• Adirosi, E., L. Baldini, F. Lombardo, F. Russo, F. Napolitano, E. Volpi, and A. Tokay, 2015: Comparison of different fittings of drop spectra for rainfall retrievals. Adv. Water Resour., 83, 5567, doi:10.1016/j.advwatres.2015.05.009.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Adirosi, E., L. Baldini, N. Roberto, P. Gatlin, and A. Tokay, 2016: Improvement of vertical profiles of raindrop size distribution from micro rain radar using 2D video disdrometer measurements. Atmos. Res., 169, 404415, doi:10.1016/j.atmosres.2015.07.002.

    • 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, doi:10.1175/1520-0450(2001)040<2165:AOBCMO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Atlas, D., R. Srivastava, and R. S. Sekhon, 1973: Doppler radar characteristics of precipitation at vertical incidence. Rev. Geophys., 11, 135, doi:10.1029/RG011i001p00001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Battaglia, A., E. Rustemeier, A. Tokay, U. Blahak, and C. Simmer, 2010: PARSIVEL snow observations: A critical assessment. J. Atmos. Oceanic Technol., 27, 333344, doi:10.1175/2009JTECHA1332.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brandes, E. A., G. Zhang, and J. Vivekanandan, 2002: Experiments in rainfall estimation with a polarimetric radar in a subtropical environment. J. Appl. Meteor., 41, 674685, doi:10.1175/1520-0450(2002)041<0674:EIREWA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brandes, E. A., G. Zhang, and J. Vivekanandan, 2003: An evaluation of a drop distribution–based polarimetric radar rainfall estimator. J. Appl. Meteor., 42, 652660, doi:10.1175/1520-0450(2003)042<0652:AEOADD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brandes, E. A., G. Zhang, and J. Vivekanandan, 2004: Drop size distribution retrieval with polarimetric radar: Model and application. J. Appl. Meteor., 43, 461475, doi:10.1175/1520-0450(2004)043<0461:DSDRWP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brandes, E. A., G. Zhang, and J. Sun, 2006: On the influence of assumed drop size distribution form on radar-retrieved thunderstorm microphysics. J. Appl. Meteor. Climatol., 45, 259268, doi:10.1175/JAM2335.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bringi, V., V. Chandrasekar, J. Hubbert, E. Gorgucci, W. L. Randeu, and M. Schoenhuber, 2003: Raindrop size distribution in different climatic regimes from disdrometer and dual-polarized radar analysis. J. Atmos. Sci., 60, 354365, doi:10.1175/1520-0469(2003)060<0354:RSDIDC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Campos, E., and I. Zawadzki, 2000: Instrumental uncertainties in ZR relations. J. Appl. Meteor., 39, 10881102, doi:10.1175/1520-0450(2000)039<1088:IUIZRR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cao, Q., and G. Zhang, 2009: Errors in estimating raindrop size distribution parameters employing disdrometer and simulated raindrop spectra. J. Appl. Meteor. Climatol., 48, 406425, doi:10.1175/2008JAMC2026.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cao, Q., G. Zhang, E. Brandes, T. Schuur, A. Ryzhkov, and K. Ikeda, 2008: Analysis of video disdrometer and polarimetric radar data to characterize rain microphysics in Oklahoma. J. Appl. Meteor. Climatol., 47, 22382255, doi:10.1175/2008JAMC1732.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Carbone, R., J. Tuttle, D. Ahijevych, and S. Trier, 2002: Inferences of predictability associated with warm season precipitation episodes. J. Atmos. Sci., 59, 20332056, doi:10.1175/1520-0469(2002)059<2033:IOPAWW>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, B., J. Yang, and J. Pu, 2013: Statistical characteristics of raindrop size distribution in the Meiyu season observed in eastern China. J. Meteor. Soc. Japan, 91, 215227, doi:10.2151/jmsj.2013-208.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ding, Y., and J. C. Chan, 2005: The East Asian summer monsoon: An overview. Meteor. Atmos. Phys., 89, 117142, doi:10.1007/s00703-005-0125-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gilmore, M. S., J. M. Straka, and E. N. Rasmussen, 2004: Precipitation uncertainty due to variations in precipitation particle parameters within a simple microphysics scheme. Mon. Wea. Rev., 132, 26102627, doi:10.1175/MWR2810.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Habib, E., W. F. Krajewski, and A. Kruger, 2001: Sampling errors of tipping-bucket rain gauge measurements. J. Hydrol. Eng., 6, 159166, doi:10.1061/(ASCE)1084-0699(2001)6:2(159).

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jaffrain, J., A. Studzinski, and A. Berne, 2011: A network of disdrometers to quantify the small‐scale variability of the raindrop size distribution. Water Resour. Res., 47, W00H06, doi:10.1029/2010WR009872.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Joss, J., and A. Waldvogel, 1969: Raindrop size distribution and sampling size errors. J. Atmos. Sci., 26, 566569, doi:10.1175/1520-0469(1969)026<0566:RSDASS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kessler, E., 1969: On the Distribution and Continuity of Water Substance in Atmospheric Circulation. Meteor. Monogr., No. 32, Amer. Meteor. Soc., 84 pp.

    • Crossref
    • Export Citation
  • Kliche, D. V., P. L. Smith, and R. W. Johnson, 2008: L-moment estimators as applied to gamma drop size distributions. J. Appl. Meteor. Climatol., 47, 31173130, doi:10.1175/2008JAMC1936.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krajewski, W. F., and et al. , 2006: DEVEX-disdrometer evaluation experiment: Basic results and implications for hydrologic studies. Adv. Water Resour., 29, 311325, doi: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, doi:10.1175/1520-0426(2002)019<0602:TDVDAD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lee, G. W., and I. Zawadzki, 2005: Variability of drop size distributions: Time-scale dependence of the variability and its effects on rain estimation. J. Appl. Meteor., 44, 241255, doi:10.1175/JAM2183.1.

    • 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, doi:10.1175/1520-0426(2000)017<0130:AODFMS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mallet, C., and L. Barthes, 2009: Estimation of gamma raindrop size distribution parameters: Statistical fluctuations and estimation errors. J. Atmos. Oceanic Technol., 26, 15721584, doi:10.1175/2009JTECHA1199.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Milbrandt, J., and M. Yau, 2006a: A multimoment bulk microphysics parameterization. Part III: Control simulation of a hailstorm. J. Atmos. Sci., 63, 31143136, doi:10.1175/JAS3816.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Milbrandt, J., and M. Yau, 2006b: A multimoment bulk microphysics parameterization. Part IV: Sensitivity experiments. J. Atmos. Sci., 63, 31373159, doi:10.1175/JAS3817.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morrison, H., G. Thompson, and V. Tatarskii, 2009: Impact of cloud microphysics on the development of trailing stratiform precipitation in a simulated squall line: Comparison of one-and two-moment schemes. Mon. Wea. Rev., 137, 9911007, doi:10.1175/2008MWR2556.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nemeth, K., and M. Löffler-Mang, 2006: OTT Parsivel—Enhanced precipitation identifier and new generation of present weather sensor by OTT Messtechnik, Germany. Preprints, Fourth Int. Conf. on Experiences with Automatic Weather Stations (ICEAWS), Lisbon, Portugal, WMO, 2. [Available online at http://projects.knmi.nl/geoss/ICEAWS/ICEAWS-4/CD/docs/ORAL/2_oral.pdf.]

  • Nešpor, V., W. F. Krajewski, and A. Kruger, 2000: Wind-induced error of raindrop size distribution measurement using a two-dimensional video disdrometer. J. Atmos. Oceanic Technol., 17, 14831492, doi:10.1175/1520-0426(2000)017<1483:WIEORS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peters, G., B. Fischer, and T. Andersson, 2002: Rain observations with a vertically looking Micro Rain Radar (MRR). Boreal Environ. Res., 7, 353362.

    • Search Google Scholar
    • Export Citation
  • Peters, G., B. Fischer, H. Münster, M. Clemens, and A. Wagner, 2005: Profiles of raindrop size distributions as retrieved by microrain radars. J. Appl. Meteor., 44, 19301949, doi:10.1175/JAM2316.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peters, G., B. Fischer, and M. Clemens, 2010: Rain attenuation of radar echoes considering finite-range resolution and using drop size distributions. J. Atmos. Oceanic Technol., 27, 829842, doi:10.1175/2009JTECHA1342.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Raupach, T., and A. Berne, 2015: Correction of raindrop size distributions measured by Parsivel disdrometers, using a two-dimensional video disdrometer as a reference. Atmos. Meas. Tech., 8, 343365, doi:10.5194/amt-8-343-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sarkar, T., S. Das, and A. Maitra, 2015: Assessment of different raindrop size measuring techniques: Inter-comparison of Doppler radar, impact and optical disdrometer. Atmos. Res., 160, 1527, doi:10.1016/j.atmosres.2015.03.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sauvageot, H., and J.-P. Lacaux, 1995: The shape of averaged drop size distributions. J. Atmos. Sci., 52, 10701083, doi:10.1175/1520-0469(1995)052<1070:TSOADS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schönhuber, M., H. Urban, J. P. V. P. Baptista, W. Randeu, and W. Riedler, 1997: Weather radar versus 2D-video-disdrometer data. Weather Radar Technology for Water Resources Management, B. P. F. Bragg Jr. and O. Massambani, Eds., Unesco Press, 159–171.

  • Sheppard, B. E., 1990: Measurement of raindrop size distributions using a small Doppler radar. J. Atmos. Oceanic Technol., 7, 255268, doi:10.1175/1520-0426(1990)007<0255:MORSDU>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sheppard, B. E., and P. I. Joe, 1994: Comparison of raindrop size distribution measurements by a Joss-Waldvogel disdrometer, a PMS 2DG spectrometer, and a POSS Doppler radar. J. Atmos. Oceanic Technol., 11, 874887, doi:10.1175/1520-0426(1994)011<0874:CORSDM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, P. L., 2003: Raindrop size distributions: Exponential or gamma—Does the difference matter? J. Appl. Meteor., 42, 10311034, doi:10.1175/1520-0450(2003)042<1031:RSDEOG>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, P. L., and D. V. Kliche, 2005: The bias in moment estimators for parameters of drop size distribution functions: Sampling from exponential distributions. J. Appl. Meteor., 44, 11951205, doi:10.1175/JAM2258.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, P. L., Z. Liu, and J. Joss, 1993: A study of sampling-variability effects in raindrop size observations. J. Appl. Meteor., 32, 12591269, doi:10.1175/1520-0450(1993)032<1259:ASOSVE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, P. L., D. V. Kliche, and R. W. Johnson, 2009: The bias and error in moment estimators for parameters of drop size distribution functions: Sampling from gamma distributions. J. Appl. Meteor. Climatol., 48, 21182126, doi:10.1175/2009JAMC2114.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Snook, N., and M. Xue, 2008: Effects of microphysical drop size distribution on tornadogenesis in supercell thunderstorms. Geophys. Res. Lett., 35, L24803, doi:10.1029/2008GL035866.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thurai, M., and V. Bringi, 2005: Drop axis ratios from a 2D video disdrometer. J. Atmos. Oceanic Technol., 22, 966978, doi:10.1175/JTECH1767.1.

  • Thurai, M., W. Petersen, A. Tokay, C. Schultz, and P. Gatlin, 2011: Drop size distribution comparisons between Parsivel and 2-D video disdrometers. Adv. Geosci., 30, 39, doi:10.5194/adgeo-30-3-2011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tokay, A., and D. A. Short, 1996: Evidence from tropical raindrop spectra of the origin of rain from stratiform versus convective clouds. J. Appl. Meteor., 35, 355371, doi:10.1175/1520-0450(1996)035<0355:EFTRSO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tokay, A., and P. G. Bashor, 2010: An experimental study of small-scale variability of raindrop size distribution. J. Appl. Meteor. Climatol., 49, 23482365, doi:10.1175/2010JAMC2269.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tokay, A., A. Kruger, and W. F. Krajewski, 2001: Comparison of drop size distribution measurements by impact and optical disdrometers. J. Appl. Meteor., 40, 20832097, doi:10.1175/1520-0450(2001)040<2083:CODSDM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tokay, A., P. Hartmann, A. Battaglia, K. S. Gage, W. L. Clark, and C. R. Williams, 2009: A field study of reflectivity and ZR relations using vertically pointing radars and disdrometers. J. Atmos. Oceanic Technol., 26, 11201134, doi:10.1175/2008JTECHA1163.1.

    • 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, doi:10.1175/JTECH-D-12-00163.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tokay, A., D. B. Wolff, and W. A. Petersen, 2014: Evaluation of the new version of the laser-optical disdrometer, OTT Parsivel2. J. Atmos. Oceanic Technol., 31, 12761288, doi:10.1175/JTECH-D-13-00174.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ulbrich, C. W., 1983: Natural variations in the analytical form of the raindrop size distribution. J. Climate Appl. Meteor., 22, 17641775, doi:10.1175/1520-0450(1983)022<1764:NVITAF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wen, L., S. Liu, K. Zhao, Y. Li, and L. Li, 2015: Precision evaluation of micro rain radar observation in two precipitation events (in Chinese). Meteor. Mon., 41, 577587, doi:10.7519/j.issn.1000-0526.2015.05.006.

    • Search Google Scholar
    • Export Citation
  • Wen, L., K. Zhao, G. Zhang, M. Xue, B. Zhou, S. Liu, and X. Chen, 2016: Statistical characteristics of raindrop size distributions observed in East China during the Asian summer monsoon season using 2-D video disdrometer and Micro Rain Radar data. J. Geophys. Res. Atmos., 121, 22652282, doi:10.1002/2015JD024160.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yuter, S. E., D. E. Kingsmill, L. B. Nance, and M. Löffler-Mang, 2006: Observations of precipitation size and fall speed characteristics within coexisting rain and wet snow. J. Appl. Meteor. Climatol., 45, 14501464, doi:10.1175/JAM2406.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, G., J. Vivekanandan, and E. Brandes, 2001: A method for estimating rain rate and drop size distribution from polarimetric radar measurements. IEEE Trans. Geosci. Remote Sens., 39, 830841, doi:10.1109/36.917906.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, G., J. Vivekanandan, E. A. Brandes, R. Meneghini, and T. Kozu, 2003: The shape–slope relation in observed gamma raindrop size distributions: Statistical error or useful information? J. Atmos. Oceanic Technol., 20, 11061119, doi:10.1175/1520-0426(2003)020<1106:TSRIOG>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, G., J. Sun, and E. A. Brandes, 2006: Improving parameterization of rain microphysics with disdrometer and radar observations. J. Atmos. Sci., 63, 12731290, doi:10.1175/JAS3680.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, G., E. Brandes, and K. Iketa, 2011: Instrumentation effects on estimated drop size distribution and radar parameters. 35th Conf. on Radar Meteorology, Pittsburgh, PA, Amer. Meteor. Soc., 35. [Available online at https://ams.confex.com/ams/35Radar/webprogram/Paper191853.html.]

  • Zhao, K., and M. Xue, 2015: Preliminary results from the field experiment of OPACC. Geophysical Research Abstracts, Vol. 17, Abstract EGU2015-4420. [Available online at http://meetingorganizer.copernicus.org/EGU2015/EGU2015-4420.pdf.]

  • Zheng, K., and B. Chen, 2014: Sensitivities of tornadogenesis to drop size distribution in a simulated subtropical supercell over eastern China. Adv. Atmos. Sci., 31, 657668, doi:10.1007/s00376-013-3143-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • View in gallery

    Field view of the Jiangning site (JN), displaying the relative positions of the MRR, OTT-1 and OTT-2, and the 2DVD. (inset) Local topography around the JN site.

  • View in gallery

    Comparison of hourly rain totals between rain gauge and 2DVD, MRR, OTT-1, and OTT-2 for mei-yu rainfall. The CC, SD, bias, and absolute bias are also given.

  • View in gallery

    Comparison of rain rate between 2DVD, MRR, OTT-1, and OTT-2 for mei-yu rainfall. The CC, SD, bias, and absolute bias are also given.

  • View in gallery

    Composite raindrop spectra from the 2DVD, MRR, OTT-1 and OTT-2.

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    Mean and SD of measured fall velocity by 2DVD, OTT-1, and OTT-2 as a function of diameter. 2DVD data are adjusted to OTT-2 diameter bins.

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    Occurrence frequencies of various parameters computed from the DSDs: (a) Dm (mm), (b) log10 Nt (m−3), (c) LWC (g m−3), and (d) σm (mm).

  • View in gallery

    Scatterplot of averaged log10 Nw vs Dm for convective (square box), stratiform (triangle), and shallow (cross) rain types, and for the whole dataset (dots). Red, blue, green, black, and pink symbol represent the averaged values for the 2DVD, MRR, OTT-1, OTT-2, and Chen et al. (2013), respectively. Two gray rectangles correspond to the maritime and continental convective clusters, respectively, reported by Bringi et al. (2003). Cyan dashed line is that of Bringi et al. (2003) stratiform rain.

  • View in gallery

    (a) Derived ZR relations from two mei-yu season observations for the four instruments with different colors. (b)–(e) Comparison of rain gauge observed and ZR relation estimated hourly rain totals for mei-yu rainfall.

  • View in gallery

    Kernel smoothing function estimate of estimated evaporation and accretion rates computed from collocated measurements.

  • View in gallery

    Time series of (a) observed DSD and (b) DSD attributes, as well as estimated evaporation and accretion rates computed from collocated measurements. Vertical gray line and data are for 0938 LST 12 Jul 2014.

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Impacts of Instrument Limitations on Estimated Raindrop Size Distribution, Radar Parameters, and Model Microphysics during Mei-Yu Season in East China

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  • 1 Key Laboratory of Mesoscale Severe Weather, Ministry of Education, and School of Atmospheric Science, Nanjing University, Nanjing, and Xichang Measurement Station, Xichang Satellite Launch Center, Xichang, China
  • | 2 Key Laboratory of Mesoscale Severe Weather, Ministry of Education, and School of Atmospheric Science, Nanjing University, Nanjing, and State Key Laboratory of Severe Weather and Joint Center for Atmospheric Radar Research of CMA/NJU, Chinese Academy of Meteorological Sciences, Beijing, China
  • | 3 Key Laboratory of Mesoscale Severe Weather, Ministry of Education, and School of Atmospheric Science, Nanjing University, Nanjing, China, and School of Meteorology, and Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, Oklahoma
  • | 4 Key Laboratory of Mesoscale Severe Weather, Ministry of Education, and School of Atmospheric Science, Nanjing University, Nanjing, China
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Abstract

Instrumentation limitations on measured raindrop size distributions (DSDs) and their derived relations and physical parameters are studied through a comparison of the DSD measurements during mei-yu season in east China by four collocated instruments, that is, a two-dimensional video disdrometer (2DVD), a vertically pointing Micro Rain Radar (MRR), and two laser-optical OTT Particle Size Velocity (PARSIVEL) disdrometers (first generation: OTT-1; second generation: OTT-2). Among the four instruments, the 2DVD provides the most accurate DSD and drop velocity measurements, so its measured rainfall amount has the best agreement with the reference rain gauge. Other instruments tend to miss more small drops (D < 1 mm), leading to inaccurate DSDs and a lower rainfall amount. The low rainfall estimation becomes significant during heavy rainfall. The impacts of instrument limitations on the microphysical processes (e.g., evaporation and accretion rates) and convective storm morphology are evaluated. This is important especially for mei-yu precipitation, which is dominated by a high concentration of small drops. Hence, the instrument limitations need to be taken into account in both QPE and microphysics parameterization.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Dr. Kun Zhao, zhaokun@nju.edu.cn; Guifu Zhang, guzhang1@ou.edu

Abstract

Instrumentation limitations on measured raindrop size distributions (DSDs) and their derived relations and physical parameters are studied through a comparison of the DSD measurements during mei-yu season in east China by four collocated instruments, that is, a two-dimensional video disdrometer (2DVD), a vertically pointing Micro Rain Radar (MRR), and two laser-optical OTT Particle Size Velocity (PARSIVEL) disdrometers (first generation: OTT-1; second generation: OTT-2). Among the four instruments, the 2DVD provides the most accurate DSD and drop velocity measurements, so its measured rainfall amount has the best agreement with the reference rain gauge. Other instruments tend to miss more small drops (D < 1 mm), leading to inaccurate DSDs and a lower rainfall amount. The low rainfall estimation becomes significant during heavy rainfall. The impacts of instrument limitations on the microphysical processes (e.g., evaporation and accretion rates) and convective storm morphology are evaluated. This is important especially for mei-yu precipitation, which is dominated by a high concentration of small drops. Hence, the instrument limitations need to be taken into account in both QPE and microphysics parameterization.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Dr. Kun Zhao, zhaokun@nju.edu.cn; Guifu Zhang, guzhang1@ou.edu

1. Introduction

Raindrop size distributions (DSDs) are an important characteristic of microphysical processes. Much of what is known about DSDs has been determined from observations from different types of disdrometers. While seemingly a straightforward task, inferred DSD properties are subject to a number of error sources. The size of the raindrop sample (Joss and Waldvogel 1969; Smith et al. 1993; Smith and Kliche 2005; Mallet and Barthes 2009; Smith et al. 2009), the appropriateness of the assumed DSD model and the fitting method (e.g., Smith 2003; Kliche et al. 2008; Mallet and Barthes 2009; Adirosi et al. 2015), wind effects (Nešpor et al. 2000), and splashing effects all contribute to DSD errors. The parameters of an assumed DSD function form (such as the gamma DSD model) are usually computed from various moments of the observed drops (e.g., Tokay and Short 1996) and vary somewhat according to the particular moments used (Zhang et al. 2003; Smith and Kliche 2005; Cao and Zhang 2009; Smith et al. 2009). However, the abovementioned error sources may be secondary to instrumentation limitations and related biases (Zhang et al. 2011).

Driven by the need for a more accurate measurement of DSDs, two types of disdrometers—that is, ground-based optical disdrometers and low-power radar disdrometers—have been developed. The first type can measure both DSD and the fall velocity of raindrops near the ground, and includes the Joss–Waldvogel impact disdrometer (JWD; Joss and Waldvogel 1969), the laser-optical OTT Particle Size Velocity (PARSIVEL) disdrometer (Löffler-Mang and Joss 2000), and the two-dimensional video disdrometer (2DVD; Schönhuber et al. 1997). The second type can measure the vertical profile of DSDs and includes low-power radars like the Precipitation Occurrence Sensor System (POSS; Sheppard 1990) and the vertically pointing Micro Rain Radar (MRR; Peters et al. 2002).

The impacts of the instrument limitations on the derived DSDs have been widely studied. Typically, the radar reflectivity computed from disdrometer measurements agrees well with radar observations (e.g., Sheppard and Joe 1994; Tokay et al. 2001; Zhang et al. 2001; Brandes et al. 2003, 2004). The disdrometer measurements are frequently extended to estimate other DSD attributes, such as the governing parameters of assumed exponential and gamma DSD models (Tokay and Short 1996; Tokay et al. 2001; Zhang et al. 2001; Brandes et al. 2003, 2004; Brandes et al. 2006). To clearly verify the estimated DSD attributes, several intercomparison studies have been reported using various types of collocated disdrometers and radars. The estimated drop spectra were affected by differences in the working principles of the instruments (Krajewski et al. 2006; Cao et al. 2008; Sarkar et al. 2015; Wen et al. 2015; Adirosi et al. 2016), sampling volumes (Campos and Zawadzki 2000; Thurai et al. 2011), and truncation (Tokay et al. 2001; Smith 2003), resulting in clear differences in the estimated DSDs and the derived relations, that is, the ZR relation (where Z and R are the radar reflectivity factor and rain rate, respectively) used for radar quantitative precipitation estimation (QPE). The discrepancies in estimated DSDs among different disdrometers were comparable to or even larger than those found in different climate regimes (Campos and Zawadzki 2000).

The OTT has been shown to be susceptible to errors in undetected small drops (D < 1 mm) compared to the 2DVD, as reported by Krajewski et al. (2006) and Tokay et al. (2013). However, their conclusions concerning small drop impacts on derived DSD properties were quite different. Krajewski et al. (2006) found that the impact of missing small drops can lead to substantial differences in the nth-ordered DSD moments. Tokay et al. (2013), on the other hand, noted that when related to the integral parameters such as liquid water content (LWC, the third moment of DSDs) and rain rate (R, the 3.67th moment of DSDs), both 2DVD and OTT are reliable, since the small drops do not significantly influence these parameters. Compared to ground-based optical disdrometers, the MRR can be influenced by several issues related to vertical winds, attenuation correction, Doppler spectra aliasing, and range–Doppler ambiguity (Peters et al. 2005; Peters et al. 2010). Adirosi et al. (2016) found that the MRR overestimates (underestimate) small (midsize and large) raindrops relative to the 2DVD, resulting in the discrepancy of the measured Z (the sixth moment of DSDs) between MRR and 2DVD. Recently, efforts have been carried out to apply the 2DVD as a reference for the correction of OTT (Raupach and Berne 2015) and MRR (Adirosi et al. 2016) measurements.

Documenting observational uncertainties has value because it helps to define the limits of instruments and to correct their measurements. Despite elaborate efforts by previous studies mentioned above, two important issues remain unclear and unsettled. First, the influence of instrument differences on estimated DSD attributes are not conclusive; second, the effects of undetected small drops on radar QPE and model microphysical processes still remain unclear.

To improve the understanding of the dynamics and microphysics of severe convective storms in China, the field campaign of Observation, Prediction and Analysis of Severe Convection of China (OPACC; Zhao and Xue 2015) project was conducted in the Yangtze–Huaihe River Basin in east China during the summer of 2014 and 2015. As the Asian summer monsoon progresses northward, the mei-yu season in the Yangtze–Huaihe River basin (from mid-June to mid-July) is associated with a quasi-stationary front often producing persistent heavy rainfall (Ding and Chan 2005). For the first time, four different instruments—that is, a 2DVD, a first-generation OTT PARSIVEL (OTT-1), an upgraded second-generation OTT PARSIVEL (OTT-2), and an MRR—were collocated to observe the mei-yu precipitation microphysics in China. Based on 2 years of 2DVD observations, Wen et al. (2016) found that the DSDs of mei-yu precipitation have a much higher concentration of small raindrops than the precipitation in other monsoon regions. But the limitations of different disdrometers on these unique precipitation characteristics remain unclear. Hence, the purpose of this study is to better address the two issues raised above through comparative study using this unique dataset from OPACC. The results can be applied to other climate regions with similar precipitation characteristics of abundant small drops.

Data and methodology are described in section 2. A comparison of DSD measurements from the four instruments is given in section 3. Section 4 reveals the effects of DSD measurements on radar QPE and model microphysical processes. Section 5 provides a summary and discussion.

2. Data and methods

a. Instruments and datasets

The datasets used in this study were collected at the Jiangning field site, Nanjing, China, during the mei-yu seasons. The mei-yu periods are between 23 June and 19 July 2014, and between 24 June and 13 July 2015, as determined by the Chinese Meteorological Administration (CMA; http://www.cma.gov.cn/). The location and instruments of the Jiangning field site (JN; 31.93°N, 118.90°E) are shown in Fig. 1. The 2DVD, OTT-1, and OTT-2 were positioned within 3 m of each other. The MRR was located on the roof of a nearby building, about 7 m above the ground and 20 m away from the 2DVD. Note that the OTT-1 data were available only in 2014.

Fig. 1.
Fig. 1.

Field view of the Jiangning site (JN), displaying the relative positions of the MRR, OTT-1 and OTT-2, and the 2DVD. (inset) Local topography around the JN site.

Citation: Journal of Atmospheric and Oceanic Technology 34, 5; 10.1175/JTECH-D-16-0225.1

The third-generation 2DVD (Tokay et al. 2013) is designed to mitigate splashing as well as reduce wind-induced errors in response to Nešpor et al. (2000). In this study, the equivalent volume diameters are sorted into size categories of 0.2 mm. The range in tabulated raindrop diameters is 0.1–8.1 mm (41 bins). The fall velocity of each bin is then obtained by averaging measured particle velocities within that size bin. Drops under 0.3 mm were considered unreliable, as suggested in Tokay et al. (2013). The data are retained in this study, but we will not discuss that part of the spectrum. The OTT disdrometers, both OTT-1 and OTT-2, measure 32 bins of diameter from 0 to 25 mm and 32 bins of fall speeds from 0 to 22.4 m s−1 (Nemeth and Löffler-Mang 2006). The first two drop size classes are left empty due to the low signal-to-noise ratio. Compared with OTT-1, OTT-2 uses a more expensive laser device to provide better homogeneity of the laser sheet, so its accuracy has been improved; more details can be found in Tokay et al. (2014). The MRR observes 30 vertical levels in the atmosphere with a changeable range-gate resolution (set to 200 m in this study). It is also capable of estimating DSDs from the Doppler spectra utilizing the relationship between drop size and terminal fall velocity (Peters et al. 2002). The retrieval is applied only in the size range from 0.246 to 5.03 mm, corresponding to the height-normalized velocity range from 0.78 to 9.34 m s−1 (Sarkar et al. 2015; Wen et al. 2015). The standard processing algorithm of MRR as described in Peters et al. (2005) is used where the MRR does not account for wind (horizontal or vertical) effect (Peters et al. 2002); thus, the zero vertical wind assumption is made in the DSD retrieval. Since the goal of this study is to examine the limitations of different disdrometers, the 2DVD observations have not been used to correct the MRR or OTT measurements.

The temporal resolution is 1 min for all four instruments in this study. Detailed information about the four instruments is provided in Table 1. A size correction proposed by Battaglia et al. (2010) has been applied for the OTT-1 because of the nonnegligible measurement error. The OTT-2 provided significant improvements over OTT-1 (Tokay et al. 2014), so the size correction is not applied. For only the fall velocity comparisons, 2DVD data have been recomputed using OTT-2 diameter bins. Precision evaluation of MRR observations using 2DVD and S-band radar (Tokay et al. 2009; Wen et al. 2015) has shown that an important limitation in MRR is the strong reflectivity and attenuation in heavy rainfall, which leads to a height-dependent underestimation of rain rate. However, the attenuation can be neglected for the lowest level (200 m) data (see also Sarkar et al. 2015). Therefore, the 200-m DSD data from the MRR were used as a proxy for the surface DSDs to compare with those measured by the other three disdrometers on the ground in this study.

Table 1.

Specifications of the four instruments.

Table 1.

The particle-size-versus-velocity filter (e.g., Kruger and Krajewski 2002; Thurai and Bringi 2005; Jaffrain et al. 2011) was used on the 2DVD’s and OTTs’ data to remove spurious drops. The filter removes 25.47%, 4.83%, and 2.26% of the total number of particles detected by OTT-1, OTT-2, and 2DVD in this study, respectively. Rainfall from a tipping-bucket rain gauge (RG) with 0.1-mm resolution was used as the ground truth. During the two mei-yu periods, nine precipitation episodes were identified (Table 2) using the method proposed by Carbone et al. (2002). A quality control method like that used in Tokay et al. (2013) was employed to process the disdrometer measurements. Specifically, for the 1-min data from the disdrometers, if the total number of drops is less than 10 or a disdrometer-derived rain rate is less than 0.1 mm h−1, then it is discarded; otherwise, it is considered to be a rainy minute. For the 1-min data from the MRR, if the rain rate observed by the MRR at 200 m is less than 0.1 mm h−1, then it was discarded as nonprecipitation echo.

Table 2.

Episode dates and the total rainfall accumulations.

Table 2.

b. DSD parameters and rainfall statistics

When a raindrop size distribution is given, the integral rainfall parameters, including R (mm h−1), LWC (g m−3), and the total concentration of raindrops (m−3), can be derived from the nth-order weighted moment of the DSD using measured fall velocity.

A widely accepted model applied to raindrop size distributions is the gamma distribution (Ulbrich 1983),
e1
where D (mm) is the equivalent volume diameter, and N0 (mm−1-µ m−3), Λ (mm−1), and µ (dimensionless) are the concentration, the slope, and the shape parameters, respectively. In this study, raindrops are considered as small or large if D is less than 1 mm or larger than 4 mm; otherwise, they are classified as midsize.

Other estimated parameters of interest, including the mass-weighted mean diameter Dm (mm), the generalized intercept parameter Nw (mm−1 m−3), and the radar reflectivity at horizontal polarization ZH, are computed from the measured DSD directly. Detailed expressions for the computed parameters mentioned above are all given in Wen et al. (2016). After that, three types of precipitation—that is, stratiform, convective, and shallow rain—are identified from the rainfall intensity and the vertical structure of radar reflectivity from 2DVD and MRR observations using the same method in Wen et al. (2016). The classification scheme produces 1188 convective (1 min) samples, 3696 stratiform samples, and 439 shallow samples. The 2DVD-measured frequencies of precipitation for the three rain types are listed in Table 3. Note that the fraction of uncategorized rainfall is about 15.7% in terms of total rainfall contribution for 2DVD measurements. The 2DVD-measured convective rain contributes 75.7% of the categorized rainfall amount for the mei-yu precipitation at Jiangning station.

Table 3.

Frequency of precipitation of the classified rain types by the 2DVD. Percentage refers to the contribution of each rain type to the total categorized rainfall amount.

Table 3.

The correlation coefficient (CC), the standard deviations (SD), the mean difference, and absolute difference are used to evaluate the performance of RG and the four DSD measurement instruments. The quantities between the two instrument measurements (x, y) for n samples are calculated as
e2
e3
e4
e5
where and are the mean values for all samples from each instrument.

3. Comparison of DSD measurements from four instruments

a. Rainfall observations

The error characteristics of RG measurements are well understood (Habib et al. 2001). Nevertheless, they can provide reliable rain totals (Tokay and Bashor 2010). For the hourly rainfall, general agreement can be found among all four instruments and the RG (Fig. 2). Integrated over all episodes, the 2DVD recorded 4.6% more rainfall than the RG (Table 2). The MRR showed the lowest bias, but its absolute bias is relatively high, indicating a large variability in the rainfall measured. The OTT-1 and OTT-2, on the other hand, had 16.5% and 13.3%, respectively, less rainfall than the RG. Krajewski et al. (2006) and Tokay et al. (2013, 2014) reported approximately a 15%–20% bias for OTT when compared with RG for different event rain totals. In our study the hourly rain totals are compared, which result in a relatively larger deviation than reported in these previous studies. Among the four instruments, the 2DVD showed the best agreement with the RG, consistent with many previous studies (e.g., Raupach and Berne 2015). Therefore, the 2DVD observation is used as a reference in the following comparison of 1-min rainfall and DSD measurements.

Fig. 2.
Fig. 2.

Comparison of hourly rain totals between rain gauge and 2DVD, MRR, OTT-1, and OTT-2 for mei-yu rainfall. The CC, SD, bias, and absolute bias are also given.

Citation: Journal of Atmospheric and Oceanic Technology 34, 5; 10.1175/JTECH-D-16-0225.1

The scatterplots of all categorized 1-min rainfall samples among the four instruments are presented in Fig. 3. The MRR showed the highest variability when compared to the other three instruments. This may be attributed to its higher measurement height (200 m) and the DSD retrieval method (which could be affected by vertical wind) that it applied. The highest CC can be found for the 2DVD–OTT-2 pair in all three rain types, while the OTT-2 tends to underestimate rainfall. Specifically, there is good agreement for the shallow rain (with 4.4% bias, figure not shown), as the extent of underestimation increases when the rainfall intensity increases. A similar pattern (with a larger bias) can be seen for the 2DVD–OTT-1 comparison. Because of the use of a more accurate laser device, the OTT-2 showed some improvements over the OTT-1, especially for the observations in both light and heavy rain.

Fig. 3.
Fig. 3.

Comparison of rain rate between 2DVD, MRR, OTT-1, and OTT-2 for mei-yu rainfall. The CC, SD, bias, and absolute bias are also given.

Citation: Journal of Atmospheric and Oceanic Technology 34, 5; 10.1175/JTECH-D-16-0225.1

When considering the close proximity of the instruments to each other however, the differences of rainfall measurements are nonnegligible and should be largely attributed to the instrumental characteristics. The underestimation of rainfall by the OTTs in our study is opposite of that in Krajewski et al. (2006). This can be mainly attributed to a more critical missing of mid- to small-size drops that dominant the mei-yu precipitation, as showed in Fig. 4. Further comparisons of the DSD measurements will be discussed to better understand the impact of these instrumental effects in the next section.

Fig. 4.
Fig. 4.

Composite raindrop spectra from the 2DVD, MRR, OTT-1 and OTT-2.

Citation: Journal of Atmospheric and Oceanic Technology 34, 5; 10.1175/JTECH-D-16-0225.1

b. Composite raindrop spectra

The characteristics of the DSD shapes based on the measurements from four instruments are shown in Fig. 4. The composite spectra exhibit similar one-peak distributions in all four curves. Nevertheless, there are distinct differences in the DSDs at the small and large drop sizes.

For the small drops, the peak concentration for the 2DVD appears near the low limit of drop size (~0.3 mm) and much higher than the other three instruments. The OTTs had peak concentrations mostly in a diameter of 0.32–0.58 mm with a sharp drop-off toward smaller sizes, similar to those observed by previous studies (e.g., Tokay et al. 2013). Note that the peak concentrations of the OTTs are nearly 4 times less than those measured by the 2DVD. The MRR, on the other hand, showed a sharp rise in concentration toward smaller sizes under 0.5 mm, which lies between the 2DVD and OTT’s curve.

The concave downward shape of OTT size spectra at the small drop end was interpreted as a physical fact and an instrument artifact in previous studies. It was considered that evaporation exceeds collision breakup in convective rain, resulting in the concave shape of the size spectra (Sauvageot and Lacaux 1995; Atlas and Ulbrich 2000). However, as the environment of mei-yu in our study is very humid, it can somehow suppress the evaporative process. Moreover, a similar concave pattern can still be seen from the OTT-measured shallow rain size spectra (not shown). In this shallow rain, splashes should be negligible because of the light rain. An important foundation in OTT PARSIVEL data analysis is the “one drop at once” assumption, which means that it is rare for two particles to be in the beam at the same time (Yuter et al. 2006). However, there is a great probability for such a high concentration of small drops to pass through the sensor area as a juxtaposition of particles. Moreover, because of the 1D laser signal measurements method applied by the OTTs, a few small drops may be blocked by a large drop when passing through the sensor area, or the shadow of two or more raindrops overlap together and are thus mistakenly recognized as a larger drop. Therefore, the missed small drops by the OTTs should be attributed more to these instrumental limitations mentioned above than to the nature of the rain or other sources.

Despite its higher resolution and more accurate rainfall and DSD measurements, it remains to be demonstrated whether the high concentration of small drops by the 2DVD are due to rain at all and not partly from some noise or other effects, for example, splash effects. Note that the effect of splashing can be partially avoided by applying the filter criterion based on fall velocity (Tokay et al. 2001). While splashing during heavy rainfall could be important with all of the employed instruments, its effects on small drop concentrations are not considered in this study.

For midsize drops, it is expected that all measurements from the four instruments exhibited good agreement. The averaged DSD spectra of convective rain from MRR also showed good agreement with that of 2DVD (not shown), suggesting that the impact of updraft/downdraft on MRR spectra during convective rain should be minor in this study. For large drops, the sampling is limited where 2DVD has low concentrations and the MRR cannot distinguish between drops larger than 5.03 mm. The MRR shows slightly higher concentration of drops between 4 and 5 mm in diameter than other instruments. This may have resulted from the modified Doppler spectra produced by the rare occurrence of large drops and other effects, such as aliasing in particular during convective rain. For the OTTs, due to the likelihood of misclassifying overlapped drops as a larger drop, as well as a wider bin size (increase from 0.5 to 1 mm) for the raindrop diameter larger than 4 mm, the measurement uncertainties are quite high (e.g., Thurai et al. 2011; Tokay et al. 2013). For reference, the 2DVD bin size remains constant at 0.2 mm, while the MRR bin size is about 0.5 mm for large drops. Sphere calibration tests were conducted on site both before and after the field campaign for the 2DVD; the accuracy of drop size measurements is very high even when 8- and 10-mm spheres were used. As noticed before, the measurement of large drops from the 2DVD is the most accurate among the four instruments for rain DSD measurements.

c. Fall velocity measurements

The 2DVD and OTTs can also measure the fall velocity of hydrometeors. After binning the 2DVD size measurements using OTT-2 size bins, the mean and standard deviation of the raindrop fall velocities were calculated for each size bin and shown in Fig. 5. The Brandes terminal fall velocity (Brandes et al. 2002) is also depicted as a reference. The Atlas relationship (Atlas et al. 1973), which resolved DSDs from MRR Doppler spectra measurements, is plotted as well. Significant differences can be seen in fall velocity measurements from the three disdrometers. The OTT-1 fall velocities are higher than the Brandes fall speed at drop sizes less than 1.18 or larger than 3.47 mm, while OTT-2 shows an acceptable underestimation between them. The OTT-2 showed improvement for fall velocity measurements compared with the OTT-1, except for an up to 8% greater underestimation at midsize drops. Note that after the size correction, the OTT-1 fall velocities show a lower standard deviation than the OTT-2 velocities for large drops. The 2DVD showed the most accurate fall velocity measurements. For the drops larger than 1 mm, the difference between the 2DVD-measured and the Brandes terminal fall velocity is less than 2.7%, and the standard deviation is less than ±0.5 m s−1 except for drops at sizes 5.5 mm or larger. The standard deviation of fall velocity measured by the 2DVD and the OTTs increases as the drop size increases. This is due to the large variability of vertical wind in convective rain where large drops tend to present and other factors, such as the lower number of large drops, which can influence the statistics.

Fig. 5.
Fig. 5.

Mean and SD of measured fall velocity by 2DVD, OTT-1, and OTT-2 as a function of diameter. 2DVD data are adjusted to OTT-2 diameter bins.

Citation: Journal of Atmospheric and Oceanic Technology 34, 5; 10.1175/JTECH-D-16-0225.1

d. Distributions of raindrop parameters

The occurrence frequency of various integral parameters is derived from the measured DSDs to investigate the impact of the instrument measurements (Fig. 6). As shown in Fig. 6a, the Dm distribution curves for the 2DVD, OTT-1, and OTT-2 correspond closely with each other, while the MRR curve shows a larger percentage around 1.4–1.8 mm. Meanwhile, because of the inherent limitation of the MRR, it can detect reliable rain DSDs only between 0.246 and 5.03 mm, resulting in an absence of larger drops. Therefore, almost all of the Dm values computed from MRR data are below 2 mm. The distribution of σm (the standard deviation of the mass spectrum with respect to Dm) showed corresponding characteristics (Fig. 6d).

Fig. 6.
Fig. 6.

Occurrence frequencies of various parameters computed from the DSDs: (a) Dm (mm), (b) log10 Nt (m−3), (c) LWC (g m−3), and (d) σm (mm).

Citation: Journal of Atmospheric and Oceanic Technology 34, 5; 10.1175/JTECH-D-16-0225.1

The log10 Nt showed a more complicated distribution than the Dm (Fig. 6b). A relatively higher (lower) percentage when the log10 Nt value is higher (lower) than 2.7 indicates that the 2DVD has the highest number concentration of raindrops. The curves of the OTTs yield smaller values on the sizes. The LWC curve of the MRR was distinctly different (Fig. 6c); nearly 38% of the peak value appears at about 0.05 g m−3, which is the same value as the MRR-measured LWC for shallow rain (Table 4). Because of the low frequency of occurrence, these properties would be easily masked when considering the mean value for the whole dataset (Table 4). Nevertheless, as an aspect of rain DSDs, more attention should be paid to these differences, especially for the study of instrument performance on the detection of small drops.

Table 4.

Integral rain parameters derived from the DSDs for the three rain types by each instrument’s measurement. Parameters Nt, Nw, LWC, R, σm, Dm, and D0 are the total raindrop concentration, generalized raindrop concentration, liquid water content, rain rate, mass-weighted mean diameter, and maximum raindrop diameter, respectively.

Table 4.

Figure 7 shows scatterplots of Dm and log10 Nw for three rain types. The averaged values of Dm and log10 Nw (also see Table 4) measured by OTTs for convective rain matched well with the statistical result based on OTT observations in Nanjing during three mei-yu seasons (2009–11) reported by Chen et al. (2013). However, the 2DVD and MRR observations showed relatively lower values of Dm and higher values of Nw, and plotted roughly over the “maritime” cluster [gray rectangle in the top-right corner of Fig. 7 as reported by Bringi et al. (2003)]. This should be attributed to the underestimation of the number of mid- to small-size drops by the OTTs compared with 2DVD (Fig. 4), which are especially critical during heavy rainfall.

Fig. 7.
Fig. 7.

Scatterplot of averaged log10 Nw vs Dm for convective (square box), stratiform (triangle), and shallow (cross) rain types, and for the whole dataset (dots). Red, blue, green, black, and pink symbol represent the averaged values for the 2DVD, MRR, OTT-1, OTT-2, and Chen et al. (2013), respectively. Two gray rectangles correspond to the maritime and continental convective clusters, respectively, reported by Bringi et al. (2003). Cyan dashed line is that of Bringi et al. (2003) stratiform rain.

Citation: Journal of Atmospheric and Oceanic Technology 34, 5; 10.1175/JTECH-D-16-0225.1

For stratiform rain, the OTTs still measured a slightly lower value of Nw than 2DVD, but the differences are relatively small when compared to those of convective rain. As the stratiform rain has the highest population among the three rain types during the mei-yu season, the NwDm pair for the whole dataset (circles) is also close to the stratiform line reported by Bringi et al. (2003; cyan dashed line). Compared with stratiform and convective rain, shallow rain has the lowest value of Dm but the highest value of Nw. The variation of the Dm value for the shallow rain among the four instruments is small, but that of the Nw value varies in a wide range. The 2DVD showed the highest number concentration, followed by the OTT-2 and the MRR, and the OTT-1 had the lowest Nw value. The performance of the shallow rain detection from the four instruments shows that the 2DVD can detect more small drops than the OTTs and the MRR.

4. Effects of DSD measurements on radar QPE and model microphysical processes

Because of the difference in instruments’ measurement mechanisms, sensor inaccuracies, and other sources of errors, the measurements of both small and large drops disagree between the four instruments, as shown in the analyses above. We will discuss the instrumentation effects of DSD measurements on radar QPE and model microphysical processes.

a. Effects on derived ZR relation

Disdrometer measurements are often used to derive relationships between radar reflectivity and rainfall rate. Previous studies showed that the derived ZR relationships from different instruments exhibit significant variations, indicating a strong dependence on sensor type (Campos and Zawadzki 2000; Tokay et al. 2001; Lee and Zawadzki 2005; Sarkar et al. 2015), and ultimately lead to inaccurate rainfall estimation from radar. Similar results were obtained in our study, as shown in Fig. 8a. The ZR relationship for the 2DVD has an in-between coefficient but a smaller exponent (slope) than the OTTs. Large differences occur at heavy rain rates, and for a particular reflectivity the estimated rainfall rate is much higher using the 2DVD ZR relationship. For example, at a reflectivity of 50 dBZ, rain rate with the 2DVD relation is 43.8% (51.5%) higher than that with the OTT-2 (OTT-1) relation. This indicated that missing smaller drops will cause heavy rain rates to be underestimated with the ZR relationship derived from OTT observations during the mei-yu seasons. On the other hand, because of the uncertainty of rainfall and DSD measurements during heavy rainfall, the MRR relation estimated rain rate is 61.5% higher than the 2DVD relation at 50 dBZ. These inherit differences will be important in flash flood situations. The comparison of the 2DVD-observed R and the R derived from the corresponding ZR relation for the whole data also shows the highest correlation, followed by the OTTs, while that of the MRR has the highest deviation (figure not shown). The correlation coefficients between estimated and observed Rs are 0.98, 0.923, 0.936, and 0.94 for 2DVD, MRR, OTT-1, and OTT-2, respectively.

Fig. 8.
Fig. 8.

(a) Derived ZR relations from two mei-yu season observations for the four instruments with different colors. (b)–(e) Comparison of rain gauge observed and ZR relation estimated hourly rain totals for mei-yu rainfall.

Citation: Journal of Atmospheric and Oceanic Technology 34, 5; 10.1175/JTECH-D-16-0225.1

Similar to Fig. 2, the comparison of RG observed hourly rainfall and rainfall derived from the corresponding ZR relation for the whole dataset are presented in Figs. 8b–e. General agreements can be found between estimated and observed rainfall totals among different disdrometers. The estimated rain total from the MRR-derived ZR relation showed the highest absolute bias, which can be attributed to the large variability in rainfall measurement and the relatively large uncertainties of MRR observation in convective rain. Therefore, the MRR-derived relation is not suitable for radar QPE. With lower standard deviations and biases, the performance of the OTTs’ rainfall estimation is better than that of the MRR. Not surprisingly, because it produced the most accurate rainfall and DSD measurements, the 2DVD-derived ZR relation resulted in rainfall measurements that had the best agreement with the RG. It is worth noting that the ZR relations in this study are obtained from a few months of data, which allows the results to be used for comparison purposes. However, a larger dataset over a longer period of time is needed to provide relations representative of the climatology of the area.

b. Effects on microphysics parameterization

Previous studies (Gilmore et al. 2004; Zhang et al. 2006; Snook and Xue 2008) reveal that the simulated storm structure, evolution, and precipitation are highly sensitive to microphysics parameterization. Gilmore et al. (2004) found that variations in microphysical parameters within the observed range of uncertainty can cause significant changes in precipitation and the intensity of simulated storms. Further, Snook and Xue (2008) demonstrated that, to focus on cold pool effects (which largely influenced low-level storm dynamics), the evaporation of rain is the dominant contributor to cooling. They also found that larger raindrops fall faster and have less total surface area than many smaller ones containing the same water content, resulting in less evaporation and cooling, and ultimately a smaller, weaker cold pool. That means the accurate measurements and representation of raindrop concentration are in desperate need of numerical simulations. While the DSDs observed using different instruments show a wide range of variation (especially at small drop sizes), it is vital to obtain a better understanding of model sensitivity to these instrumental differences.

As shown in the abovementioned analysis, the observed small drop concentration shows significant differences among the different instruments. That is to say, small drop issues would also influence derived microphysical processes, such as evaporation and accretion. The relative impact of reduced small drop populations on evaporation and accretion rates, both of which are important microphysical processes in numerical weather prediction, can be estimated using simple Kessler (1969) parameterizations as in Zhang et al. (2006, 2011). Assuming an evaporation coefficient Ee = 1 and accretion coefficient Ec = 1, the evaporation rate of a water drop at a saturation deficit me (for simplicity assumed to be 1.0 g m−3) is given by
e6
The total evaporation rate for an ensemble of drops is
e7
Similarly, the accretion of cloud water, assuming a collection efficiency of 1.0, is
e8
where mc is the cloud water content (taken to be 1.0 g m−3) and N(D) is the observed drop concentration.

Figure 9 shows the kernel smoothing function estimate of 1-min evaporation and accretion rates for all categorized datasets from the four instruments. As would be expected, differences in the observed DSDs (especially at small drop concentrations) have a pronounced effect on these two parameters. The mean value of evaporation rates were nearly a factor of 1.4 smaller for the MRR and OTT-2, and more than 2 times smaller for the OTT-1 when compared to that of the 2DVD (Fig. 9a). Similarly, the OTTs and MRR computed mean accretion rates show 0.5–1 times smaller than that of the 2DVD’s (Fig. 9b). To quantitatively evaluate the instrumentation effects on small drop detection during heavy rainfall and their reflection in estimating Re and Rc, Fig. 10 presents the time series of DSDs as well as several computed parameters for a convective rainfall in 12 July 2014. One can see that the 2DVD consistently showed the highest number concentration for drop sizes smaller than 1 mm, followed by the MRR and OTT-2; the OTT-1 has the lowest concentration (Fig. 10a). Large drops are rare but the OTTs tend to measure them with a higher frequency, along with a larger maximum drop size than the 2DVD.

Fig. 9.
Fig. 9.

Kernel smoothing function estimate of estimated evaporation and accretion rates computed from collocated measurements.

Citation: Journal of Atmospheric and Oceanic Technology 34, 5; 10.1175/JTECH-D-16-0225.1

Fig. 10.
Fig. 10.

Time series of (a) observed DSD and (b) DSD attributes, as well as estimated evaporation and accretion rates computed from collocated measurements. Vertical gray line and data are for 0938 LST 12 Jul 2014.

Citation: Journal of Atmospheric and Oceanic Technology 34, 5; 10.1175/JTECH-D-16-0225.1

The computed physical quantities (Fig. 10b) show dramatic differences for Nt, Re, and Rc between the four instruments, especially during heavy rainfall, but resulted in similar radar reflectivity ZH. Note that the zeroth-order moment of the DSD is the drop counts Nt, while rain rate approximates to the 3.67th order and ZH is the sixth-order moment under the Rayleigh assumption. Since rare large drops contribute substantially to ZH, the influence of the many missed small drops on the ZH may consequently be masked by the overestimation of a few large drops. To reveal this issue more clearly, the parameters estimated from a 1-min raw sample are presented as well—that is, 0938 LST 12 July 2014—and denoted by the vertical gray line in Fig. 10b.

At 0938 LST, the 2DVD-estimated rain rate is 144.3 mm h−1. Although the OTT-2-estimated ZH is about 1 dB higher than that from the 2DVD because of an overestimation of large drops, the rain rate, however, shows a 30% underestimation (103.5 mm h−1) due to many missing small drops. These differences are too large to ignore and would certainly affect the accuracy of QPE, especially during heavy rainfall. Benefitting from the more accurate measurement of the large number of small drops by the 2DVD, the corresponding Nt value is 89 291 m−3, which is about a factor of 6, 31, and 20 higher than that of the MRR, OTT-1, and OTT-2, respectively. As a result, the gap in the estimated evaporation rates and accretion rates between the 2DVD and the other instruments became (2–3 times) much wider at 0938 LST than that of the mean values in Fig. 9.

According to Snook and Xue (2008), such differences would influence the modeling of the development of cold pools by evaporation, thereby altering the motion of predicted storms. Consistent results have also been documented by Zheng and Chen (2014) for the simulation of a supercell storm during mei-yu season in east China. Therefore, numerically simulated thunderstorms using the 2DVD-derived DSD model, which contains a much higher concentration of small raindrops, would have a larger areal extent of precipitation and total surface area that is covered, thus enhancing evaporative cooling. As a result, the 2DVD-derived DSD model would have stronger cold pools and outflows than those initialized with the OTT- or MRR-derived DSD model, and they would probably propagate differently as a result of different cold pool/outflow strength. As revealed in Zheng and Chen (2014), for idealized simulations of supercell storms during mei-yu season, a stronger cold pool leads to a strong and surging gust front. No doubt that the cold pool intensity is not the only factor that influences the prediction of storm evolution, but this topic is out of the scope of this study.

While previous studies demonstrated that the microphysical parameterization has pronounced effects on numerically simulated storms, our study reveals that the instrumental differences and/or error have substantial impacts on the tuning of model microphysics. Therefore, to improve the accuracy of microphysical parameterization and ultimately the accuracy of storm simulations, obtaining more accurate DSDs from observations is critical and essential. This study demonstrated that the microphysical parameterization schemes derived from disdrometers other than 2DVD would contain significant errors in the absence of abundant small drops, especially during the mei-yu seasons in east China.

5. Summary and discussion

In this paper, instrumentation limitations on estimated DSD, radar QPE, and model microphysical processes have been examined using four collocated instruments—that is, a 2DVD, an MRR, and two OTT PARSIVELs—during the 2014 and 2015 mei-yu seasons. Significant differences in the measurements by those four instruments have been demonstrated for derived DSD properties and integral physical parameters. The results of the instrument comparisons can be used to address questions related to disdrometer applications and measurement uncertainty, as well as numerically simulated microphysical properties.

Instrumental errors will lead to inaccurate and misleading DSD measurements. The ability and inability for different instruments to detect small drops have been shown through the analyses in this paper. One shortcoming of the OTT PARSIVEL is the underestimation of small drops with a sharp drop-off occurring toward smaller sizes after peak concentrations. This could be attributed to the 1D laser signal measurements method and the “one drop at once” assumption in data analysis. Both of them lead to the underestimation of the number of mid- to small-size drops, and ultimately resulted in relatively higher Dm and lower Nw as showed in several previous studies. The missing of small drops also causes heavy rainfall to be underestimated with the ZR relationship derived from OTT observations. The OTT-2 provided significant improvements over OTT-1, especially for the observation of both light and heavy rain.

Because of its higher measurement height and the retrieval method applied, the MRR showed the highest variability for rainfall and DSD observations. However, for midsize drops, the averaged DSD spectra from MRR and 2DVD showed good agreement with each other (even in convective rain). At large drop sizes, the MRR shows a slightly higher concentration of drops between 4 and 5 mm in diameter. This may have resulted from the modified Doppler spectra produced by the rare occurrence of large drops and the aliasing effect. Because of the large variability in rainfall measurement and the relatively large uncertainties of MRR observation in convective rain, we do not recommend the use of the MRR-derived ZR relation for radar QPE.

The 2DVD provides the most accurate rainfall, DSD, and drop fall velocity measurements among the four instruments, which is consistent with previous studies. Therefore, the estimated rainfall using the 2DVD-derived ZR relation showed the best agreement with RG observations. The impact of missed small drops on model microphysical processes such as evaporation and accretion has been examined as well. As expected, differences in the observed DSDs have a pronounced effect on estimated evaporation and accretion rates. Such differences would influence the development of cold pools by evaporation, thus altering the evolution of simulated storms and their produced precipitation. To improve the accuracy of microphysical parameterization and ultimately the accuracy of storm simulations, obtaining more accurate DSDs from observations is a feasible and valid method. Among the four instruments, the 2DVD is the most appropriate one for this purpose.

It should be noted that this study is mainly focused on the specific mei-yu precipitation microphysics in east China. The variability of DSDs across different climate regimes or seasons may lead to different results. The purpose of this study is not to belittle the OTT or the importance of the numerous studies conducted with it. Our knowledge of DSDs and the ability to deduce storm microphysical properties are limited by the instruments available to us. We believe instrumental limitation deficiencies have not been fully explored and appreciated when the data are used to describe the microphysical properties of storms. The numerical weather forecasting community has realized the importance of detailed DSD descriptions in models and begun to use sophisticated two- and three-moment microphysics schemes (such as Morrison et al. 2009; Milbrandt and Yau 2006a,b). Such schemes must be based on and validated by reliable observations not subject to instrumentation peculiarities. Progress will entail comprehensive instrument intercomparisons and perhaps the development of new sensors. Moreover, along with the wide installation of OTT and MRR in China, a full understanding of the instrument characteristics and shortcomings and developing the new algorithm to improve the quality of the DSDs retrieved from these instruments are desirable and of great importance for quantitative precipitation estimation and forecast in this specific region.

Acknowledgments

This work was primarily supported by the National Natural Science Foundation of China (Grants 41475015, 41275031, and 41322032), the Social Common Wealth Research Program (GYHY201006007), and the Program for New Century Excellent Talents in Universities of China (NCET-13-0287), and observational data used in this study were collected by a National 973 Project (2013CB430101). Because of Nanjing University’s data policy, the DSD and MRR data cannot be released to the public until 2018. Special requests for the data can be made online (at http://scw973.nju.edu.cn/) or by contacting the project office at yang.zhengwei@nju.edu.cn. After 2018, the data this paper will be uploaded to the National Energy Research Scientific Computing Center website (at http://www.nersc.gov/users/science-gate-ways/) for public sharing.

REFERENCES

  • Adirosi, E., L. Baldini, F. Lombardo, F. Russo, F. Napolitano, E. Volpi, and A. Tokay, 2015: Comparison of different fittings of drop spectra for rainfall retrievals. Adv. Water Resour., 83, 5567, doi:10.1016/j.advwatres.2015.05.009.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Adirosi, E., L. Baldini, N. Roberto, P. Gatlin, and A. Tokay, 2016: Improvement of vertical profiles of raindrop size distribution from micro rain radar using 2D video disdrometer measurements. Atmos. Res., 169, 404415, doi:10.1016/j.atmosres.2015.07.002.

    • 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, doi:10.1175/1520-0450(2001)040<2165:AOBCMO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Atlas, D., R. Srivastava, and R. S. Sekhon, 1973: Doppler radar characteristics of precipitation at vertical incidence. Rev. Geophys., 11, 135, doi:10.1029/RG011i001p00001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Battaglia, A., E. Rustemeier, A. Tokay, U. Blahak, and C. Simmer, 2010: PARSIVEL snow observations: A critical assessment. J. Atmos. Oceanic Technol., 27, 333344, doi:10.1175/2009JTECHA1332.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brandes, E. A., G. Zhang, and J. Vivekanandan, 2002: Experiments in rainfall estimation with a polarimetric radar in a subtropical environment. J. Appl. Meteor., 41, 674685, doi:10.1175/1520-0450(2002)041<0674:EIREWA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brandes, E. A., G. Zhang, and J. Vivekanandan, 2003: An evaluation of a drop distribution–based polarimetric radar rainfall estimator. J. Appl. Meteor., 42, 652660, doi:10.1175/1520-0450(2003)042<0652:AEOADD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brandes, E. A., G. Zhang, and J. Vivekanandan, 2004: Drop size distribution retrieval with polarimetric radar: Model and application. J. Appl. Meteor., 43, 461475, doi:10.1175/1520-0450(2004)043<0461:DSDRWP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brandes, E. A., G. Zhang, and J. Sun, 2006: On the influence of assumed drop size distribution form on radar-retrieved thunderstorm microphysics. J. Appl. Meteor. Climatol., 45, 259268, doi:10.1175/JAM2335.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bringi, V., V. Chandrasekar, J. Hubbert, E. Gorgucci, W. L. Randeu, and M. Schoenhuber, 2003: Raindrop size distribution in different climatic regimes from disdrometer and dual-polarized radar analysis. J. Atmos. Sci., 60, 354365, doi:10.1175/1520-0469(2003)060<0354:RSDIDC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Campos, E., and I. Zawadzki, 2000: Instrumental uncertainties in ZR relations. J. Appl. Meteor., 39, 10881102, doi:10.1175/1520-0450(2000)039<1088:IUIZRR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cao, Q., and G. Zhang, 2009: Errors in estimating raindrop size distribution parameters employing disdrometer and simulated raindrop spectra. J. Appl. Meteor. Climatol., 48, 406425, doi:10.1175/2008JAMC2026.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cao, Q., G. Zhang, E. Brandes, T. Schuur, A. Ryzhkov, and K. Ikeda, 2008: Analysis of video disdrometer and polarimetric radar data to characterize rain microphysics in Oklahoma. J. Appl. Meteor. Climatol., 47, 22382255, doi:10.1175/2008JAMC1732.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Carbone, R., J. Tuttle, D. Ahijevych, and S. Trier, 2002: Inferences of predictability associated with warm season precipitation episodes. J. Atmos. Sci., 59, 20332056, doi:10.1175/1520-0469(2002)059<2033:IOPAWW>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, B., J. Yang, and J. Pu, 2013: Statistical characteristics of raindrop size distribution in the Meiyu season observed in eastern China. J. Meteor. Soc. Japan, 91, 215227, doi:10.2151/jmsj.2013-208.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ding, Y., and J. C. Chan, 2005: The East Asian summer monsoon: An overview. Meteor. Atmos. Phys., 89, 117142, doi:10.1007/s00703-005-0125-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gilmore, M. S., J. M. Straka, and E. N. Rasmussen, 2004: Precipitation uncertainty due to variations in precipitation particle parameters within a simple microphysics scheme. Mon. Wea. Rev., 132, 26102627, doi:10.1175/MWR2810.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Habib, E., W. F. Krajewski, and A. Kruger, 2001: Sampling errors of tipping-bucket rain gauge measurements. J. Hydrol. Eng., 6, 159166, doi:10.1061/(ASCE)1084-0699(2001)6:2(159).

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jaffrain, J., A. Studzinski, and A. Berne, 2011: A network of disdrometers to quantify the small‐scale variability of the raindrop size distribution. Water Resour. Res., 47, W00H06, doi:10.1029/2010WR009872.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Joss, J., and A. Waldvogel, 1969: Raindrop size distribution and sampling size errors. J. Atmos. Sci., 26, 566569, doi:10.1175/1520-0469(1969)026<0566:RSDASS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kessler, E., 1969: On the Distribution and Continuity of Water Substance in Atmospheric Circulation. Meteor. Monogr., No. 32, Amer. Meteor. Soc., 84 pp.

    • Crossref
    • Export Citation
  • Kliche, D. V., P. L. Smith, and R. W. Johnson, 2008: L-moment estimators as applied to gamma drop size distributions. J. Appl. Meteor. Climatol., 47, 31173130, doi:10.1175/2008JAMC1936.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krajewski, W. F., and et al. , 2006: DEVEX-disdrometer evaluation experiment: Basic results and implications for hydrologic studies. Adv. Water Resour., 29, 311325, doi: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, doi:10.1175/1520-0426(2002)019<0602:TDVDAD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lee, G. W., and I. Zawadzki, 2005: Variability of drop size distributions: Time-scale dependence of the variability and its effects on rain estimation. J. Appl. Meteor., 44, 241255, doi:10.1175/JAM2183.1.

    • 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, doi:10.1175/1520-0426(2000)017<0130:AODFMS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mallet, C., and L. Barthes, 2009: Estimation of gamma raindrop size distribution parameters: Statistical fluctuations and estimation errors. J. Atmos. Oceanic Technol., 26, 15721584, doi:10.1175/2009JTECHA1199.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Milbrandt, J., and M. Yau, 2006a: A multimoment bulk microphysics parameterization. Part III: Control simulation of a hailstorm. J. Atmos. Sci., 63, 31143136, doi:10.1175/JAS3816.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Milbrandt, J., and M. Yau, 2006b: A multimoment bulk microphysics parameterization. Part IV: Sensitivity experiments. J. Atmos. Sci., 63, 31373159, doi:10.1175/JAS3817.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morrison, H., G. Thompson, and V. Tatarskii, 2009: Impact of cloud microphysics on the development of trailing stratiform precipitation in a simulated squall line: Comparison of one-and two-moment schemes. Mon. Wea. Rev., 137, 9911007, doi:10.1175/2008MWR2556.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nemeth, K., and M. Löffler-Mang, 2006: OTT Parsivel—Enhanced precipitation identifier and new generation of present weather sensor by OTT Messtechnik, Germany. Preprints, Fourth Int. Conf. on Experiences with Automatic Weather Stations (ICEAWS), Lisbon, Portugal, WMO, 2. [Available online at http://projects.knmi.nl/geoss/ICEAWS/ICEAWS-4/CD/docs/ORAL/2_oral.pdf.]

  • Nešpor, V., W. F. Krajewski, and A. Kruger, 2000: Wind-induced error of raindrop size distribution measurement using a two-dimensional video disdrometer. J. Atmos. Oceanic Technol., 17, 14831492, doi:10.1175/1520-0426(2000)017<1483:WIEORS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peters, G., B. Fischer, and T. Andersson, 2002: Rain observations with a vertically looking Micro Rain Radar (MRR). Boreal Environ. Res., 7, 353362.

    • Search Google Scholar
    • Export Citation
  • Peters, G., B. Fischer, H. Münster, M. Clemens, and A. Wagner, 2005: Profiles of raindrop size distributions as retrieved by microrain radars. J. Appl. Meteor., 44, 19301949, doi:10.1175/JAM2316.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peters, G., B. Fischer, and M. Clemens, 2010: Rain attenuation of radar echoes considering finite-range resolution and using drop size distributions. J. Atmos. Oceanic Technol., 27, 829842, doi:10.1175/2009JTECHA1342.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Raupach, T., and A. Berne, 2015: Correction of raindrop size distributions measured by Parsivel disdrometers, using a two-dimensional video disdrometer as a reference. Atmos. Meas. Tech., 8, 343365, doi:10.5194/amt-8-343-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sarkar, T., S. Das, and A. Maitra, 2015: Assessment of different raindrop size measuring techniques: Inter-comparison of Doppler radar, impact and optical disdrometer. Atmos. Res., 160, 1527, doi:10.1016/j.atmosres.2015.03.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sauvageot, H., and J.-P. Lacaux, 1995: The shape of averaged drop size distributions. J. Atmos. Sci., 52, 10701083, doi:10.1175/1520-0469(1995)052<1070:TSOADS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schönhuber, M., H. Urban, J. P. V. P. Baptista, W. Randeu, and W. Riedler, 1997: Weather radar versus 2D-video-disdrometer data. Weather Radar Technology for Water Resources Management, B. P. F. Bragg Jr. and O. Massambani, Eds., Unesco Press, 159–171.

  • Sheppard, B. E., 1990: Measurement of raindrop size distributions using a small Doppler radar. J. Atmos. Oceanic Technol., 7, 255268, doi:10.1175/1520-0426(1990)007<0255:MORSDU>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sheppard, B. E., and P. I. Joe, 1994: Comparison of raindrop size distribution measurements by a Joss-Waldvogel disdrometer, a PMS 2DG spectrometer, and a POSS Doppler radar. J. Atmos. Oceanic Technol., 11, 874887, doi:10.1175/1520-0426(1994)011<0874:CORSDM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, P. L., 2003: Raindrop size distributions: Exponential or gamma—Does the difference matter? J. Appl. Meteor., 42, 10311034, doi:10.1175/1520-0450(2003)042<1031:RSDEOG>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, P. L., and D. V. Kliche, 2005: The bias in moment estimators for parameters of drop size distribution functions: Sampling from exponential distributions. J. Appl. Meteor., 44, 11951205, doi:10.1175/JAM2258.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, P. L., Z. Liu, and J. Joss, 1993: A study of sampling-variability effects in raindrop size observations. J. Appl. Meteor., 32, 12591269, doi:10.1175/1520-0450(1993)032<1259:ASOSVE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, P. L., D. V. Kliche, and R. W. Johnson, 2009: The bias and error in moment estimators for parameters of drop size distribution functions: Sampling from gamma distributions. J. Appl. Meteor. Climatol., 48, 21182126, doi:10.1175/2009JAMC2114.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Snook, N., and M. Xue, 2008: Effects of microphysical drop size distribution on tornadogenesis in supercell thunderstorms. Geophys. Res. Lett., 35, L24803, doi:10.1029/2008GL035866.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thurai, M., and V. Bringi, 2005: Drop axis ratios from a 2D video disdrometer. J. Atmos. Oceanic Technol., 22, 966978, doi:10.1175/JTECH1767.1.

  • Thurai, M., W. Petersen, A. Tokay, C. Schultz, and P. Gatlin, 2011: Drop size distribution comparisons between Parsivel and 2-D video disdrometers. Adv. Geosci., 30, 39, doi:10.5194/adgeo-30-3-2011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tokay, A., and D. A. Short, 1996: Evidence from tropical raindrop spectra of the origin of rain from stratiform versus convective clouds. J. Appl. Meteor., 35, 355371, doi:10.1175/1520-0450(1996)035<0355:EFTRSO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tokay, A., and P. G. Bashor, 2010: An experimental study of small-scale variability of raindrop size distribution. J. Appl. Meteor. Climatol., 49, 23482365, doi:10.1175/2010JAMC2269.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tokay, A., A. Kruger, and W. F. Krajewski, 2001: Comparison of drop size distribution measurements by impact and optical disdrometers. J. Appl. Meteor., 40, 20832097, doi:10.1175/1520-0450(2001)040<2083:CODSDM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tokay, A., P. Hartmann, A. Battaglia, K. S. Gage, W. L. Clark, and C. R. Williams, 2009: A field study of reflectivity and ZR relations using vertically pointing radars and disdrometers. J. Atmos. Oceanic Technol., 26, 11201134, doi:10.1175/2008JTECHA1163.1.

    • 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, doi:10.1175/JTECH-D-12-00163.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tokay, A., D. B. Wolff, and W. A. Petersen, 2014: Evaluation of the new version of the laser-optical disdrometer, OTT Parsivel2. J. Atmos. Oceanic Technol., 31, 12761288, doi:10.1175/JTECH-D-13-00174.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ulbrich, C. W., 1983: Natural variations in the analytical form of the raindrop size distribution. J. Climate Appl. Meteor., 22, 17641775, doi:10.1175/1520-0450(1983)022<1764:NVITAF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wen, L., S. Liu, K. Zhao, Y. Li, and L. Li, 2015: Precision evaluation of micro rain radar observation in two precipitation events (in Chinese). Meteor. Mon., 41, 577587, doi:10.7519/j.issn.1000-0526.2015.05.006.

    • Search Google Scholar
    • Export Citation
  • Wen, L., K. Zhao, G. Zhang, M. Xue, B. Zhou, S. Liu, and X. Chen, 2016: Statistical characteristics of raindrop size distributions observed in East China during the Asian summer monsoon season using 2-D video disdrometer and Micro Rain Radar data. J. Geophys. Res. Atmos., 121, 22652282, doi:10.1002/2015JD024160.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yuter, S. E., D. E. Kingsmill, L. B. Nance, and M. Löffler-Mang, 2006: Observations of precipitation size and fall speed characteristics within coexisting rain and wet snow. J. Appl. Meteor. Climatol., 45, 14501464, doi:10.1175/JAM2406.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, G., J. Vivekanandan, and E. Brandes, 2001: A method for estimating rain rate and drop size distribution from polarimetric radar measurements. IEEE Trans. Geosci. Remote Sens., 39, 830841, doi:10.1109/36.917906.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, G., J. Vivekanandan, E. A. Brandes, R. Meneghini, and T. Kozu, 2003: The shape–slope relation in observed gamma raindrop size distributions: Statistical error or useful information? J. Atmos. Oceanic Technol., 20, 11061119, doi:10.1175/1520-0426(2003)020<1106:TSRIOG>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, G., J. Sun, and E. A. Brandes, 2006: Improving parameterization of rain microphysics with disdrometer and radar observations. J. Atmos. Sci., 63, 12731290, doi:10.1175/JAS3680.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, G., E. Brandes, and K. Iketa, 2011: Instrumentation effects on estimated drop size distribution and radar parameters. 35th Conf. on Radar Meteorology, Pittsburgh, PA, Amer. Meteor. Soc., 35. [Available online at https://ams.confex.com/ams/35Radar/webprogram/Paper191853.html.]

  • Zhao, K., and M. Xue, 2015: Preliminary results from the field experiment of OPACC. Geophysical Research Abstracts, Vol. 17, Abstract EGU2015-4420. [Available online at http://meetingorganizer.copernicus.org/EGU2015/EGU2015-4420.pdf.]

  • Zheng, K., and B. Chen, 2014: Sensitivities of tornadogenesis to drop size distribution in a simulated subtropical supercell over eastern China. Adv. Atmos. Sci., 31, 657668, doi:10.1007/s00376-013-3143-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
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