• Accadia, C., and Coauthors. 2003: Application of a statistical methodology for limited area model intercomparison using a bootstrap technique. Il Nuovo Cimento, 26C , 6177.

    • Search Google Scholar
    • Export Citation
  • Baldwin, M. E., cited 2000: QPF verification system documentation. [Available online at http://sgi62.wwb.noaa.gov:8080/testmb/verfsp.doc.html.].

    • Search Google Scholar
    • Export Citation
  • Barnes, S. L., 1964: A technique for maximizing details in numerical weather map analysis. J. Appl. Meteor., 3 , 396409.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barnes, S. L., 1973: Mesoscale objective analysis using weighted time-series observations. NOAA Tech. Memo. ERL NSSL-62, National Severe Storm Laboratory, Norman, OK, 60 pp. [NTIS COM-73-10781.].

    • Search Google Scholar
    • Export Citation
  • Buzzi, A., , Fantini M. , , Malguzzi P. , , and Nerozzi F. , 1994: Validation of a limited area model in cases of Mediterranean cyclogenesis: Surface fields and precipitation scores. Meteor. Atmos. Phys., 53 , 5367.

    • Search Google Scholar
    • Export Citation
  • Buzzi, A., , Tartaglione N. , , and Malguzzi P. , 1998: Numerical simulation of the 1994 Piedmont flood: Role of orography and moist processes. Mon. Wea. Rev., 126 , 23692383.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Diaconis, P., , and Efron B. , 1983: Computer-intensive methods in statistics. Sci. Amer., 248 , 116130.

  • Donaldson, B. J., , Dyer R. , , and Kraus R. , 1975: An objective evaluator of techniques for predicting severe weather events. Preprints, Ninth Conf. on Severe Local Storms, Norman, OK, Amer. Meteor. Soc., 321–326.

    • Search Google Scholar
    • Export Citation
  • ECMWF, cited 2001: Grid point to grid point interpolation. [Available online at http://www.ecmwf.int/publications/manuals/libraries/interpolation/gridToGridFIS.html.].

    • Search Google Scholar
    • Export Citation
  • Flueck, J. A., 1987: A study of some measures of forecast verification. Preprints, 10th Conf. on Probability and Statistics in Atmospheric Sciences, Edmonton, AB, Canada, Amer. Meteor. Soc., 69–73.

    • Search Google Scholar
    • Export Citation
  • Georgelin, M., and Coauthors. 2000: The second COMPARE exercise: A model intercomparison using a case of a typical mesoscale orographic flow, the PYREX IOP3. Quart. J. Roy. Meteor. Soc., 126 , 9911030.

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

  • Hanssen, A. W., , and Kuipers W. J. A. , 1965: On the relationship between the frequency of rain and various meteorological parameters. Meded. Verh., 81 , 215.

    • Search Google Scholar
    • Export Citation
  • Koch, S. E., , desJardins M. , , and Kocin P. J. , 1983: An interactive Barnes objective map analysis scheme for use with satellite and conventional data. J. Climate Appl. Meteor., 22 , 14871503.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kuo, H. L., 1974: Further studies of the parameterization of the influence of cumulus convection on large scale flow. J. Atmos. Sci., 31 , 12321240.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mason, I., 1989: Dependence of the critical success index on sample climate and threshold probability. Aust. Meteor. Mag., 37 , 7581.

  • McBride, J. L., , and Ebert E. E. , 2000: Verification of quantitative precipitation forecasts from operational numerical weather prediction models over Australia. Wea. Forecasting, 15 , 103121.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mesinger, F., 1996: Improvements in quantitative precipitation forecasting with the Eta regional model at the National Centers for Environmental Prediction: The 48-km upgrade. Bull. Amer. Meteor. Soc., 77 , 26372649.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mesinger, F., , Black T. L. , , Plummer D. W. , , and Ward J. H. , 1990: Eta model precipitation forecasts for a period including Tropical Storm Allison. Wea. Forecasting, 5 , 483493.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nagata, M., and Coauthors. 2001: Third COMPARE workshop: A model intercomparison experiment of tropical cyclone intensity and track prediction. Bull. Amer. Meteor. Soc., 82 , 20072020.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Page, J. K., 1986: Prediction of Solar Radiation on Inclined Surfaces. Solar Energy R&D in the European Community: Series F, Vol. 3, Dordrecht Reidel, 459 pp.

    • Search Google Scholar
    • Export Citation
  • Peirce, C. S., 1884: The numerical measure of the success of predictions. Science, 5 , 453454.

  • Schaefer, J. T., 1990: The critical success index as an indicator of warning skill. Wea. Forecasting, 5 , 570575.

  • Stephenson, D. B., 2000: Use of the “odds ratio” for diagnosing forecast skill. Wea. Forecasting, 15 , 221232.

  • Wilks, D. S., 1995: Statistical Methods in the Atmospheric Sciences. Academic Press, 467 pp.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 433 433 34
PDF Downloads 241 241 33

Sensitivity of Precipitation Forecast Skill Scores to Bilinear Interpolation and a Simple Nearest-Neighbor Average Method on High-Resolution Verification Grids

View More View Less
  • 1 Istituto di Scienze dell' Atmosfera e del Clima–Consiglio Nazionale delle Ricerche, Rome, Italy
  • | 2 Dipartimento di Matematica e Informatica, Università di Camerino, Camerino, Italy
© Get Permissions
Restricted access

Abstract

Grid transformations are common postprocessing procedures used in numerical weather prediction to transfer a forecast field from one grid to another. This paper investigates the statistical effects of two different interpolation techniques on widely used precipitation skill scores like the equitable threat score and the Hanssen–Kuipers score. The QUADRICS Bologna Limited Area Model (QBOLAM), which is a parallel version of the Bologna Limited Area Model (BOLAM) described by Buzzi et al., is used, and it is verified on grids of about 10 km (grid-box size). The precipitation analysis is obtained by means of a Barnes objective analysis scheme. The rain gauge data are from the Piedmont and Liguria regions, in northwestern Italy. The data cover 243 days, from 1 October 2000 to 31 May 2001. The interpolation methods considered are bilinear interpolation and a simple nearest-neighbor averaging method, also known as remapping or budget interpolation, which maintains total precipitation to a desired degree of accuracy. A computer-based bootstrap technique is applied to perform hypothesis testing on nonparametric skill scores, in order to assess statistical significance of score differences. Small changes of the precipitation field induced by the two interpolation methods do affect skill scores in a statistically significant way. Bilinear interpolation affects skill scores more heavily, smoothing the maxima, and smearing and increasing the minima of the precipitation field over the grid. The remapping procedure seems to be more appropriate for performing high-resolution grid transformations, although the present work shows that a precipitation edge-smearing effect at lower precipitation thresholds exists. Equitable threat score is more affected than Hanssen–Kuipers score by the interpolation process, since this last score weights all kind of successes (hits and correct no-rain forecasts). Correct no-rain forecasts at higher thresholds often outnumber hits, misses, and false alarms, reducing the sensitivity to false alarm changes introduced by the interpolation process.

Corresponding author address: Mr. Christophe Accadia, Istituto di Scienze dell'Atmosfera e del Clima–CNR, Area di Ricerca di Roma Tor Vergata, Via del Fosso del Cavaliere 100, 00133 Rome, Italy. Email: C.Accadia@isac.cnr.it

Abstract

Grid transformations are common postprocessing procedures used in numerical weather prediction to transfer a forecast field from one grid to another. This paper investigates the statistical effects of two different interpolation techniques on widely used precipitation skill scores like the equitable threat score and the Hanssen–Kuipers score. The QUADRICS Bologna Limited Area Model (QBOLAM), which is a parallel version of the Bologna Limited Area Model (BOLAM) described by Buzzi et al., is used, and it is verified on grids of about 10 km (grid-box size). The precipitation analysis is obtained by means of a Barnes objective analysis scheme. The rain gauge data are from the Piedmont and Liguria regions, in northwestern Italy. The data cover 243 days, from 1 October 2000 to 31 May 2001. The interpolation methods considered are bilinear interpolation and a simple nearest-neighbor averaging method, also known as remapping or budget interpolation, which maintains total precipitation to a desired degree of accuracy. A computer-based bootstrap technique is applied to perform hypothesis testing on nonparametric skill scores, in order to assess statistical significance of score differences. Small changes of the precipitation field induced by the two interpolation methods do affect skill scores in a statistically significant way. Bilinear interpolation affects skill scores more heavily, smoothing the maxima, and smearing and increasing the minima of the precipitation field over the grid. The remapping procedure seems to be more appropriate for performing high-resolution grid transformations, although the present work shows that a precipitation edge-smearing effect at lower precipitation thresholds exists. Equitable threat score is more affected than Hanssen–Kuipers score by the interpolation process, since this last score weights all kind of successes (hits and correct no-rain forecasts). Correct no-rain forecasts at higher thresholds often outnumber hits, misses, and false alarms, reducing the sensitivity to false alarm changes introduced by the interpolation process.

Corresponding author address: Mr. Christophe Accadia, Istituto di Scienze dell'Atmosfera e del Clima–CNR, Area di Ricerca di Roma Tor Vergata, Via del Fosso del Cavaliere 100, 00133 Rome, Italy. Email: C.Accadia@isac.cnr.it

Save