short-range ensemble simulations of mesoscale convective systems. Mon. Wea. Rev., 128, 20772107.

    • 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
  • Theis, S. E., A. Hense, and U. Damrath, 2005: Probabilistic precipitation forecasts from a deterministic model: A pragmatic approach. Meteor. Appl., 12, 257268.

    • 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
  • Toth, Z., and E. Kalnay, 1993: Ensemble forecasting at NMC: The generation of perturbations. Bull. Amer. Meteor. Soc., 74, 23172330.

  • 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., and C. H. Bishop, 2005: Improvement of ensemble reliability with a new dressing kernel. Quart. J. Roy. Meteor. Soc., 131, 965986.

    • 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., T. M. Hamill, J. S. Whitaker, and C. H. Bishop, 2007: A comparison of hybrid ensemble transform Kalman filter-OI and ensemble square-root filter analysis schemes. Mon. Wea. Rev., 135, 10551076.

    • Search Google Scholar
    • Export Citation
  • Ward, J., 1963: Hierarchical grouping to minimize an objective function. J. Amer. Stat. Assoc., 58, 236244.

  • Wei, M., Z. Toth, R. Wobus, and Y. Zhu, 2008: Initial perturbations based on the Ensemble Transform (ET) technique in the NCEP global operational forecast system. Tellus, 60A, 6279.

    • Search Google Scholar
    • Export Citation
  • Weisman, M. L., W. C. Skamarock, and J. B. Klemp, 1997: The resolution dependence of explicitly modeled convective systems. Mon. Wea. Rev., 125, 527548.

    • Search Google Scholar
    • Export Citation
  • Weiss, S. J., J. S. Kain, J. J. Levit, M. E. Baldwin, and D. R. Bright, 2004: Examination of several different versions of the WRF model for the prediction of severe convective weather: The SPC/NSSL Spring Program 2004. Preprints, 22nd Conf. on Severe Local Storms, Hyannis, MA, Amer. Meteor. Soc., 17.1. [Available online at http://ams.confex.com/ams/11aram22sls/techprogram/paper_82052.htm.]

    • Search Google Scholar
    • Export Citation
  • Weiss, S. J., and Coauthors, 2009: NOAA Hazardous Weather Testbed Experimental Forecast Program Spring Experiment 2009: Program overview and operations plan. NOAA, 40 pp. [Available online at http://hwt.nssl.noaa.gov/Spring_2009/Spring_Experiment_2009_ops_plan_2May_v4.pdf.]

    • 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 tool. Part I: Model dynamics and verification. Meteor. Atmos. Phys., 75, 161193.

    • 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, 143166.

    • 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, 2007: CAPS real-time storm-scale ensemble and high-resolution forecasts as part of the NOAA Hazardous Weather Testbed 2007 Spring Experiment. Preprints, 22nd Conf. on Weather Analysis and Forecasting/18th Conf. on Numerical Weather Prediction, Salt Lake City, UT, Amer. Meteor. Soc., 3B.1.

    • Search Google Scholar
    • Export Citation
  • Xue, M., and Coauthors, 2008: CAPS real-time storm-scale ensemble and high-resolution forecasts as part of the NOAA Hazardous Weather Testbed 2008 Spring Experiment. Preprints, 24th Conf. on Several Local Storms, Savannah, GA, Amer. Meteor. Soc., 12.2.

    • Search Google Scholar
    • Export Citation
  • Xue, M., and Coauthors, 2009: CAPS real-time 4-km multi-model convection-allowing ensemble and 1-km convection-resolving forecasts from the NOAA Hazardous Weather Testbed 2009 Spring Experiment. Preprints, 23rd Conf. on Weather Analysis and Forecasting/19th Conf. on Numerical Weather Prediction, Omaha, NE, Amer. Meteor. Soc., 16A.2.

    • Search Google Scholar
    • Export Citation
  • Xue, M., and Coauthors, 2010: CAPS real-time 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.

    • Search Google Scholar
    • Export Citation
  • Zhang, J., K. Howard, and J. J. Gourley, 2005: Constructing three-dimensional multiple-radar reflectivity mosaics: Examples of convective storms and stratiform rain echoes. J. Atmos. Oceanic Technol., 22, 3042.

    • Search Google Scholar
    • Export Citation
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Hierarchical Cluster Analysis of a Convection-Allowing Ensemble during the Hazardous Weather Testbed 2009 Spring Experiment. Part I: Development of the Object-Oriented Cluster Analysis Method for Precipitation Fields

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  • 1 School of Meteorology, University of Oklahoma, and Center for Analysis and Prediction of Storms, Norman, Oklahoma
  • | 2 Center for Analysis and Prediction of Storms, Norman, Oklahoma
  • | 3 School of Meteorology, University of Oklahoma, and Center for Analysis and Prediction of Storms, Norman, Oklahoma
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Abstract

Convection-allowing ensemble forecasts with perturbations to model physics, dynamics, and initial (IC) and lateral boundary conditions (LBC) generated by the Center for the Analysis and Prediction of Storms for the NOAA Hazardous Weather Testbed (HWT) Spring Experiments provide a unique opportunity to understand the relative impact of different sources of perturbation on convection-allowing ensemble diversity. Such impacts are explored in this two-part study through an object-oriented hierarchical cluster analysis (HCA) technique.

In this paper, an object-oriented HCA algorithm, where the dissimilarity of precipitation forecasts is quantified with a nontraditional object-based threat score (OTS), is developed. The advantages of OTS-based HCA relative to HCA using traditional Euclidean distance and neighborhood probability-based Euclidean distance (NED) as dissimilarity measures are illustrated by hourly accumulated precipitation ensemble forecasts during a representative severe weather event.

Clusters based on OTS and NED are more consistent with subjective evaluation than clusters based on traditional Euclidean distance because of the sensitivity of Euclidean distance to small spatial displacements. OTS improves the clustering further compared to NED. Only OTS accounts for important features of precipitation areas, such as shape, size, and orientation, and OTS is less sensitive than NED to precise spatial location and precipitation amount. OTS is further improved by using a fuzzy matching method. Application of OTS-based HCA for regional subdomains is also introduced. Part II uses the HCA method developed in this paper to explore systematic clustering of the convection-allowing ensemble during the full 2009 HWT Spring Experiment period.

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

Convection-allowing ensemble forecasts with perturbations to model physics, dynamics, and initial (IC) and lateral boundary conditions (LBC) generated by the Center for the Analysis and Prediction of Storms for the NOAA Hazardous Weather Testbed (HWT) Spring Experiments provide a unique opportunity to understand the relative impact of different sources of perturbation on convection-allowing ensemble diversity. Such impacts are explored in this two-part study through an object-oriented hierarchical cluster analysis (HCA) technique.

In this paper, an object-oriented HCA algorithm, where the dissimilarity of precipitation forecasts is quantified with a nontraditional object-based threat score (OTS), is developed. The advantages of OTS-based HCA relative to HCA using traditional Euclidean distance and neighborhood probability-based Euclidean distance (NED) as dissimilarity measures are illustrated by hourly accumulated precipitation ensemble forecasts during a representative severe weather event.

Clusters based on OTS and NED are more consistent with subjective evaluation than clusters based on traditional Euclidean distance because of the sensitivity of Euclidean distance to small spatial displacements. OTS improves the clustering further compared to NED. Only OTS accounts for important features of precipitation areas, such as shape, size, and orientation, and OTS is less sensitive than NED to precise spatial location and precipitation amount. OTS is further improved by using a fuzzy matching method. Application of OTS-based HCA for regional subdomains is also introduced. Part II uses the HCA method developed in this paper to explore systematic clustering of the convection-allowing ensemble during the full 2009 HWT Spring Experiment period.

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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