Idealized Simulations of a Squall Line from the MC3E Field Campaign Applying Three Bin Microphysics Schemes: Dynamic and Thermodynamic Structure

Lulin Xue National Center for Atmospheric Research, Boulder, Colorado

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Jiwen Fan Pacific Northwest National Laboratory, Richland, Washington

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Zachary J. Lebo University of Wyoming, Laramie, Wyoming

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Wei Wu University of Illinois at Urbana–Champaign, Urbana, Illinois
National Center for Atmospheric Research, Boulder, Colorado

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Hugh Morrison National Center for Atmospheric Research, Boulder, Colorado

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Wojciech W. Grabowski National Center for Atmospheric Research, Boulder, Colorado

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Xia Chu University of Wyoming, Laramie, Wyoming

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István Geresdi University of Pécs, Pécs, Hungary

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Kirk North McGill University, Montréal, Québec, Canada

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Ronald Stenz University of North Dakota, Grand Forks, North Dakota

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Yang Gao Pacific Northwest National Laboratory, Richland, Washington

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Xiaofeng Lou Chinese Academy of Meteorological Sciences, Beijing, China

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Aaron Bansemer National Center for Atmospheric Research, Boulder, Colorado

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Andrew J. Heymsfield National Center for Atmospheric Research, Boulder, Colorado

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Greg M. McFarquhar University of Illinois at Urbana–Champaign, Urbana, Illinois
National Center for Atmospheric Research, Boulder, Colorado

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Roy M. Rasmussen National Center for Atmospheric Research, Boulder, Colorado

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Abstract

The squall-line event on 20 May 2011, during the Midlatitude Continental Convective Clouds (MC3E) field campaign has been simulated by three bin (spectral) microphysics schemes coupled into the Weather Research and Forecasting (WRF) Model. Semi-idealized three-dimensional simulations driven by temperature and moisture profiles acquired by a radiosonde released in the preconvection environment at 1200 UTC in Morris, Oklahoma, show that each scheme produced a squall line with features broadly consistent with the observed storm characteristics. However, substantial differences in the details of the simulated dynamic and thermodynamic structure are evident. These differences are attributed to different algorithms and numerical representations of microphysical processes, assumptions of the hydrometeor processes and properties, especially ice particle mass, density, and terminal velocity relationships with size, and the resulting interactions between the microphysics, cold pool, and dynamics. This study shows that different bin microphysics schemes, designed to be conceptually more realistic and thus arguably more accurate than bulk microphysics schemes, still simulate a wide spread of microphysical, thermodynamic, and dynamic characteristics of a squall line, qualitatively similar to the spread of squall-line characteristics using various bulk schemes. Future work may focus on improving the representation of ice particle properties in bin schemes to reduce this uncertainty and using the similar assumptions for all schemes to isolate the impact of physics from numerics.

© 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: Lulin Xue, xuel@ucar.edu

Abstract

The squall-line event on 20 May 2011, during the Midlatitude Continental Convective Clouds (MC3E) field campaign has been simulated by three bin (spectral) microphysics schemes coupled into the Weather Research and Forecasting (WRF) Model. Semi-idealized three-dimensional simulations driven by temperature and moisture profiles acquired by a radiosonde released in the preconvection environment at 1200 UTC in Morris, Oklahoma, show that each scheme produced a squall line with features broadly consistent with the observed storm characteristics. However, substantial differences in the details of the simulated dynamic and thermodynamic structure are evident. These differences are attributed to different algorithms and numerical representations of microphysical processes, assumptions of the hydrometeor processes and properties, especially ice particle mass, density, and terminal velocity relationships with size, and the resulting interactions between the microphysics, cold pool, and dynamics. This study shows that different bin microphysics schemes, designed to be conceptually more realistic and thus arguably more accurate than bulk microphysics schemes, still simulate a wide spread of microphysical, thermodynamic, and dynamic characteristics of a squall line, qualitatively similar to the spread of squall-line characteristics using various bulk schemes. Future work may focus on improving the representation of ice particle properties in bin schemes to reduce this uncertainty and using the similar assumptions for all schemes to isolate the impact of physics from numerics.

© 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: Lulin Xue, xuel@ucar.edu
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  • Beheng, K. D., 1987: Microphysical properties of glaciating cumulus clouds: Comparison of measurements with a numerical simulation. Quart. J. Roy. Meteor. Soc., 113, 13771382, https://doi.org/10.1002/qj.49711347815.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Berry, E. X., 1967: Cloud droplet growth by collection. J. Atmos. Sci., 24, 688701, https://doi.org/10.1175/1520-0469(1967)024<0688:CDGBC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Berry, E. X., and R. L. Reinhardt, 1974: An analysis of cloud drop growth by collection: Part I. Double distributions. J. Atmos. Sci., 31, 18141824, https://doi.org/10.1175/1520-0469(1974)031<1814:AAOCDG>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bigg, E. K., 1953: The formation of atmospheric ice crystals by the freezing of droplets. Quart. J. Roy. Meteor. Soc., 79, 510519, https://doi.org/10.1002/qj.49707934207.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Blahak, U., 2007: RADAR_MIE_LM and RADAR_MIELIB—Calculation of radar reflectivity from model output. KIT Institute for Meteorology and Climate Research Internal Rep., 150 pp.

  • Bleck, R., 1970: A fast approximative method for integrating the stochastic coalescence equation. J. Geophys. Res., 75, 51655171, https://doi.org/10.1029/JC075i027p05165.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bryan, G. H., and H. Morrison, 2012: Sensitivity of a simulated squall line to horizontal resolution and parameterization of microphysics. Mon. Wea. Rev., 140, 202225, https://doi.org/10.1175/MWR-D-11-00046.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bryan, G. H., and R. Rotunno, 2014: The optimal state for gravity currents in shear. J. Atmos. Sci., 71, 448468, https://doi.org/10.1175/JAS-D-13-0156.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bryan, G. H., J. C. Knievel, and M. D. Parker, 2006: A multimodel assessment of RKW theory’s relevance to squall-line characteristics. Mon. Wea. Rev., 134, 27722792, https://doi.org/10.1175/MWR3226.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, Y.-C., L. Xue, Z. J. Lebo, H. Wang, R. M. Rasmussen, and J. H. Seinfeld, 2011: A comprehensive numerical study of aerosol-cloud-precipitation interactions in marine stratocumulus. Atmos. Chem. Phys., 11, 97499769, https://doi.org/10.5194/acp-11-9749-2011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Collis, S., A. Protat, P. T. May, and C. Williams, 2013: Statistics of storm updraft velocities from TWP-ICE including verification with profiling measurements. J. Appl. Meteor. Climatol., 52, 19091922, https://doi.org/10.1175/JAMC-D-12-0230.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cooper, W. A., 1986: Ice initiation in natural clouds. Precipitation Enhancement—A Scientific Challenge, Meteor. Monogr., No. 21, Amer. Meteor. Soc., 29–32.

    • Crossref
    • Export Citation
  • Fan, J., L. R. Leung, Z. Li, H. Morrison, H. Chen, Y. Zhou, Y. Qian, and Y. Wang, 2012: Aerosol impacts on clouds and precipitation in eastern China: Results from bin and bulk microphysics. J. Geophys. Res., 117, D00K36, https://doi.org/10.1029/2011JD016537.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fan, J., and Coauthors, 2015: Improving representation of convective transport for scale-aware parameterization: 1. Convection and cloud properties simulated with spectral bin and bulk microphysics. J. Geophys. Res. Atmos., 120, 34853509, https://doi.org/10.1002/2014JD022142.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fan, J., and Coauthors, 2017: Cloud-resolving model intercomparison of an MC3E squall line case: Part I—Convective updrafts. J. Geophys. Res. Atmos., 122, 93519378, https://doi.org/10.1002/2017JD026622.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feingold, G., S. Tzivion, and Z. Levin, 1988: Evolution of raindrop spectra. Part I: Solution to the stochastic collection/breakup equation using the method of moments. J. Atmos. Sci., 45, 33873399, https://doi.org/10.1175/1520-0469(1988)045<3387:EORSPI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ferrier, B. S., W. K. Tao, and J. Simpson, 1995: A double-moment multiple-phase four-class bulk ice scheme. Part II: Simulations of convective storms in different large-scale environments and comparisons with other bulk parameterizations. J. Atmos. Sci., 52, 10011033, https://doi.org/10.1175/1520-0469(1995)052<1001:ADMMPF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fovell, R. G., and Y. Ogura, 1989: Effect of vertical wind shear on numerically simulated multicell storm structure. J. Atmos. Sci., 46, 31443176, https://doi.org/10.1175/1520-0469(1989)046<3144:EOVWSO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Geresdi, I., 1998: Idealized simulation of the Colorado hailstorm case: Comparison of bulk and detailed microphysics. Atmos. Res., 45, 237252, https://doi.org/10.1016/S0169-8095(97)00079-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Geresdi, I., N. Sarkadi, and G. Thompson, 2014: Effect of the accretion by water drops on the melting of snowflakes. Atmos. Res., 149, 96110, https://doi.org/10.1016/j.atmosres.2014.06.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hall, W. D., 1980: A detailed microphysical model within a two-dimensional dynamic framework: Model description and preliminary results. J. Atmos. Sci., 37, 24862507, https://doi.org/10.1175/1520-0469(1980)037<2486:ADMMWA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Harrington, J. Y., M. P. Meyers, R. L. Walko, and W. R. Cotton, 1995: Parameterization of ice crystal conversion processes due to vapor deposition for mesoscale models using double-moment basis functions. Part I: Basic formulation and parcel model results. J. Atmos. Sci., 52, 43444366, https://doi.org/10.1175/1520-0469(1995)052<4344:POICCP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Harrington, J. Y., K. Sulia, and H. Morrison, 2013: A method for adaptive habit prediction in bulk microphysical models. Part I: Theoretical development. J. Atmos. Sci., 70, 87778796, https://doi.org/10.1175/JAS-D-12-040.1.

    • Search Google Scholar
    • Export Citation
  • Hashino, T., and G. J. Tripoli, 2007: The Spectral Ice Habit Prediction System (SHIPS). Part I: Model description and simulation of the vapor deposition process. J. Atmos. Sci., 64, 22102237, https://doi.org/10.1175/JAS3963.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Igel, A. L., and S. C. van den Heever, 2017a: The importance of the shape of cloud droplet size distributions in shallow cumulus clouds. Part I: Bin microphysics simulations. J. Atmos. Sci., 74, 249258, https://doi.org/10.1175/JAS-D-15-0382.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Igel, A. L., and S. C. van den Heever, 2017b: The importance of the shape of cloud droplet size distributions in shallow cumulus clouds. Part II: Bulk microphysics simulations. J. Atmos. Sci., 74, 259273, https://doi.org/10.1175/JAS-D-15-0383.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jensen, M., and Coauthors, 2016: The Midlatitude Continental Convective Clouds Experiment (MC3E). Bull. Amer. Meteor. Soc., 97, 16671686, https://doi.org/10.1175/BAMS-D-14-00228.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Johnson, R. H., S. Chen, and J. J. Toth, 1989: Circulations associated with a mature-to-decaying midlatitude mesoscale convective system. Part I: Surface features—Heat bursts and mesolow development. Mon. Wea. Rev., 117, 942959, https://doi.org/10.1175/1520-0493(1989)117<0942:CAWAMT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Khain, A. P., and I. Sednev, 1995: Simulation of hydrometeor size spectra evolution by water-water, ice-water and ice-ice interactions. Atmos. Res., 36, 107138, https://doi.org/10.1016/0169-8095(94)00030-H.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Khain, A. P., and I. Sednev, 1996: Simulation of precipitation formation in the eastern Mediterranean coastal zone using a spectral microphysics cloud ensemble model. Atmos. Res., 43, 77110, https://doi.org/10.1016/S0169-8095(96)00005-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Khain, A. P., D. Rosenfeld, and I. L. Sednev, 1993: Coastal effects in the eastern Mediterranean as seen from experiments using a cloud ensemble model with detailed description of warm and ice microphysical processes. Atmos. Res., 30, 295319, https://doi.org/10.1016/0169-8095(93)90029-N.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Khain, A. P., A. Pokrovsky, M. Pinsky, A. Seifert, and V. Phillips, 2004: Simulation of effects of atmospheric aerosols on deep turbulent convective clouds using a spectral microphysics mixed-phase cumulus cloud model. Part I: Model description and possible applications. J. Atmos. Sci., 61, 29632982, https://doi.org/10.1175/JAS-3350.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Khain, A. P., and Coauthors, 2015: Representation of microphysical processes in cloud‐resolving models: Spectral (bin) microphysics versus bulk parameterization. Rev. Geophys., 53, 247322, https://doi.org/10.1002/2014RG000468.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Khairoutdinov, M., and Y. Kogan, 2000: A new cloud physics parameterization in a large-eddy simulation model of marine stratocumulus. Mon. Wea. Rev., 128, 229243, https://doi.org/10.1175/1520-0493(2000)128<0229:ANCPPI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Khvorostyanov, V. I., A. P. Khain, and E. L. Kogteva, 1989: A two-dimensional non-stationary microphysical model of a three-phase convective cloud and evaluation of the effects of seeding by a crystallizing agent. Sov. Meteor. Hydrol., 5, 3345.

    • Search Google Scholar
    • Export Citation
  • Kogan, Y. L., and A. Belochitski, 2012: Parameterization of cloud microphysics based on full integral moments. J. Atmos. Sci., 69, 22292242, https://doi.org/10.1175/JAS-D-11-0268.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kurowski, M. J., W. W. Grabowski, and P. K. Smolarkiewicz, 2014: Anelastic and compressible simulation of moist deep convection. J. Atmos. Sci., 71, 37673787, https://doi.org/10.1175/JAS-D-14-0017.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lebo, Z. J., and J. H. Seinfeld, 2011: Theoretical basis for convective invigoration due to increased aerosol concentration. Atmos. Chem. Phys., 11, 54075429, https://doi.org/10.5194/acp-11-5407-2011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lebo, Z. J., and H. Morrison, 2014: Dynamical effects of aerosol perturbations on simulated idealized squall lines. Mon. Wea. Rev., 142, 9911009, https://doi.org/10.1175/MWR-D-13-00156.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lebo, Z. J., and H. Morrison, 2015: Effects of horizontal and vertical grid spacing on mixing in simulated squall lines and implications for convective strength and structure. Mon. Wea. Rev., 143, 43554375, https://doi.org/10.1175/MWR-D-15-0154.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lebo, Z. J., H. Morrison, and J. H. Seinfeld, 2012: Are simulated aerosol-induced effects on deep convective clouds strongly dependent on saturation adjustment? Atmos. Chem. Phys., 12, 99419964, https://doi.org/10.5194/acp-12-9941-2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, X., W.-K. Tao, A. P. Khain, J. Simpson, and D. E. Johnson, 2009a: Sensitivity of a cloud-resolving model to bulk and explicit bin microphysical schemes. Part I: Comparisons. J. Atmos. Sci., 66, 321, https://doi.org/10.1175/2008JAS2646.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, X., W.-K. Tao, A. P. Khain, J. Simpson, and D. E. Johnson, 2009b: Sensitivity of a cloud-resolving model to bulk and explicit bin microphysical schemes. Part II: Cloud microphysics and storm dynamics interactions. J. Atmos. Sci., 66, 2240, https://doi.org/10.1175/2008JAS2647.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, C., M. W. Moncrieff, and E. J. Zipser, 1997: Dynamical influence of microphysics in a tropical squall line: A numerical study. Mon. Wea. Rev., 125, 21932210, https://doi.org/10.1175/1520-0493(1997)125<2193:DIOMIT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Loftus, A. M., D. B. Weber, and C. A. Doswell III, 2008: Parameterized mesoscale forcing mechanisms for initiating numerically simulated isolated multicellular convection. Mon. Wea. Rev., 136, 24082421, https://doi.org/10.1175/2007MWR2133.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mätzler, C., 1998: Microwave properties of ice and snow. Solar System Ices, B. Schmitt, C. De Bergh, and M. Festou, Eds., Astrophysics and Space Science Library Series, Vol. 227, Springer, 241–257.

    • Crossref
    • Export Citation
  • McCumber, M., W. K. Tao, J. Simpson, R. Penc, and S. T. Soong, 1991: Comparison of ice-phase microphysical parameterization schemes using numerical simulations of tropical convection. J. Appl. Meteor., 30, 9851004, https://doi.org/10.1175/1520-0450-30.7.985.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meyers, M. P., P. J. DeMott, and W. R. Cotton, 1992: New primary ice-nucleation parameterizations in an explicit cloud model. J. Appl. Meteor., 31, 708721, https://doi.org/10.1175/1520-0450(1992)031<0708:NPINPI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Milbrandt, J. A., and R. McTaggart-Cowan, 2010: Sedimentation-induced errors in bulk microphysics schemes. J. Atmos. Sci., 67, 39313948, https://doi.org/10.1175/2010JAS3541.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mitchell, D. L., 1996: Use of mass- and area-dimensional power laws for determining precipitation particle terminal velocities. J. Atmos. Sci., 53, 17101723, https://doi.org/10.1175/1520-0469(1996)053<1710:UOMAAD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mitra, S. K., O. Vohl, M. Ahr, and H. R. Pruppacher, 1990: A wind tunnel and theoretical study of the melting behavior of atmospheric ice particles. IV: Experiment and theory for snow flakes. J. Atmos. Sci., 47, 584591, https://doi.org/10.1175/1520-0469(1990)047<0584:AWTATS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morrison, H., and W. W. Grabowski, 2007: Comparison of bulk and bin warm-rain microphysics models using a kinematic framework. J. Atmos. Sci., 64, 28392861, https://doi.org/10.1175/JAS3980.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morrison, H., and J. A. Milbrandt, 2015: Parameterization of cloud microphysics based on the prediction of bulk ice particle properties. Part I: Scheme description and idealized tests. J. Atmos. Sci., 72, 287311, https://doi.org/10.1175/JAS-D-14-0065.1.

    • 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, https://doi.org/10.1175/2008MWR2556.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morrison, H., S. A. Tessendorf, K. Ikeda, and G. Thompson, 2012: Sensitivity of a simulated midlatitude squall line to parameterization of raindrop breakup. Mon. Wea. Rev., 140, 24372460, https://doi.org/10.1175/MWR-D-11-00283.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morrison, H., J. A. Milbrandt, G. H. Bryan, K. Ikeda, S. A. Tessendorf, and G. Thompson, 2015: Parameterization of cloud microphysics based on the prediction of bulk ice particle properties. Part II: Case study comparisons with observations and other schemes. J. Atmos. Sci., 72, 312339, https://doi.org/10.1175/JAS-D-14-0066.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mossop, S. C., and J. Hallett, 1974: Ice crystal concentration in cumulus clouds: Influence of the drop spectrum. Science, 186, 632634, https://doi.org/10.1126/science.186.4164.632.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Muhlbauer, A., T. Hashino, L. Xue, A. Teller, U. Lohmann, R. M. Rasmussen, I. Geresdi, and Z. Pan, 2010: Intercomparison of aerosol-cloud-precipitation interactions in stratiform orographic mixed-phase clouds. Atmos. Chem. Phys., 10, 81738196, https://doi.org/10.5194/acp-10-8173-2010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Oklahoma Mesonet, 2015: Oklahoma Mesonet observations. University of Oklahoma, Norman, OK, accessed 2015, https://doi.org/10.15763/dbs.mesonet, https://www.mesonet.org/index.php/weather/local.

    • Crossref
    • Export Citation
  • Ovchinnikov, M., and Coauthors, 2014: Intercomparison of large‐eddy simulations of Arctic mixed‐phase clouds: Importance of ice size distribution assumptions. J. Adv. Model. Earth Syst., 6, 223248, https://doi.org/10.1002/2013MS000282.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Parker, M. D., 2010: Relationship between system slope and updraft intensity in squall lines. Mon. Wea. Rev., 138, 35723578, https://doi.org/10.1175/2010MWR3441.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rasmussen, R. M., and A. J. Heymsfield, 1987: Melting and shedding of graupel and hail. Part I: Model physics. J. Atmos. Sci., 44, 27542763, https://doi.org/10.1175/1520-0469(1987)044<2754:MASOGA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rasmussen, R. M., I. Geresdi, G. Thompson, K. Manning, and E. Karplus, 2002: Freezing drizzle formation in stably stratified layer clouds: The role of radiative cooling of cloud droplets, cloud condensation nuclei, and ice initiation. J. Atmos. Sci., 59, 837860, https://doi.org/10.1175/1520-0469(2002)059<0837:FDFISS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ray, P. S., 1972: Broadband complex refractive indices of ice and water. Appl. Opt., 11, 18361844, https://doi.org/10.1364/AO.11.001836.

  • Reisin, T., Z. Levin, and S. Tzivion, 1996: Rain production in convective clouds as simulated in an axisymmetric model with detailed microphysics. Part I: Description of the model. J. Atmos. Sci., 53, 497519, https://doi.org/10.1175/1520-0469(1996)053<0497:RPICCA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rotunno, R., J. B. Klemp, and M. L. Weisman, 1988: A theory for strong, long-lived squall lines. J. Atmos. Sci., 45, 463485, https://doi.org/10.1175/1520-0469(1988)045<0463:ATFSLL>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saleeby, S. M., and W. R. Cotton, 2008: A binned approach to cloud-droplet riming implemented in a bulk microphysics model. J. Appl. Meteor. Climatol., 47, 694703, https://doi.org/10.1175/2007JAMC1664.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sarkadi, N., I. Geresdi, and G. Thompson, 2016: Numerical simulation of precipitation formation in the case orographically induced convective cloud: Comparison of the results of bin and bulk microphysical schemes. Atmos. Res., 180, 241261, https://doi.org/10.1016/j.atmosres.2016.04.010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seifert, A., and K. D. Beheng, 2006: A two-moment cloud microphysics parameterization for mixed-phase clouds. Part 1: Model description. Meteor. Atmos. Phys., 92, 4566, https://doi.org/10.1007/s00703-005-0112-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seifert, A., A. Khain, A. Pokrovsky, and K. D. Beheng, 2006: A comparison of spectral bin and two-moment bulk mixed-phase cloud microphysics. Atmos. Res., 80, 4666, https://doi.org/10.1016/j.atmosres.2005.06.009.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shipway, B. J., and A. A. Hill, 2012: Diagnosis of systematic differences between multiple parametrizations of warm rain microphysics using a kinematic framework. Quart. J. Roy. Meteor. Soc., 138, 21962211, https://doi.org/10.1002/qj.1913.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and Coauthors, 2008: A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN-475+STR, 113 pp., https://doi.org/10.5065/D68S4MVH.

    • Crossref
    • Export Citation
  • Smith, P. L., 1984: Equivalent radar reflectivity factors for snow and ice particles. J. Climate Appl. Meteor., 23, 12581260, https://doi.org/10.1175/1520-0450(1984)023<1258:ERRFFS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Soong, S. T., 1974: Numerical simulation of warm rain development in an axisymmetric cloud model. J. Atmos. Sci., 31, 12621285, https://doi.org/10.1175/1520-0469(1974)031<1262:NSOWRD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Takahashi, T., 1976: Hail in an axisymmetric cloud model. J. Atmos. Sci., 33, 15791601, https://doi.org/10.1175/1520-0469(1976)033<1579:HIAACM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Teller, A., L. Xue, and Z. Levin, 2012: The effects of mineral dust particles, aerosol regeneration and ice nucleation parameterizations on clouds and precipitation. Atmos. Chem. Phys., 12, 93039320, https://doi.org/10.5194/acp-12-9303-2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thompson, G., R. Rasmussen, and K. Manning, 2004: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part I: Description and sensitivity analysis. Mon. Wea. Rev., 132, 519542, https://doi.org/10.1175/1520-0493(2004)132<0519:EFOWPU>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tzivion, S., G. Feingold, and Z. Levin, 1987: An efficient numerical solution to the stochastic collection equation. J. Atmos. Sci., 44, 31393149, https://doi.org/10.1175/1520-0469(1987)044<3139:AENSTT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tzivion, S., T. Reisin, and Z. Levin, 1999: A numerical solution of the kinetic collection equation using high spectral grid resolution: A proposed reference. J. Comput. Phys., 148, 527544, https://doi.org/10.1006/jcph.1998.6128.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vali, G., 1975: Remarks on the mechanism of atmospheric ice nucleation. Proc. Eighth Int. Conf. on Nucleation, Leningrad, Russia, Committee on Nucleation & Atmospheric Aerosols, 265–269.

  • vanZanten, M. C., and Coauthors, 2011: Controls on precipitation and cloudiness in simulations of trade-wind cumulus as observed during RICO. J. Adv. Model Earth Syst., 3, M06001, https://doi.org/10.1029/2011MS000056.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Varble, A., and Coauthors, 2014: Evaluation of cloud-resolving and limited area model intercomparison simulations using TWP-ICE observations: 1. Deep convective updraft properties. J. Geophys. Res. Atmos., 119, 13 89113 918, https://doi.org/10.1002/2013JD021371.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, Y., J. Fan, R. Zhang, L. R. Leung, and C. Franklin, 2013: Improving bulk microphysics parameterizations in simulations of aerosol effects. J. Geophys. Res. Atmos., 118, 53615379, https://doi.org/10.1002/jgrd.50432.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weisman, M. L., 1992: The role of convectively generated rear-inflow jets in the evolution of long-lived mesoconvective systems. J. Atmos. Sci., 49, 18261847, https://doi.org/10.1175/1520-0469(1992)049<1826:TROCGR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weisman, M. L., and R. Rotunno, 2004: “A theory for strong long-lived squall lines” revisited. J. Atmos. Sci., 61, 361382, https://doi.org/10.1175/1520-0469(2004)061<0361:ATFSLS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, W., and G. M. McFarquhar, 2016: On the impacts of different definitions of maximum dimension for nonspherical particles recorded by 2D imaging probes. J. Atmos. Oceanic Technol., 33, 10571072, https://doi.org/10.1175/JTECH-D-15-0177.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xue, L., A. Teller, R. M. Rasmussen, I. Geresdi, and Z. Pan, 2010: Effects of aerosol solubility and regeneration on warm-phase orographic clouds and precipitation simulated by a detailed bin microphysical scheme. J. Atmos. Sci., 67, 33363354, https://doi.org/10.1175/2010JAS3511.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xue, L., A. Teller, R. M. Rasmussen, I. Geresdi, Z. Pan, and X. Liu, 2012: Effects of aerosol solubility and regeneration on mixed-phase orographic clouds and precipitation. J. Atmos. Sci., 69, 19942010, https://doi.org/10.1175/JAS-D-11-098.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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