A Method to Merge WSR-88D Data with ARM SGP Millimeter Cloud Radar Data by Studying Deep Convective Systems

Zhe Feng Department of Atmospheric Sciences, University of North Dakota, Grand Forks, North Dakota

Search for other papers by Zhe Feng in
Current site
Google Scholar
PubMed
Close
,
Xiquan Dong Department of Atmospheric Sciences, University of North Dakota, Grand Forks, North Dakota

Search for other papers by Xiquan Dong in
Current site
Google Scholar
PubMed
Close
, and
Baike Xi Department of Atmospheric Sciences, University of North Dakota, Grand Forks, North Dakota

Search for other papers by Baike Xi in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

A decade of collocated Atmospheric Radiation Measurement Program (ARM) 35-GHz Millimeter Cloud Radar (MMCR) and Weather Surveillance Radar-1988 Doppler (WSR-88D) data over the ARM Southern Great Plains (SGP) site have been collected during the period of 1997–2006. A total of 28 winter and 45 summer deep convective system (DCS) cases over the ARM SGP site have been selected for this study during the 10-yr period. For the winter cases, the MMCR reflectivity, on average, is only 0.2 dB lower than that of the WSR-88D, with a correlation coefficient of 0.85. This result indicates that the MMCR signals have not been attenuated for ice-phase convective clouds, and the MMCR reflectivity measurements agree well with the WSR-88D, regardless of their vastly different characteristics. For the summer nonprecipitating convective clouds, however, the MMCR reflectivity, on average, is 10.6 dB lower than the WSR-88D measurement, and the average differences between the two radar reflectivities are nearly constant with height above cloud base. Three lookup tables with Mie calculations have been generated for correcting the MMCR signal attenuation. After applying attenuation correction for the MMCR reflectivity measurements, the averaged difference between the two radars has been reduced to 9.1 dB. Within the common sensitivity range (−10 to 20 dBZ), the mean differences for the uncorrected and corrected MMCR reflectivities have been reduced to 6.2 and 5.3 dB, respectively. The corrected MMCR reflectivities were then merged with the WSR-88D data to fill in the gaps during the heavy precipitation periods. This merged dataset provides a more complete radar reflectivity profile for studying convective systems associated with heavier precipitation than the original MMCR dataset. It also provides the intensity, duration, and frequency of the convective systems as they propagate over the ARM SGP for climate modelers. Eventually, it will be possible to improve understanding of the cloud-precipitation processes, and evaluate GCM predictions using the long-term merged dataset, which could not have been done with either the MMCR or the WSR-88D dataset alone.

Corresponding author address: Mr. Zhe Feng, Dept. of Atmospheric Sciences, University of North Dakota, 4149 University Dr., Stop 9006, Grand Forks, ND 58202-9006. Email: zhe.feng@und.nodak.edu

Abstract

A decade of collocated Atmospheric Radiation Measurement Program (ARM) 35-GHz Millimeter Cloud Radar (MMCR) and Weather Surveillance Radar-1988 Doppler (WSR-88D) data over the ARM Southern Great Plains (SGP) site have been collected during the period of 1997–2006. A total of 28 winter and 45 summer deep convective system (DCS) cases over the ARM SGP site have been selected for this study during the 10-yr period. For the winter cases, the MMCR reflectivity, on average, is only 0.2 dB lower than that of the WSR-88D, with a correlation coefficient of 0.85. This result indicates that the MMCR signals have not been attenuated for ice-phase convective clouds, and the MMCR reflectivity measurements agree well with the WSR-88D, regardless of their vastly different characteristics. For the summer nonprecipitating convective clouds, however, the MMCR reflectivity, on average, is 10.6 dB lower than the WSR-88D measurement, and the average differences between the two radar reflectivities are nearly constant with height above cloud base. Three lookup tables with Mie calculations have been generated for correcting the MMCR signal attenuation. After applying attenuation correction for the MMCR reflectivity measurements, the averaged difference between the two radars has been reduced to 9.1 dB. Within the common sensitivity range (−10 to 20 dBZ), the mean differences for the uncorrected and corrected MMCR reflectivities have been reduced to 6.2 and 5.3 dB, respectively. The corrected MMCR reflectivities were then merged with the WSR-88D data to fill in the gaps during the heavy precipitation periods. This merged dataset provides a more complete radar reflectivity profile for studying convective systems associated with heavier precipitation than the original MMCR dataset. It also provides the intensity, duration, and frequency of the convective systems as they propagate over the ARM SGP for climate modelers. Eventually, it will be possible to improve understanding of the cloud-precipitation processes, and evaluate GCM predictions using the long-term merged dataset, which could not have been done with either the MMCR or the WSR-88D dataset alone.

Corresponding author address: Mr. Zhe Feng, Dept. of Atmospheric Sciences, University of North Dakota, 4149 University Dr., Stop 9006, Grand Forks, ND 58202-9006. Email: zhe.feng@und.nodak.edu

Save
  • Ackerman, T. P., and Stokes G. M. , 2003: The Atmospheric Radiation Measurement Program. Phys. Today, 56 , 3844.

  • Bohren, C. F., and Huffman D. R. , 1983: Absorption and Scattering of Light by Small Particles. Wiley, 530 pp.

  • Bringi, V. N., and Chandrasekar V. , 2001: Polarimetric Doppler Weather Radar: Principles and Applications. Cambridge University Press, 636 pp.

    • Search Google Scholar
    • Export Citation
  • Brown, R. A., Wood V. T. , and Sirmans D. , 2000: Improved WSR-88D scanning strategies for convective storms. Wea. Forecasting, 15 , 208220.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cess, R. D., and Coauthors, 1996: Cloud feedback in atmospheric general circulation models: An update. J. Geophys. Res., 101 , 1279112794.

  • Chandrasekar, V., and Bringi V. , 1987: Simulation of radar reflectivity and surface measurements of rainfall. J. Atmos. Oceanic Technol., 4 , 464478.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clothiaux, E. E., and Coauthors, 1999: The Atmospheric Radiation Measurement Program cloud radars: Operational modes. J. Atmos. Oceanic Technol., 16 , 819827.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clothiaux, E. E., Ackerman T. P. , Mace G. G. , Moran K. P. , Marchand R. T. , Miller M. A. , and Martner B. E. , 2000: Objective determination of cloud heights and radar reflectivities using a combination of active remote sensors at the ARM CART sites. J. Appl. Meteor., 39 , 645665.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Crum, T. D., Alberty R. L. , and Burgess D. W. , 1993: Recording, archiving, and using WSR-88D Data. Bull. Amer. Meteor. Soc., 74 , 645653.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Del Genio, A. D., Kovari W. , Yao M. S. , and Jonas J. , 2005: Cumulus microphysics and climate sensitivity. J. Climate, 18 , 23762387.

  • Dong, X., and Mace G. G. , 2003: Profiles of low-level stratus cloud microphysics deduced from ground-based measurements. J. Atmos. Oceanic Technol., 20 , 4253.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dong, X., Minnis P. , Ackerman T. P. , Clothiaux E. E. , Mace G. G. , Long C. N. , and Liljegren J. C. , 2000: A 25-month database of stratus cloud properties generated from ground-based measurements at the ARM SGP site. J. Geophys. Res., 105 , 45294538.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dong, X., Minnis P. , and Xi B. , 2005: A climatology of midlatitude continental clouds from the ARM SGP central facility. Part I: Low-level cloud macrophysical, microphysical, and radiative properties. J. Climate, 18 , 13911410.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dong, X., Xi B. , and Minnis P. , 2006: A climatology of midlatitude continental clouds from the ARM SGP central facility. Part II: Cloud fraction and surface radiative forcing. J. Climate, 19 , 17651783.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dong, X., and Coauthors, 2008: Using observations of deep convective systems to constrain atmospheric column absorption in the optically thick limit. J. Geophys. Res., 113 , D10206. doi:10.1029/2007JD009769.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Heymsfield, A. J., and Hjelmfelt M. R. , 1984: Processes of hydrometeor development in Oklahoma convective clouds. J. Atmos. Sci., 41 , 28112835.

    • 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
  • Kollias, P., Clothiaux E. E. , Miller M. A. , Albrecht B. A. , Stephens G. L. , and Ackerman T. P. , 2007: Millimeter-wavelength radars: New frontier in atmospheric cloud and precipitation research. Bull. Amer. Meteor. Soc., 88 , 16081624.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lhermitte, R., 1990: Attenuation and scattering of millimeter wavelength radiation by clouds and precipitation. J. Atmos. Oceanic Technol., 7 , 464479.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liljegren, J., Clothiaux E. , Mace G. , Kato S. , and Dong X. , 2001: A new retrieval for cloud liquid water path using a ground-based microwave radiometer and measurements of cloud temperature. J. Geophys. Res., 106 , 1448514500.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, B., Wielicki B. A. , Minnis P. , Chambers L. , Xu K-M. , Hu Y. , and Fan A. , 2006: The effect of environmental conditions on tropical deep convective systems observed from the TRMM satellite. J. Climate, 19 , 57455761.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mace, G. G., and Benson-Troth S. , 2002: Cloud-layer overlap characteristics derived from long-term cloud radar data. J. Climate, 15 , 25052515.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mace, G. G., and Coauthors, 2006: Cloud radiative forcing at the atmospheric radiation measurement program climate research facility: 1. Technique, validation, and comparison to satellite-derived diagnostic quantities. J. Geophys. Res., 111 , D11S90. doi:10.1029/2005JD005921.

    • Search Google Scholar
    • Export Citation
  • Marshall, J., and Palmer W. M. , 1948: The distribution of raindrops with size. J. Meteor., 5 , 165166.

  • Matrosov, S. Y., 2008: Assessment of radar signal attenuation caused by the melting hydrometeor layer. IEEE Trans. Geosci. Remote Sens., 46 , 10391047.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Matrosov, S. Y., May P. T. , and Shupe M. D. , 2006: Rainfall profiling using Atmospheric Radiation Measurement Program vertically pointing 8-mm wavelength radars. J. Atmos. Oceanic Technol., 23 , 14781491.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Miller, M. A., Verlinde J. , Gilbert C. V. , Lehenbauer G. J. , Tongue J. S. , and Clothiaux E. E. , 1998: Detection of nonprecipitating clouds with the WSR-88D: A theoretical and experimental survey of capabilities and limitations. Wea. Forecasting, 13 , 10461062.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Moran, K. P., Martner B. E. , Post M. J. , Kropfli R. A. , Welsh D. C. , and Widener K. B. , 1998: An unattended cloud-profiling radar for use in climate research. Bull. Amer. Meteor. Soc., 79 , 443455.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Randall, D. A., Schlesinger M. E. , Galin V. , Meleshko V. , Morcrette J. J. , and Wetherald R. , 2006: Cloud feedbacks. Frontiers in the Science of Climate Modelling: Proceedings of a Symposium in Honor of Professor Robert D. Cess, J. T. Kiehl and V. Ramanathan, Eds., Cambridge University Press, 217–250.

    • Search Google Scholar
    • Export Citation
  • Rinehart, R. E., 2004: Radar for Meteorologists. 4th ed. Rinehart Publishing, 482 pp.

  • Sekelsky, S. M., 2002: Near-field reflectivity and antenna boresight gain corrections for millimeter-wave atmospheric radars. J. Atmos. Oceanic Technol., 19 , 468477.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Straka, J. M., Zrnić D. S. , and Ryzhkov A. V. , 2000: Bulk hydrometeor classification and quantification using polarimetric radar data: Synthesis of relations. J. Appl. Meteor., 39 , 13411372.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ulbrich, C. W., 1983: Natural variations in the analytical form of the raindrop size distribution. J. Appl. Meteor., 22 , 17641775.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wielicki, B. A., Barkstrom B. R. , Harrison E. F. , Lee R. B. , Louis Smith G. , and Cooper J. E. , 1996: Clouds and the Earth’s Radiant Energy System (CERES): An Earth observing system experiment. Bull. Amer. Meteor. Soc., 77 , 853868.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xu, K-M., and Krueger S. K. , 1991: Evaluation of cloudiness parameterizations using a cumulus ensemble model. Mon. Wea. Rev., 119 , 342367.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 259 68 6
PDF Downloads 143 50 6