• Baddeley, A. J., 1992a: Errors in binary images and an Lp version of the Hausdorff metric. Nieuw Arch. Wiskunde, 10 , 157183.

  • Baddeley, A. J., 1992b: An error metric for binary images. Robust Computer Vision: Quality of Vision Algorithms, W. Förstner and S. Ruwiedel, Eds., Wichmann, 59–78.

    • Search Google Scholar
    • Export Citation
  • Borgefors, G., 1986: Distance transformations in digital images. Comput. Vision Graphics Image Process., 34 , 3. 344371.

  • Briggs, W. M., and R. A. Levine, 1997: Wavelets and field forecast verification. Mon. Wea. Rev., 125 , 13291341.

  • Brown, B. G., R. G. Bullock, J. Halley Gotway, D. Ahijevych, E. Gilleland, and L. Holland, 2007: Application of the MODE object-based verification tool for the evaluation of model precipitation fields. Preprints, 22nd Conf. on Weather Analysis and Forecasting/18th Conf. on Numerical Weather Prediction, Park City, UT, Amer. Meteor. Soc., 10A.2. [Available online at http://ams.confex.com/ams/pdfpapers/124856.pdf.].

  • Browning, K. A., C. G. Collier, P. R. Larke, P. Menmuir, G. A. Monk, and R. G. Owens, 1982: On the forecasting of frontal rain using a weather radar network. Mon. Wea. Rev., 110 , 534552.

    • 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
  • Davis, C. A., B. G. Brown, and R. G. 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. G. Brown, and R. G. 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., 2007: Fuzzy verification of high resolution gridded forecasts: A review and proposed framework. Meteor. Appl., in press.

  • 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
  • Ebert, E. E., U. Damrath, W. Wergen, and M. E. Baldwin, 2003: The WGNE assessment of short-term quantitative precipitation forecasts. Bull. Amer. Meteor. Soc., 84 , 481492.

    • Search Google Scholar
    • Export Citation
  • Harris, D., E. Foufoula-Georgiou, K. K. Droegemeier, and J. J. Levit, 2001: Multiscale statistical properties of a high-resolution precipitation forecast. J. Hydrometeor., 2 , 406418.

    • Search Google Scholar
    • Export Citation
  • Hoffman, R. N., Z. Liu, J-F. Louis, and C. Grassotti, 1995: Distortion representation of forecast errors. Mon. Wea. Rev., 123 , 27582770.

    • Search Google Scholar
    • Export Citation
  • Johnson, M. E., L. M. Moore, and D. Ylvisaker, 1990: Minimax and maximin distance designs. J. Stat. Plann. Inference, 5 , 26. 131148.

  • Lin, Y., and K. E. Mitchell, 2005: The NCEP Stage II/IV hourly precipitation analyses: Development and applications. Preprints, 19th Conf on Hydrology, San Diego, CA, Amer. Meteor. Soc., 1.2 [Available online at http://ams.confex.com/ams/Annual2005/techprogram/paper_83847.htm.].

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

  • Marzban, C., and S. Sandgathe, 2008: Cluster analysis for object-oriented verification of fields: A variation. Mon. Wea. Rev., 136 , 10131025.

    • Search Google Scholar
    • Export Citation
  • Micheas, A. C., N. I. Fox, S. A. Lack, and C. K. Wikle, 2007: Cell identification and verification of QPF ensembles using shape analysis techniques. J. Hydrol., 343 , 105116.

    • Search Google Scholar
    • Export Citation
  • Nychka, D., and N. Saltzman, 1998: Design of air quality monitoring networks. Lecture Notes in Statistics: Case Studies in Environmental Statistics, D. Nychka, W. Piegorsch, and L. Cox, Eds., Springer, 51–76.

    • Search Google Scholar
    • Export Citation
  • Rosenfeld, A., and J. L. Pfalz, 1966: Sequential operations in digital picture processing. J. Assoc. Comput. Machinery, 13 , 4. 471494.

    • Search Google Scholar
    • Export Citation
  • Rosenfeld, A., and J. L. Pfalz, 1968: Distance functions on digital pictures. Pattern Recognit., 5 , 3361.

  • 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, 100 pp. [Available online at http://www.mmm.ucar.edu/wrf/users/docs/arw_v2.pdf.].

  • 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
  • Wicker, L. J., and W. C. Skamarock, 2002: Time splitting methods for elastic models using forward time schemes. Mon. Wea. Rev., 130 , 20882097.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 80 47 4
PDF Downloads 67 42 1

Computationally Efficient Spatial Forecast Verification Using Baddeley’s Delta Image Metric

View More View Less
  • 1 Research Applications Laboratory, National Center for Atmospheric Research, Boulder, Colorado
  • | 2 Department of Statistics, The Chinese University of Hong Kong, Hong Kong, China, and Department of Statistics, Colorado State University, Fort Collins, Colorado
  • | 3 Research Applications Laboratory, National Center for Atmospheric Research, Boulder, Colorado
Restricted access

Abstract

An important focus of research in the forecast verification community is the development of alternative verification approaches for quantitative precipitation forecasts, as well as for other spatial forecasts. The need for information that is meaningful in an operational context and the importance of capturing the specific sources of forecast error at varying spatial scales are two primary motivating factors. In this paper, features of precipitation as identified by a convolution threshold technique are merged within fields and matched across fields in an automatic and computationally efficient manner using Baddeley’s metric for binary images.

The method is carried out on 100 test cases, and 4 representative cases are shown in detail. Results of merging and matching objects are generally positive in that they are consistent with how a subjective observer might merge and match features. The results further suggest that the Baddeley metric may be useful as a computationally efficient summary metric giving information about location, shape, and size differences of individual features, which could be employed for other spatial forecast verification methods.

Corresponding author address: Eric Gilleland, Research Applications Laboratory, National Center for Atmospheric Research, 3450 Mitchell Lane, Boulder, CO 80301. Email: ericg@ucar.edu

Abstract

An important focus of research in the forecast verification community is the development of alternative verification approaches for quantitative precipitation forecasts, as well as for other spatial forecasts. The need for information that is meaningful in an operational context and the importance of capturing the specific sources of forecast error at varying spatial scales are two primary motivating factors. In this paper, features of precipitation as identified by a convolution threshold technique are merged within fields and matched across fields in an automatic and computationally efficient manner using Baddeley’s metric for binary images.

The method is carried out on 100 test cases, and 4 representative cases are shown in detail. Results of merging and matching objects are generally positive in that they are consistent with how a subjective observer might merge and match features. The results further suggest that the Baddeley metric may be useful as a computationally efficient summary metric giving information about location, shape, and size differences of individual features, which could be employed for other spatial forecast verification methods.

Corresponding author address: Eric Gilleland, Research Applications Laboratory, National Center for Atmospheric Research, 3450 Mitchell Lane, Boulder, CO 80301. Email: ericg@ucar.edu

Save