Editorial Type:
Article
Article Type:
Research Article

Mass-Conserving Remapping of Radar Data onto Two-Dimensional Cartesian Coordinates for Hydrologic Applications

Hatim O. Sharif Department of Civil and Environmental Engineering, University of Texas at San Antonio, San Antonio, Texas

Search for other papers by Hatim O. Sharif in
Current site
Google Scholar
PubMed Close
and
Fred L. Ogden Department of Civil and Architectural Engineering, University of Wyoming, Laramie, Wyoming

Search for other papers by Fred L. Ogden in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Dec 2014
DOI:
https://doi.org/10.1175/JHM-D-14-0058.1
Page(s):
2190–2202
Restricted access

Abstract

Recent upgrades to operational radar-rainfall products in terms of quality and resolution call for reexamination of the factors that contribute to the uncertainty of radar-rainfall estimation. Remapping or regridding of radar observations onto Cartesian coordinates is implemented by practitioners when radar estimates are compared against rain gauge observations, in hydrologic applications, or for merging data from different radars. However, assuming perfect radar observations, many of the widely used remapping methodologies do not conserve mass for the rainfall rate field. The most popular remapping approaches used are those based on extracting information from radar bins whose centers fall within a certain distance from the center of the Cartesian grid. This paper develops a mass-conserving method for remapping, which is called “precise remapping,” which is compared against two other commonly used remapping methods. Results show that the choice of the remapping method can make a substantial difference in grid-averaged rainfall accumulations (up to more than 100%). Differences were quantified using observations from two radars, collected during a field experiment. The interpolation grid resolution was also found to affect interpolated rainfall estimates. Approximate remapping methods tend to be much more sensitive to the interpolation grid resolution than precise remapping. High-resolution radar data such as those from radars with short gate spacing or narrow beams, or the super-resolution Weather Surveillance Radar-1988 Doppler (WSR-88D) sampling format, are significantly more sensitive (by up to 100%) to the remapping method and the interpolation grid resolution than the legacy WSR-88D rainfall data resolution of 1° × 1 km.

Corresponding author address: Hatim Sharif, Department of Civil and Environmental Engineering, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 78249-0668. E-mail: hatim.sharif@utsa.edu

Abstract

Recent upgrades to operational radar-rainfall products in terms of quality and resolution call for reexamination of the factors that contribute to the uncertainty of radar-rainfall estimation. Remapping or regridding of radar observations onto Cartesian coordinates is implemented by practitioners when radar estimates are compared against rain gauge observations, in hydrologic applications, or for merging data from different radars. However, assuming perfect radar observations, many of the widely used remapping methodologies do not conserve mass for the rainfall rate field. The most popular remapping approaches used are those based on extracting information from radar bins whose centers fall within a certain distance from the center of the Cartesian grid. This paper develops a mass-conserving method for remapping, which is called “precise remapping,” which is compared against two other commonly used remapping methods. Results show that the choice of the remapping method can make a substantial difference in grid-averaged rainfall accumulations (up to more than 100%). Differences were quantified using observations from two radars, collected during a field experiment. The interpolation grid resolution was also found to affect interpolated rainfall estimates. Approximate remapping methods tend to be much more sensitive to the interpolation grid resolution than precise remapping. High-resolution radar data such as those from radars with short gate spacing or narrow beams, or the super-resolution Weather Surveillance Radar-1988 Doppler (WSR-88D) sampling format, are significantly more sensitive (by up to 100%) to the remapping method and the interpolation grid resolution than the legacy WSR-88D rainfall data resolution of 1° × 1 km.

Corresponding author address: Hatim Sharif, Department of Civil and Environmental Engineering, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 78249-0668. E-mail: hatim.sharif@utsa.edu
Save
Cover Journal of Hydrometeorology
Journal of Hydrometeorology

  • Bousquet, O., and Chong M. , 1998: A Multiple Doppler and Continuity Adjustment Technique (MUSCAT) to recover wind components from Doppler radar measurements. J. Atmos. Oceanic Technol., 15, 343–359, doi:10.1175/1520-0426(1998)015<0343:AMDSAC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Brandes, E. A., Vivekanandan J. , and Wilson J. W. , 1999: A comparison of radar reflectivity estimates of rainfall from collocated radars. J. Atmos. Oceanic Technol., 16, 1264–1272, doi:10.1175/1520-0426(1999)016<1264:ACORRE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Dixon, M., and Wiener G. , 1993: TITAN: Thunderstorm Identification, Tracking, Analysis and Nowcasting—A radar-based methodology. J. Atmos. Oceanic Technol., 10, 785–797, doi:10.1175/1520-0426(1993)010<0785:TTITAA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Fulton, R. A., 1998: WSR-88D polar to HRAP mapping. Tech. Memo., Hydrology Research Laboratory, Office of Hydrology, National Weather Service, Silver Spring, MD, 33 pp.

  • Fulton, R. A., 1999: Sensitivity of WSR-88D rainfall estimates to the rain-rate threshold and rain gauge adjustment: A flash flood case study. Wea. Forecasting, 14, 604–624, doi:10.1175/1520-0434(1999)014<0604:SOWRET>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Greene, D. R., and Hudlow M. D. , 1982: Hydrometeorological grid mapping procedures. Proc. Int. Symp. on Hydrometeorology, Denver, CO, American Water Resources Association, 147–156.

  • Harrison, D. L., Scovell R. W. , and Kitchen M. , 2009: High-resolution precipitation estimates for hydrological uses. Proc. Inst. Civil Eng. Water Manage.,162, 125–135.

  • Henja, A., and Michelson D. B. , 1999: Improved polar to Cartesian radar data transformation. Preprints, 29th Int. Conf. on Radar Meteorology, Montreal, QC, Canada, Amer. Meteor. Soc., 252–255.

  • Jorgensen, D. P., Hildebrand P. H. , and Frush C. L. , 1983: Feasibility test of an airborne pulse-Doppler meteorological radar. J. Climate Appl. Meteor., 22, 744–757, doi:10.1175/1520-0450(1983)022<0744:FTOAAP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Julier, S., and Uhlmann J. , 1997: Consistent debiased method for converting between polar and Cartesian coordinate systems. Proceedings of the 1997 SPIE Conference on Acquisition, Tracking, and Pointing, M. K. Masten and L. A. Stockum, Eds., International Society for Optical Engineering (SPIE Proceedings, Vol. 3086), doi:10.1117/12.277178.

  • Kumjian, M. R., Ryzhkov A. V. , Melnikov V. M. , and Schuur T. J. , 2010: Rapid-scan super-resolution observations of a cyclic supercell with a dual-polarization WSR-88D. Mon. Wea. Rev., 138, 3762–3786, doi:10.1175/2010MWR3322.1.

    • Search Google Scholar
    • Export Citation

  • Mittermaier, M. P., and Terblanche D. E. , 1997: Converting weather radar data to Cartesian space: A new approach using DISPLACE averaging. Water S.A., 23, 46–50.

    • Search Google Scholar
    • Export Citation

  • Mittermaier, M. P., Illingworth A. J. , and Hogan R. J. , 2006: Possible benefits of oversampling on operational weather radar data quality. Atmos. Sci. Lett., 7, 9–14, doi:10.1002/asl.122.

    • Search Google Scholar
    • Export Citation

  • Morin, E., Krajewski W. F. , Goodrich D. C. , Gao X. , and Sorooshian S. , 2003: Estimating rainfall intensities from weather radar data: The scale-dependency problem. J. Hydrometeor., 4, 782–797, doi:10.1175/1525-7541(2003)004<0782:ERIFWR>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Randeu, W. L., and Schonhuber M. , 2000: Rainfall overestimates induced by radar area averaging. Phys. Chem. Earth, Part B: Hydrol. Oceans Atmos., 25, 965–969, doi:10.1016/S1464-1909(00)00134-9.

    • Search Google Scholar
    • Export Citation

  • Sharif, H. O., Ogden F. L. , Krajewski W. F. , and Xue M. , 2002: Numerical simulations of radar rainfall error propagation. Water Resour. Res., 38, doi:10.1029/2001WR000525.

    • Search Google Scholar
    • Export Citation

  • Stumpf, G. J., Smith T. M. , and Gerard A. E. , 2002: The multiple radar severe storm analysis program (MR-SSAP) for WDSS-II. 21st Conf. on Severe Local Storms, San Antonio, TX, Amer. Meteor. Soc., 138–141.

  • Terblanche, D. E., 1996: A simple digital signal processing method to simulate linear and quadratic responses from a radar’s logarithmic receiver. J. Atmos. Oceanic Technol., 13, 533–538, doi:10.1175/1520-0426(1996)013<0533:ASDSPM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Terblanche, D. E., Pegram G. G. S. , and Mittermaier M. P. , 2001: The development of weather radar as a research and operational tool for hydrology in South Africa. J. Hydrol., 241, 3–25, doi:10.1016/S0022-1694(00)00372-3.

    • Search Google Scholar
    • Export Citation

  • Trapp, R. J., and Doswell C. A. III, 2000: Radar data objective analysis. J. Atmos. Oceanic Technol., 17, 105–120, doi:10.1175/1520-0426(2000)017<0105:RDOA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Vivoni, E. R., Entekhabi D. , and Hoffman R. N. , 2007: Error propagation of radar rainfall nowcasting fields through a fully-distributed flood forecasting model. J. Appl. Meteor. Climatol., 46, 932–940, doi:10.1175/JAM2506.1.

    • Search Google Scholar
    • Export Citation

  • Weygandt, S. S., Shapiro A. , and Droegemeier K. K. , 2002: Retrieval of model initial fields from single-Doppler observations of a supercell thunderstorm. Part I: Single-Doppler velocity retrieval. Mon. Wea. Rev., 130, 433–453, doi:10.1175/1520-0493(2002)130<0433:ROMIFF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Wurman, J., and Gill S. , 2000: Finescale radar observations of the Dimmitt, Texas (2 June 1995), tornado. Mon. Wea. Rev., 128, 2135–2164, doi:10.1175/1520-0493(2000)128<2135:FROOTD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Zhang, J., Howard K. , and Gourley J. J. , 2005: Constructing three-dimensional multiple radar reflectivity mosaics: Examples of convective storms and stratiform rain echoes. J. Atmos. Oceanic Technol., 22, 30–42, doi:10.1175/JTECH-1689.1.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 933 396 42
PDF Downloads 354 74 2