Editorial Type:
Article
Article Type:
Research Article

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

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

Search for other papers by Aaron Johnson in
Current site
Google Scholar
PubMed Close
,
Xuguang Wang School of Meteorology, University of Oklahoma, and Center for Analysis and Prediction of Storms, Norman, Oklahoma

Search for other papers by Xuguang Wang in
Current site
Google Scholar
PubMed Close
,
Ming Xue School of Meteorology, University of Oklahoma, and Center for Analysis and Prediction of Storms, Norman, Oklahoma

Search for other papers by Ming Xue in
Current site
Google Scholar
PubMed Close
, and
Fanyou Kong Center for Analysis and Prediction of Storms, Norman, Oklahoma

Search for other papers by Fanyou Kong in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Dec 2011
DOI:
https://doi.org/10.1175/MWR-D-11-00016.1
Page(s):
3694–3710
Restricted access

Abstract

Twenty-member real-time convection-allowing storm-scale ensemble forecasts with perturbations to model physics, dynamics, initial conditions (IC), and lateral boundary conditions (LBC) during the NOAA Hazardous Weather Testbed Spring Experiment provide a unique opportunity to study the relative impact of different sources of perturbation on convection-allowing ensemble diversity. In Part II of this two-part study, systematic similarity/dissimilarity of hourly precipitation forecasts among ensemble members from the spring season of 2009 are identified using hierarchical cluster analysis (HCA) with a fuzzy object-based threat score (OTS), developed in Part I. In addition to precipitation, HCA is also performed on ensemble forecasts using the traditional Euclidean distance for wind speed at 10 m and 850 hPa, and temperature at 500 hPa.

At early lead times (3 h, valid at 0300 UTC) precipitation forecasts cluster primarily by data assimilation and model dynamic core, indicating a dominating impact of models, with secondary clustering by microphysics. There is an increasing impact of the planetary boundary layer (PBL) scheme on clustering relative to the microphysics scheme at later lead times. Forecasts of 10-m wind speed cluster primarily by the PBL scheme at early lead times, with an increasing impact of LBC at later lead times. Forecasts of midtropospheric variables cluster primarily by IC at early lead times and LBC at later lead times. The radar and Mesonet data assimilation (DA) show its impact, with members without DA in a distinct cluster, through the 12-h lead time (valid at 1200 UTC) for both precipitation and nonprecipitation variables. The implication for optimal ensemble design for storm-scale forecasts is also discussed.

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

Twenty-member real-time convection-allowing storm-scale ensemble forecasts with perturbations to model physics, dynamics, initial conditions (IC), and lateral boundary conditions (LBC) during the NOAA Hazardous Weather Testbed Spring Experiment provide a unique opportunity to study the relative impact of different sources of perturbation on convection-allowing ensemble diversity. In Part II of this two-part study, systematic similarity/dissimilarity of hourly precipitation forecasts among ensemble members from the spring season of 2009 are identified using hierarchical cluster analysis (HCA) with a fuzzy object-based threat score (OTS), developed in Part I. In addition to precipitation, HCA is also performed on ensemble forecasts using the traditional Euclidean distance for wind speed at 10 m and 850 hPa, and temperature at 500 hPa.

At early lead times (3 h, valid at 0300 UTC) precipitation forecasts cluster primarily by data assimilation and model dynamic core, indicating a dominating impact of models, with secondary clustering by microphysics. There is an increasing impact of the planetary boundary layer (PBL) scheme on clustering relative to the microphysics scheme at later lead times. Forecasts of 10-m wind speed cluster primarily by the PBL scheme at early lead times, with an increasing impact of LBC at later lead times. Forecasts of midtropospheric variables cluster primarily by IC at early lead times and LBC at later lead times. The radar and Mesonet data assimilation (DA) show its impact, with members without DA in a distinct cluster, through the 12-h lead time (valid at 1200 UTC) for both precipitation and nonprecipitation variables. The implication for optimal ensemble design for storm-scale forecasts is also discussed.

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
Save
Cover Monthly Weather Review
Monthly Weather Review

  • Alhamed, A., S. Lakshmivarahan, and D. J. Stensrud, 2002: Cluster analysis of multimodel ensemble data from SAMEX. Mon. Wea. Rev., 130, 226–256.

    • Search Google Scholar
    • Export Citation

  • Aligo, E. A., W. A. Gallus, and M. Segal, 2007: Summer rainfall forecast spread in an ensemble initialized with different soil moisture analyses. Wea. Forecasting, 22, 299–314.

    • Search Google Scholar
    • Export Citation

  • Anderberg, M. R., 1973: Cluster Analysis for Applications. Academic Press, 359 pp.

  • Arribas, A., K. B. Robertson, and K. R. Mylne, 2005: Test of a poor man’s ensemble prediction system for short-range probability forecasting. Mon. Wea. Rev., 133, 1825–1839.

    • Search Google Scholar
    • Export Citation

  • Atger, F., 1999: Tubing: An alternative to clustering for the classification of ensemble forecasts. Wea. Forecasting, 14, 741–757.

    • 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, 473–494.

    • 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, 1972–1995.

    • Search Google Scholar
    • Export Citation

  • Brankovic, C., T. N. Palmer, F. Molteni, S. Tibaldi, and U. Cubasch, 1990: Extended-range predictions with ECMWF models: Timelagged ensemble forecasting. Quart. J. Roy. Meteor. Soc., 116, 867–912.

    • Search Google Scholar
    • Export Citation

  • Brankovic, C., B. Matjacic, and S. Ivatek-Sahdan, 2008: Downscaling of ECMWF ensemble forecasts for cases of severe weather: Ensemble statistics and cluster analysis. Mon. Wea. Rev., 136, 3323–3342.

    • 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, 2394–2416.

    • Search Google Scholar
    • Export Citation

  • Cheng, X., and J. M. Wallace, 1993: Cluster analysis of the Northern Hemisphere wintertime 500-hPa height field. J. Atmos. Sci., 50, 2674–2696.

    • 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, 2140–2156.

    • 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, 1121–1140.

    • 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, 1772–1784.

    • Search Google Scholar
    • Export Citation

  • Du, J., J. McQueen, G. DiMego, Z. Toth, D. Jovic, B. Zhou, and H.-Y. 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. [Available online at http://www.emc.ncep.noaa.gov/mmb/SREF/WMO06_full.pdf.]

    • 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, 3077–3107.

    • Search Google Scholar
    • Export Citation

  • 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, 249–280.

    • Search Google Scholar
    • Export Citation

  • Fovell, R. G., and M. Y. C. Fovell, 1993: Climate zones of the conterminous United States defined using cluster analysis. J. Climate, 6, 2103–2135.

    • Search Google Scholar
    • Export Citation

  • Gallus, W. A., Jr., and J. F. Bresch, 2006: Comparison of impacts of WRF dynamic core, physics package, and initial conditions on warm season rainfall forecasts. Mon. Wea. Rev., 134, 2632–2641.

    • 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, 457–469.

    • 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, 897–931.

    • 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, 1–17.

    • Search Google Scholar
    • Export Citation

  • Hohenegger, C., and C. Schär, 2007: Predictability and error growth dynamics in cloud-resolving models. J. Atmos. Sci., 64, 4467–4478.

    • 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, 103–120.

    • 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

  • 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, 675–698.

    • Search Google Scholar
    • Export Citation

  • Jain, A. J., and R. C. Dubes, 1988: Algorithms for Clustering Data. Prentice Hall, 304 pp.

  • Janjić, Z. I., 1994: The step-mountain eta coordinate model: Further developments of the convection, viscous sublayer, and turbulence closure schemes. Mon. Wea. Rev., 122, 927–945.

    • Search Google Scholar
    • Export Citation

  • Janjić, Z. I., 2003: A nonhydrostatic model based on a new approach. Meteor. Atmos. Phys., 82, 271–285.

  • 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, 1048–1060.

    • Search Google Scholar
    • Export Citation

  • Jankov, I., W. A. Gallus, M. Segal, and S. E. Koch, 2007: Influence of initial conditions on the WRF–ARW model QPF response to physical parameterization changes. Wea. Forecasting, 22, 501–519.

    • 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 the object-oriented cluster analysis method for precipitation fields. Mon. Wea. Rev., 139, 3673–3693.

    • Search Google Scholar
    • Export Citation

  • Johnson, A., X. Wang, F. Kong, and M. Xue, 2011b: Object-oriented clustering analysis of CAPS convective scale ensemble forecasts for the NOAA Hazardous Weather Testbed Spring Experiment: A first step toward optimal ensemble configuration for convective scale probabilistic forecasting. Preprints, 24th Conf. on Weather Analysis and Forecasting/20th Conf. on Numerical Weather Prediction, Seattle, WA, Amer. Meteor. Soc., 9A.3. [Available online at http://ams.confex.com/ams/91Annual/webprogram/Paper181155.html.]

    • 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, 1510–1521.

    • Search Google Scholar
    • Export Citation

  • Kalkstein, L., G. Tan, and J. A. Skindlov, 1987: An evaluation of three clustering procedures for use in synoptic climatological classification. J. Climate Appl. Meteor., 26, 717–730.

    • 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, 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

  • 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, 118–133.

    • Search Google Scholar
    • Export Citation

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

  • 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, 1065–1092.

    • Search Google Scholar
    • Export Citation

  • Molinari, J., and M. Dudek, 1992: Parameterization of convective precipitation in mesoscale numerical models: A critical review. Mon. Wea. Rev., 120, 326–344.

    • 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, 73–119.

    • Search Google Scholar
    • Export Citation

  • Nakaegawa, T., and M. Kanamitsu, 2006: Cluster analysis of the seasonal forecast skill of the NCEP SFM over the Pacific–North America sector. J. Climate, 19, 123–138.

    • 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, 421–427.

    • Search Google Scholar
    • Export Citation

  • Palmer, T. N., C. Brankovic, F. Molteni, S. Tibaldi, L. Ferranti, A. Hollingsworth, U. Cubasch, and E. Klinker, 1990: The European Centre for Medium-Range Weather Forecasts (ECMWF) Program on Extended-Range Prediction. Bull. Amer. Meteor. Soc., 71, 1317–1330.

    • Search Google Scholar
    • Export Citation

  • Palmer, T. N., R. Buizza, F. Doblas-Reyes, T. Jung, M. Leutbecher, G. J. Shutts, M. Steinheimer, and A. Weisheimer, 2009: Stochastic parametrization and model uncertainty. ECMWF Tech. Memo. 598, 44 pp. [Available online at http://www.ecmwf.int/publications/library/ecpublications/_pdf/tm/501-600/tm598.pdf.]

    • 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, 345–358.

    • Search Google Scholar
    • Export Citation

  • Schwartz, C., and Coauthors, 2009: Next-day convection-allowing WRF model guidance: A second look at 2-km versus 4-km grid spacing. Mon. Wea. Rev., 137, 3351–3372.

    • 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

  • Stensrud, D. J., J. Bao, and T. T. Warner, 2000: Using initial conditions and model physics perturbations in short-range ensemble simulations of mesoscale convective systems. Mon. Wea. Rev., 128, 2077–2107.

    • 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, 97–137.

    • 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, 5095–5115.

    • Search Google Scholar
    • Export Citation

  • Toth, Z., E. Kalnay, S. M. Tracton, R. Wobus, and J. Irwin, 1997: A synoptic evaluation of the NCEP ensemble. Wea. Forecasting, 12, 140–153.

    • Search Google Scholar
    • Export Citation

  • 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

  • Wandishin, M. S., S. L. Mullen, D. J. Stensrud, and H. E. Brooks, 2001: Evaluation of a short-range multimodel ensemble system. Mon. Wea. Rev., 129, 729–747.

    • Search Google Scholar
    • Export Citation

  • Wang, X., D. Barker, C. Snyder, and T. M. Hamill, 2008a: A hybrid ETKF-3DVAR data assimilation scheme for the WRF model. Part I: Observing System Simulation Experiment. Mon. Wea. Rev., 136, 5116–5131.

    • Search Google Scholar
    • Export Citation

  • Wang, X., D. Barker, C. Snyder, and T. M. Hamill, 2008b: A hybrid ETKF-3DVAR data assimilation scheme for the WRF model. Part II: Real observation experiments. Mon. Wea. Rev., 136, 5132–5147.

    • Search Google Scholar
    • Export Citation

  • Weber, R. O., and P. Kaufmann, 1995: Automated classification scheme for wind fields. J. Appl. Meteor., 34, 1133–1141.

  • Weckwerth, T. M., and D. B. Parsons, 2006: A review of convection initiation and motivation for IHOP_2002. Mon. Wea. Rev., 134, 5–22.

    • 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, 527–548.

    • Search Google Scholar
    • Export Citation

  • Weisman, M. L., C. Davis, W. Wang, K. W. Manning, and J. B. Klemp, 2008: Experiences with 0–36-h explicit convective forecasts with the WRF-ARW model. Wea. Forecasting, 23, 407–437.

    • Search Google Scholar
    • Export Citation

  • Weiss, S., 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., and W. J. Martin, 2006: A high-resolution modeling study of the 24 May 2002 case during IHOP. Part I: Numerical simulation and general evolution of the dryline and convection. Mon. Wea. Rev., 134, 149–171.

    • 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, 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, 143–166.

    • 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, 139–170.

    • 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

  • Yussouf, N., D. J. Stensrud, and S. Lakshmivarahan, 2004: Cluster analysis of multimodel ensemble data over New England. Mon. Wea. Rev., 132, 2452–2462.

    • Search Google Scholar
    • Export Citation

  • Zhang, D.-L., and W.-Z. Zheng, 2004: Diurnal cycles of surface winds and temperatures as simulated by five boundary layer parameterizations. J. Appl. Meteor., 43, 157–169.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 2898 2123 1114
PDF Downloads 292 66 12