Cluster Analysis for Object-Oriented Verification of Fields: A Variation

Caren Marzban Applied Physics Laboratory, and Department of Statistics, University of Washington, Seattle, Washington, and Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, Oklahoma

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Scott Sandgathe Applied Physics Laboratory, University of Washington, Seattle, Washington

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Abstract

In a recent paper, a statistical method referred to as cluster analysis was employed to identify clusters in forecast and observed fields. Further criteria were also proposed for matching the identified clusters in one field with those in the other. As such, the proposed methodology was designed to perform an automated form of what has been called object-oriented verification. Herein, a variation of that methodology is proposed that effectively avoids (or simplifies) the criteria for matching the objects. The basic idea is to perform cluster analysis on the combined set of observations and forecasts, rather than on the individual fields separately. This method will be referred to as combinative cluster analysis (CCA). CCA naturally lends itself to the computation of false alarms, hits, and misses, and therefore, to the critical success index (CSI). A desirable feature of the previous method—the ability to assess performance on different spatial scales—is maintained. The method is demonstrated on reflectivity data and corresponding forecasts for three dates using three mesoscale numerical weather prediction model formulations—the NCEP/NWS Nonhydrostatic Mesoscale Model (NMM) at 4-km resolution (nmm4), the University of Oklahoma’s Center for Analysis and Prediction of Storms (CAPS) Weather Research and Forecasting Model (WRF) at 2-km resolution (arw2), and the NCAR WRF at 4-km resolution (arw4). In the small demonstration sample herein, model forecast quality is efficiently differentiated when performance is assessed in terms of the CSI. In this sample, arw2 appears to outperform the other two model formulations across all scales when the cluster analysis is performed in the space of spatial coordinates and reflectivity. However, when the analysis is performed only on spatial data (i.e., when only the spatial placement of the reflectivity is assessed), the difference is not significant. This result has been verified both visually and using a standard gridpoint verification, and seems to provide a reasonable assessment of model performance. This demonstration of CCA indicates promise in quickly evaluating mesoscale model performance while avoiding the subjectivity and labor intensiveness of human evaluation or the pitfalls of non-object-oriented automated verification.

Corresponding author address: Caren Marzban, Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, OK 73019. Email: marzban@caps.ou.edu

Abstract

In a recent paper, a statistical method referred to as cluster analysis was employed to identify clusters in forecast and observed fields. Further criteria were also proposed for matching the identified clusters in one field with those in the other. As such, the proposed methodology was designed to perform an automated form of what has been called object-oriented verification. Herein, a variation of that methodology is proposed that effectively avoids (or simplifies) the criteria for matching the objects. The basic idea is to perform cluster analysis on the combined set of observations and forecasts, rather than on the individual fields separately. This method will be referred to as combinative cluster analysis (CCA). CCA naturally lends itself to the computation of false alarms, hits, and misses, and therefore, to the critical success index (CSI). A desirable feature of the previous method—the ability to assess performance on different spatial scales—is maintained. The method is demonstrated on reflectivity data and corresponding forecasts for three dates using three mesoscale numerical weather prediction model formulations—the NCEP/NWS Nonhydrostatic Mesoscale Model (NMM) at 4-km resolution (nmm4), the University of Oklahoma’s Center for Analysis and Prediction of Storms (CAPS) Weather Research and Forecasting Model (WRF) at 2-km resolution (arw2), and the NCAR WRF at 4-km resolution (arw4). In the small demonstration sample herein, model forecast quality is efficiently differentiated when performance is assessed in terms of the CSI. In this sample, arw2 appears to outperform the other two model formulations across all scales when the cluster analysis is performed in the space of spatial coordinates and reflectivity. However, when the analysis is performed only on spatial data (i.e., when only the spatial placement of the reflectivity is assessed), the difference is not significant. This result has been verified both visually and using a standard gridpoint verification, and seems to provide a reasonable assessment of model performance. This demonstration of CCA indicates promise in quickly evaluating mesoscale model performance while avoiding the subjectivity and labor intensiveness of human evaluation or the pitfalls of non-object-oriented automated verification.

Corresponding author address: Caren Marzban, Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, OK 73019. Email: marzban@caps.ou.edu

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

  • Baldwin, M. E., S. Lakshmivarahan, and J. S. Kain, 2002: Development of an “events-oriented” approach to forecast verification. Preprints. 19th Conf. on Weather Analysis and Forecasting and 15th Conf. on Numerical Weather Prediction, San Antonio, TX, Amer. Meteor. Soc., 255–258.

    • Search Google Scholar
    • Export Citation
  • Brown, B. G., J. L. Mahoney, C. A. Davis, R. Bullock, and C. K. Mueller, 2002: Improved approaches for measuring the quality of convective weather forecasts. Preprints, 16th Conf. on Probability and Statistics in the Atmospheric Sciences, Orlando, FL, Amer. Meteor. Soc., 20–25.

  • Brown, B. G., and Coauthors, 2004: New verification approaches for convective weather forecasts. Preprints, 11th Conf. on Aviation, Range, and Aerospace, Hyannis, MA, Amer. Meteor. Soc., 9.4.

  • Bullock, R., B. G. Brown, C. A. Davis, K. W. Manning, and M. Chapman, 2004: An object-oriented approach to quantitative precipitation forecasts. Preprints, 17th Conf. on Probability and Statistics in the Atmospheric Sciences, Seattle, WA, Amer. Meteor. Soc., J12.4.

  • 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
  • Chapman, M., R. Bullock, B. G. Brown, C. A. Davis, K. W. Manning, R. Morss, and A. Takacs, 2004: An object oriented approach to the verification of quantitative precipitation forecasts: Part II—Examples. Preprints. 17th Conf. on Probability and Statistics in the Atmospheric Sciences, Seattle, WA, Amer. Meteor. Soc., J12.5.

    • Search Google Scholar
    • Export Citation
  • Davis, C. A., B. Brown, and R. Bullock, 2006a: 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. A., B. Brown, and R. Bullock, 2006b: Object-based verification of precipitation forecasts. Part II: Application to convective rain systems. Mon. Wea. Rev., 134 , 17851795.

    • Search Google Scholar
    • Export Citation
  • Du, J., and S. L. Mullen, 2000: Removal of distortion error from an ensemble forecast. Mon. Wea. Rev., 128 , 33473351.

  • Ebert, E. E., and J. L. McBride, 2000: Verification of precipitation in weather systems: Determination of systematic errors. J. Hydrol., 239 , 179202.

    • Search Google Scholar
    • Export Citation
  • Everitt, B. S., 1980: Cluster Analysis. 2nd ed. Heinemann Educational Books, 136 pp.

  • Lakshmanan, V., R. Rabin, and V. DeBrunner, 2003: Multiscale storm identification and forecast. J. Atmos. Res., 67–68 , 367380.

  • Marzban, C., 1998: Scalar measures of performance in rare-event situations. Wea. Forecasting, 13 , 753763.

  • Marzban, C., and S. Sandgathe, 2006: Cluster analysis for verification of precipitation fields. Wea. Forecasting, 21 , 824838.

  • Nachamkin, J. E., 2004: Mesoscale verification using meteorological composites. Mon. Wea. Rev., 132 , 941955.

  • Peak, J. E., and P. M. Tag, 1994: Segmentation of satellite imagery using hierarchical thresholding and neural networks. J. Appl. Meteor., 33 , 605616.

    • Search Google Scholar
    • Export Citation
  • Venugopal, V., S. Basu, and E. Foufoula-Georgiou, 2005: A new metric for comparing precipitation patterns with an application to ensemble forecasts. J. Geophys. Res., 110 .D08111, doi:10.1029/2004JD005395.

    • Search Google Scholar
    • Export Citation
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