• Andrieu, H., and Creutin J. D. , 1995: Identification of vertical profiles of radar reflectivity for hydrological applications using an inverse method. Part I: Formulation. J. Appl. Meteor, 34 , 225239.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Andrieu, H., Delrieu G. , and Creutin J. D. , 1995: Identification of vertical profiles of radar reflectivity for hydrological applications using an inverse method. Part II: Sensitivity analysis and case study. J. Appl. Meteor, 34 , 240259.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Austin, P. M., 1987: Relation between measured radar reflectivity and surface rainfall. Mon. Wea. Rev, 115 , 10531070.

  • Brock, F. V., Crawford K. C. , Elliott R. L. , Cuperus G. W. , Stadler S. J. , Johnson H. L. , and Eilts M. D. , 1995: The Oklahoma Mesonet: A technical overview. J. Atmos. Oceanic Technol., 12, 5–19.

    • Search Google Scholar
    • Export Citation
  • Ciach, J. G., and Krajewski W. F. , 1999: On the estimation of radar rainfall error variance. Adv. Water Resour, 22 , 585595.

  • French, M. N., and Krajewski W. F. , 1994: A model for real-time quantitative rainfall forecasting using remote sensing. 1. Formulation. Water Resour. Res, 30 , 10751083.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fulton, R. A., Breidenbach J. P. , Seo D-J. , Miller D. A. , and O'Bannon T. , 1998: The WSR-88D rainfall algorithm. Wea. Forecasting,, 13 , 377395.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grecu, M., and Krajewski W. F. , 1999: Anomalous pattern detection in radar echoes by using neural networks. IEEE Trans. Geosci. Remote Sens, 37 , 287296.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Houze, R. A,Jr, 1993: Cloud Dynamics. Academic Press, 573 pp.

  • Houze, R. A,Jr, Smull B. F. , and Dodge P. , 1990: Mesoscale organization of springtime rainstorms in Oklahoma. Mon. Wea. Rev, 118 , 613654.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Joss, J., and Lee R. , 1995: The application of radar–gauge comparisons to operational precipitation profile corrections. J. Appl. Meteor, 34 , 26122630.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kitchen, M., Brown R. , and Davies A. G. , 1994: Real-time correction of weather radar data for the effects of bright band, range and orographic growth in widespread precipitation. Quart. J. Roy. Meteor. Soc, 120 , 12311254.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klazura, G. E., and Imy D. A. , 1993: A description of the initial set of analysis products available from the NEXRAD WSR-88D system. Bull. Amer. Meteor. Soc, 74 , 12931311.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krajewski, W. F., and Vignal B. , 2001: Evaluation of anomalous propagation echo detection in WSR-88D data: A large sample case study. J. Atmos. Oceanic Technol, 18 , 807814.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kruger, A., and Krajewski W. F. , 1997: Efficient storage of weather radar data. Software Pract. Exper, 27 , 623635.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Menke, W., 1989: Geophysical Data Analysis: Discrete Inverse Theory. Academic Press, 260 pp.

  • Seo, D-J., Breidenbach J. , Fulton R. , Miller D. , and O'Bannon T. , 2000: Real-time adjustment of range-dependent biases in WSR-88D rainfall estimates due to nonuniform vertical profile of reflectivity. J. Hydrometeor, 1 , 222240.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shafer, M. A., Fiebrich C. A. , Arndt D. S. , Fredrickson S. E. , and Hughes T. W. , 2000: Quality assurance procedures in the Oklahoma Mesonetwork. J. Atmos. Oceanic Technol, 17 , 474494.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, J. A., Seo D-J. , Baeck M. L. , and Hudlow M. D. , 1996: An intercomparison study of NEXRAD precipitation estimates. Water Resour. Res, 32 , 20352045.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smyth, T. J., and Illingworth A. J. , 1998: Radar estimates of rainfall rates at the ground in bright band and non-bright band events. Quart. J. Roy. Meteor. Soc, 124 , 24172434.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Steiner, M., Houze Jr. R. A. , and Yuter S. E. , 1995: Climatological characterization of three-dimensional storm structure from operational radar and rain gauge data. J. Appl. Meteor, 34 , 19782007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tarantola, A., and Valette B. , 1982a: Generalized nonlinear inverse problem solved using the least squares criterion. Rev. Geophys. Space Phys, 20 , 219232.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tarantola, A., and Valette B. , 1982b: Inverse problems: Quest for information. J. Geophys. Space Phys, 50 , 159170.

  • Vignal, B., Andrieu H. , and Creutin J. D. , 1999: Identification of vertical profiles of reflectivity from volume-scan radar data. J. Appl. Meteor, 38 , 12141228.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vignal, B., Galli G. , Joss J. , and Germann U. , 2000: Three methods to determine profiles of reflectivity from volumetric radar data to correct precipitation estimates. J. Appl. Meteor, 39 , 17151726.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Young, C. B., Bradley A. A. , Krajewski W. F. , Kruger A. , and Morrissey M. L. , 2000: Evaluating NEXRAD multisensor precipitation estimates for operational hydrologic forecasting. J. Hydrometeor, 1 , 241254.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zawadzki, I., 1982: The quantitative interpretation of weather radar measurements. Atmos.–Ocean, 20 , 158180.

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Large-Sample Evaluation of Two Methods to Correct Range-Dependent Error for WSR-88D Rainfall Estimates

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  • 1 Iowa Institute of Hydraulic Research, The University of Iowa, Iowa City, Iowa
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Abstract

The vertical variability of reflectivity is an important source of error that affects estimations of rainfall quantity by radar. This error can be reduced if the vertical profile of reflectivity (VPR) is known. Different methods are available to determine VPR based on volume-scan radar data. Two such methods were tested. The first, used in the Swiss Meteorological Service, estimates a mean VPR directly from volumetric radar data collected close to the radar. The second method takes into account the spatial variability of reflectivity and relies on solving an inverse problem in determination of the local profile. To test these methods, two years of archived level-II radar data from the Weather Surveillance Radar-1988 Doppler (WSR-88D) located in Tulsa, Oklahoma, and the corresponding rain gauge observations from the Oklahoma Mesonet were used. The results, obtained by comparing rain estimates from radar data corrected for the VPR influence with rain gauge observations, show the benefits of the methods—and also their limitations. The performance of the two methods is similar, but the inverse method consistently provides better results. However, for use in operational environments, it would require substantially more computational resources than the first method.

Corresponding author address: Dr. Witold F. Krajewski, Iowa Institute of Hydraulic Research, 300 South Riverside Drive, Rm. 404, Iowa City, IA 52242-1585. Email: witold-krajewski@uiowa.edu

Abstract

The vertical variability of reflectivity is an important source of error that affects estimations of rainfall quantity by radar. This error can be reduced if the vertical profile of reflectivity (VPR) is known. Different methods are available to determine VPR based on volume-scan radar data. Two such methods were tested. The first, used in the Swiss Meteorological Service, estimates a mean VPR directly from volumetric radar data collected close to the radar. The second method takes into account the spatial variability of reflectivity and relies on solving an inverse problem in determination of the local profile. To test these methods, two years of archived level-II radar data from the Weather Surveillance Radar-1988 Doppler (WSR-88D) located in Tulsa, Oklahoma, and the corresponding rain gauge observations from the Oklahoma Mesonet were used. The results, obtained by comparing rain estimates from radar data corrected for the VPR influence with rain gauge observations, show the benefits of the methods—and also their limitations. The performance of the two methods is similar, but the inverse method consistently provides better results. However, for use in operational environments, it would require substantially more computational resources than the first method.

Corresponding author address: Dr. Witold F. Krajewski, Iowa Institute of Hydraulic Research, 300 South Riverside Drive, Rm. 404, Iowa City, IA 52242-1585. Email: witold-krajewski@uiowa.edu

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