Estimating Raindrop Size Distributions and Vertical Air Motions with Spectral Difference Using Vertically Pointing Radar

Suzhou Pang aCollege of Electronic Engineering, Chengdu University of Information Technology, Chengdu, China
bState Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, China
cKey Laboratory of Atmospheric Sounding, China Meteorological Administration, Chengdu, China

Search for other papers by Suzhou Pang in
Current site
Google Scholar
PubMed
Close
,
Zheng Ruan bState Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, China

Search for other papers by Zheng Ruan in
Current site
Google Scholar
PubMed
Close
,
Ling Yang aCollege of Electronic Engineering, Chengdu University of Information Technology, Chengdu, China
cKey Laboratory of Atmospheric Sounding, China Meteorological Administration, Chengdu, China

Search for other papers by Ling Yang in
Current site
Google Scholar
PubMed
Close
,
Xiantong Liu dInstitute of Tropical and Marine Meteorology, China Meteorological Administration, Guangzhou, China

Search for other papers by Xiantong Liu in
Current site
Google Scholar
PubMed
Close
,
Zhaoyang Huo eNanjing University of Information Science and Technology, Academy of Atmospheric Physics, Nanjing, China

Search for other papers by Zhaoyang Huo in
Current site
Google Scholar
PubMed
Close
,
Feng Li bState Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, China

Search for other papers by Feng Li in
Current site
Google Scholar
PubMed
Close
, and
Runsheng Ge bState Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, China

Search for other papers by Runsheng Ge in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Doppler spectra measured by vertically pointing radars are inherently linked to raindrop size distributions (DSDs). But accurate estimation of DSDs remains challenging because raindrop spectra are broadened by atmospheric turbulence and shifted by vertical air motions. This paper presents a novel method to estimate vertical air motions in which there is no need to assume a model for DSD at each range gate. The theory of the new method is that the spectral difference between the adjacent range gates is contributed by vertical air motions and the variability of DSDs. The contribution of the change of DSDs is estimated by looking up the prepared tables [lookup tables (LUTs)] of raindrop velocity difference and shape function difference. Then the vertical air motions can be estimated by minimizing the cost function of the two spectra between the adjacent range gates. The retrieval algorithm is applied to three cases including a stratiform case and two convective cases observed by a C-band vertically pointing radar in Longmen, Guangdong Province, China, in June 2016. Before that, the spectrum broadening effect is removed by the traditional deconvolution method with a wind profiler. The vertical profiles of precipitation parameters are also retrieved to investigate the microphysical process. The precipitation parameters retrieved near the surface are compared with the ground data collected by a two-dimensional video disdrometer (2DVD), and the results show good agreement.

Significance Statement

Estimating vertical air motions and raindrop size distributions are of great help to improve precipitation forecasts and weather modification. This study developed a new method to estimate the vertical air motions using Doppler spectra measured by a vertically pointing radar. The advantage of the new method is that there is no need to assume models for raindrop size distributions. We applied the new method to a stratiform case and two convective cases, and the vertical profiles of precipitation parameters are retrieved. The results show that both the dynamic and microphysical processes in convective cases have larger variability than those in the stratiform case. In the future, the characteristics of more types of precipitation clouds will be investigated.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding authors: Zheng Ruan, ruanz@cma.gov.cn; Ling Yang, cimyang@cuit.edu.cn

Abstract

Doppler spectra measured by vertically pointing radars are inherently linked to raindrop size distributions (DSDs). But accurate estimation of DSDs remains challenging because raindrop spectra are broadened by atmospheric turbulence and shifted by vertical air motions. This paper presents a novel method to estimate vertical air motions in which there is no need to assume a model for DSD at each range gate. The theory of the new method is that the spectral difference between the adjacent range gates is contributed by vertical air motions and the variability of DSDs. The contribution of the change of DSDs is estimated by looking up the prepared tables [lookup tables (LUTs)] of raindrop velocity difference and shape function difference. Then the vertical air motions can be estimated by minimizing the cost function of the two spectra between the adjacent range gates. The retrieval algorithm is applied to three cases including a stratiform case and two convective cases observed by a C-band vertically pointing radar in Longmen, Guangdong Province, China, in June 2016. Before that, the spectrum broadening effect is removed by the traditional deconvolution method with a wind profiler. The vertical profiles of precipitation parameters are also retrieved to investigate the microphysical process. The precipitation parameters retrieved near the surface are compared with the ground data collected by a two-dimensional video disdrometer (2DVD), and the results show good agreement.

Significance Statement

Estimating vertical air motions and raindrop size distributions are of great help to improve precipitation forecasts and weather modification. This study developed a new method to estimate the vertical air motions using Doppler spectra measured by a vertically pointing radar. The advantage of the new method is that there is no need to assume models for raindrop size distributions. We applied the new method to a stratiform case and two convective cases, and the vertical profiles of precipitation parameters are retrieved. The results show that both the dynamic and microphysical processes in convective cases have larger variability than those in the stratiform case. In the future, the characteristics of more types of precipitation clouds will be investigated.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding authors: Zheng Ruan, ruanz@cma.gov.cn; Ling Yang, cimyang@cuit.edu.cn
Save
  • Aoki, M., H. Iwai, K. Nakagawa, S. Ishii, and K. Mizutani, 2016: Measurements of rainfall velocity and raindrop size distribution using coherent Doppler lidar. J. Atmos. Oceanic Technol., 33, 19491966, https://doi.org/10.1175/JTECH-D-15-0111.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Atlas, D., and C. W. Ulbrich, 2000: An observationally based conceptual model of warm oceanic convective rain in the tropics. J. Appl. Meteor., 39, 21652181, https://doi.org/10.1175/1520-0450(2001)040<2165:AOBCMO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, M. Y., and Y. H. Chu, 2011: Beam broadening effect on Doppler spectral width of wind profiler. Radio Sci., 46, RS5013, https://doi.org/10.1029/2011RS004704.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cifelli, R., C. R. Williams, D. K. Rajopadhyaya, S. K. Avery, K. S. Gage, and P. T. May, 2000: Drop-size distribution characteristics in tropical mesoscale convective systems. J. Appl. Meteor., 39, 760777, https://doi.org/10.1175/1520-0450(2000)039<0760:DSDCIT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ecklund, W. L., C. R. Williams, P. E. Johnston, and K. S. Gage, 1999: A 3GHz profiler for precipitating cloud studies. J. Atmos. Oceanic Technol., 16, 309322, https://doi.org/10.1175/1520-0426(1999)016<0309:AGPFPC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fang, M., B. Albrecht, E. Jung, P. Kollias, H. Jonsson, and I. Popstefanija, 2017: Retrieval of vertical air motion in precipitating clouds using Mie scattering and comparison with in situ measurements. J. Appl. Meteor. Climatol., 56, 537553, https://doi.org/10.1175/JAMC-D-16-0158.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Giangrande, S. E., E. P. Luke, and P. Kollias, 2012: Characterization of vertical velocity and drop size distribution parameters in widespread precipitation at ARM facilities. J. Appl. Meteor. Climatol., 51, 380391, https://doi.org/10.1175/JAMC-D-10-05000.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, D.-K., and D. Lee, 2016: Raindrop size distribution properties associated with vertical air motion in the stratiform region of a springtime rain event from 1290 MHz wind profiler, Micro Rain Radar and Parsivel disdrometer measurements. Meteor. Appl., 23, 562, https://doi.org/10.1002/met.1583.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, D.-K., K. R. Knupp, and C. R. Williams, 2009: Airflow and precipitation properties within the stratiform region of Tropical Storm Gabrielle during landfall. Mon. Wea. Rev., 137, 19541971, https://doi.org/10.1175/2008MWR2754.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kobayashi, T., and A. Adachi, 2005: Retrieval of arbitrarily shaped raindrop size distributions from wind profiler measurements. J. Atmos. Oceanic Technol., 22, 433442, https://doi.org/10.1175/JTECH1705.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kollias, P., E. E. Clothiaux, M. A. Miller, B. A. Albrecht, G. L. Stephens, and T. P. Ackerman, 2007: Millimeter-wavelength radars: New frontier in atmospheric cloud and precipitation research. Bull. Amer. Meteor. Soc., 88, 16081624, https://doi.org/10.1175/BAMS-88-10-1608.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krajewski, W. F., and Coauthors, 2006: DEVEX—Disdrometer Evaluation Experiment: Basic results and implications for hydrologic studies. Adv. Water Resour., 29, 311325, https://doi.org/10.1016/j.advwatres.2005.03.018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kruger, A., and W. F. Krajewski, 2002: Two-dimensional video disdrometer: A description. J. Atmos. Oceanic Technol., 19, 602617, https://doi.org/10.1175/1520-0426(2002)019<0602:TDVDAD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kumar, S., T. N. Rao, and B. Radhakrishna, 2019: Identification and separation of turbulence echo from the multipeaked VHF radar spectra during precipitation. IEEE Trans. Geosci. Remote Sens., 57, 57295737, https://doi.org/10.1109/TGRS.2019.2901832.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • L’Ecuyer, T. S., and G. L. Stephens, 2002: An estimation-based precipitation retrieval algorithm for attenuating radars. J. Appl. Meteor., 41, 272285, https://doi.org/10.1175/1520-0450(2002)041<0272:AEBPRA>2.0.CO;2.

    • 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, https://doi.org/10.1175/1520-0426(1990)007<0464:AASOMW>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, L., H. Ding, X. Dong, J. Cao, and T. Su, 2019: Applications of QC and merged Doppler spectral density data from Ka-band cloud radar to microphysics retrieval and comparison with airplane in situ observation. Remote Sens., 11, 1595, https://doi.org/10.3390/rs11131595.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Löffler-Mang, M., and J. Joss, 2000: An optical disdrometer for measuring size and velocity of hydrometeors. J. Atmos. Oceanic Technol., 17, 130139, https://doi.org/10.1175/1520-0426(2000)017<0130:AODFMS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Luo, L., H. Xiao, H. Yang, H. Chen, J. Guo, Y. Sun, and L. Feng, 2020: Raindrop size distribution and microphysical characteristics of a great rainstorm in 2016 in Beijing, China. Atmos. Res., 239, 104895, https://doi.org/10.1016/j.atmosres.2020.104895.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Matrosov, S. Y., 2017: Characteristic raindrop size retrievals from measurements of differences in vertical Doppler velocities at Ka- and W-band radar frequencies. J. Atmos. Oceanic Technol., 34, 6571, https://doi.org/10.1175/JTECH-D-16-0181.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Matrosov, S. Y., P. T. May, and M. D. Shupe, 2006: Rainfall profiling using atmospheric radiation measurement program vertically pointing 8-mm wavelength radars. J. Atmos. Oceanic Technol., 23, 14781491, https://doi.org/10.1175/JTECH1957.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Petitdidier, M., A. Sy, and J. Garrouste, 1997: Statistical characteristics of the noise power spectral density in UHF and VHF wind profilers. Radio Sci., 32, 12291247, https://doi.org/10.1029/97RS00250.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rajopadhyaya, D. K., P. T. May, R. C. Cifelli, S. K. Avery, C. R. Willams, W. L. Ecklund, and K. S. Gage, 1998: The effect of vertical air motions on rain rates and median volume diameter determined from combined UHF and VHF wind profiler measurements and comparisons with rain gauge measurements. J. Atmos. Oceanic Technol., 15, 13061319, https://doi.org/10.1175/1520-0426(1998)015<1306:TEOVAM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reddy, K. K., S. Shun-Peng, and Y. H. Chu, 2002: Study of a precipitating cloud system using Chung-Li VHF radar. Radio Sci., 37, 20, https://doi.org/10.1029/2000RS002544.

    • Search Google Scholar
    • Export Citation
  • Schafer, R., S. Avery, and P. May, 2002: Estimation of rainfall drop size distributions from dual-frequency wind profiler spectra using deconvolution and a nonlinear least squares fitting technique. J. Atmos. Oceanic Technol., 19, 864874, https://doi.org/10.1175/1520-0426(2002)019<0864:EORDSD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Steiner, M., J. A. Smith, and R. Uijlenhoet, 2004: A microphysical interpretation of radar reflectivity–rain rate relationship. J. Atmos. Sci., 61, 11141131, https://doi.org/10.1175/1520-0469(2004)061<1114:AMIORR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Strauch, R. G., B. L. Weber, A. S. Frisch, C. G. Little, D. A. Merritt, K. P. Moran, and D. C. Welsh, 1987: The precision and relative accuracy of profiler wind measurements. J. Atmos. Oceanic Technol., 4, 563571, https://doi.org/10.1175/1520-0426(1987)004<0563:TPARAO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tokay, A., W. A. Petersen, P. Gatlin, and M. Wingo, 2013: Comparison of raindrop size distribution measurements by collocated disdrometers. J. Atmos. Oceanic Technol., 30, 16721690, https://doi.org/10.1175/JTECH-D-12-00163.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tokay, A., L. P. D’Adderio, D. B. Wolff, and W. A. Petersen, 2016: A field study of pixel-scale variability of raindrop size distribution in the mid-Atlantic region. J. Hydrometeor., 17, 18551868, https://doi.org/10.1175/JHM-D-15-0159.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tridon, F., and A. Battaglia, 2015: Dual-frequency radar Doppler spectral retrieval of rain drop size distributions and entangled dynamics variables. J. Geophys. Res. Atmos., 120, 55855601, https://doi.org/10.1002/2014JD023023.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wakimoto, R. M., 1982: The life cycle of thunderstorm gust fronts as viewed with Doppler radar and rawinsonde data. Mon. Wea. Rev., 110, 10601082, https://doi.org/10.1175/1520-0493(1982)110<1060:TLCOTG>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Williams, C. R., 2002: Simultaneous ambient air motion and raindrop size distributions retrieved from UHF vertical incident profiler observations. Radio Sci., 37, 1024, https://doi.org/10.1029/2000RS002603.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Williams, C. R., 2012: Reflectivity and liquid water content vertical decomposition diagrams to diagnose vertical evolution of raindrop size distributions. J. Atmos. Oceanic Technol., 33, 579595, https://doi.org/10.1175/JTECH-D-15-0208.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Williams, C. R., R. M. Beauchamp, and V. Chandrasekar, 2016: Vertical air motions and raindrop size distributions estimated using mean Doppler velocity difference from 3- and 35-GHz vertically pointing radars. IEEE Trans. Geosci. Remote Sens., 54, 60486060, https://doi.org/10.1109/TGRS.2016.2580526.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, G. J., and N. A. McFarlane, 1995: Sensitivity of climate simulations to the parameterization of cumulus convection in the Canadian Climate Centre general circulation model. Atmos.–Ocean, 33, 407446, https://doi.org/10.1080/07055900.1995.9649539.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 282 0 0
Full Text Views 2976 2138 79
PDF Downloads 790 180 14