• Ackerman, T. P., K. N. Liou, F. P. Valero, and L. Pfister, 1988: Heating rates in tropical anvils. J. Atmos. Sci., 45, 16061623, https://doi.org/10.1175/1520-0469(1988)045,1606:HRITA.2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Bailey, M. P., and J. Hallett, 2009: A comprehensive habit diagram for atmospheric ice crystals: Confirmation from the laboratory, AIRS II, and other field studies. J. Atmos. Sci., 66, 28882899, https://doi.org/10.1175/2009JAS2883.1.

    • Search Google Scholar
    • Export Citation
  • Baumgardner, D. , and Coauthors, 2017: Cloud ice properties: In situ measurement challenges. Ice Formation and Evolution in Clouds and Precipitation: Measurement and Modeling Challenges, Meteor. Monogr., No. 58, Amer. Meteor. Soc., https://doi.org/10.1175/AMSMONOGRAPHS-D-16-0011.1.

  • Brechner, P., 2021: Ice crystal size distributions in tropical mesoscale convective systems in the vicinity of Darwin, Australia: Results from the HAIC/HIWC campaign. M.S. thesis, School of Meteorology, University of Oklahoma, 57 pp., https://hdl.handle.net/11244/332327.

  • Brown, P. R., and P. N. Francis, 1995: Improved measurements of the ice water content in cirrus using a total-water probe. J. Atmos. Oceanic Technol., 12, 410414, https://doi.org/10.1175/1520-0426(1995)012<0410:IMOTIW>2.0.CO;2.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Chandrasekar, V., and V. N. Bringi, 1987: Simulation of radar reflectivity and surface measurements of rainfall. J. Atmos. Oceanic Technol., 4, 464478, https://doi.org/10.1175/1520-0426(1987)004,0464:SORRAS.2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Chen, C. H., C. L. Su, J. H. Chen, and Y. H. Chu, 2020: Vertical wind effect on slope and shape parameters of gamma drop size distribution. J. Atmos. Oceanic Technol., 37, 243262, https://doi.org/10.1175/JTECH-D-18-0026.1.

    • Search Google Scholar
    • Export Citation
  • Chen, S., and W. Cotton, 1988: The sensitivity of a simulated extratropical mesoscale convective system to longwave radiation and ice-phase microphysics. J. Atmos. Sci., 45, 38973910, https://doi.org/10.1175/1520-0469(1988)045<3897:TSOASE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Clark, A. J., and Coauthors, 2012: An overview of the 2010 Hazardous Weather Testbed Experimental Forecast Program Spring Experiment. Bull. Amer. Meteor. Soc., 93, 5574, https://doi.org/10.1175/BAMS-D-11-00040.1.

    • Search Google Scholar
    • Export Citation
  • Del Genio, A. D., and W. Kovari, 2002: Climatic properties of tropical precipitating convection under varying environmental conditions. J. Climate, 15, 25972615, https://doi.org/10.1175/1520-0442(2002)015<2597:CPOTPC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Dezitter, F., A. Grandin, J. L. Brenguier, F. Hervy, H. Schlager, P. Villedieu, and G. Zalamansky, 2013: HAIC (High Altitude Ice Crystals). Proc. Fifth AIAA Atmospheric and Space Environments Conf., AIAA-2013-2674, San Diego, CA, American Institute of Aeronautics and Astronautics, http://arc.aiaa.org/doi/abs/10.2514/6.2013-2674.

  • Ding, S., G. M. McFarquhar, S. W. Nesbitt, R. J. Chase, M. R. Poellot, and H. Wang, 2020: Dependence of mass–dimensional relationships on median mass diameter. Atmosphere, 11, 756, https://doi.org/10.3390/atmos11070756.

    • Search Google Scholar
    • Export Citation
  • DMT, 2009: Single particle imaging. Data analysis user’s guide, DOC-0223, Rev. A, 34 pp.

  • Field, P. R., R. J. Hogan, P. R. A. Brown, A. J. Illingworth, T. W. Choularton, and R. J. Cotton, 2005: Parametrization of ice‐particle size distributions for mid‐latitude stratiform cloud. Quart. J. Roy. Meteor. Soc., 131, 19972017, https://doi.org/10.1256/qj.04.134.

    • Search Google Scholar
    • Export Citation
  • Finlon, J. A., G. M. McFarquhar, S. W. Nesbitt, R. M. Rauber, H. Morrison, W. Wu, and P. Zhang, 2019: A novel approach for characterizing the variability in mass–dimension relationships: Results from MC3E. Atmos. Chem. Phys., 19, 36213643, https://doi.org/10.5194/acp-19-3621-2019.

    • Search Google Scholar
    • Export Citation
  • Fontaine, E., and Coauthors, 2017: Evaluation of radar reflectivity factor simulations of ice crystal populations from in situ observations for the retrieval of condensed water content in tropical mesoscale convective systems. Atmos. Meas. Tech., 10, 22392252, https://doi.org/10.5194/amt-10-2239-2017.

    • Search Google Scholar
    • Export Citation
  • Fu, Q., 1996: An accurate parameterization of the solar radiative properties of cirrus clouds for climate models. J. Climate, 9, 20582082, https://doi.org/10.1175/1520-0442(1996)009<2058:AAPOTS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Fu, Q., S. K. Krueger, and K. N. Liou, 1995: Interactions of radiation and convection in simulated tropical cloud clusters. J. Atmos. Sci., 52, 13101328, https://doi.org/10.1175/1520-0469(1995)052<1310:IORACI>2.0.CO;2.

    • 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, https://doi.org/10.1175/MWR2810.1.

    • Search Google Scholar
    • Export Citation
  • Gu, Y., K. N. Liou, S. C. Ou, and R. Fovell, 2011: Cirrus cloud simulations using WRF with improved radiation parameterization and increased vertical resolution. J. Geophys. Res., 116, D06119, https://doi.org/10.1029/2010JD014574.

    • Search Google Scholar
    • Export Citation
  • Gunn, K. L. S., and J. S. Marshall, 1958: The distribution with size of aggregate snowflakes. J. Meteor., 15, 452461, https://doi.org/10.1175/1520-0469(1958)015<0452:TDWSOA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Haddad, Z. S., S. L. Durden, and E. Im, 1996: Parameterizing the raindrop size distribution. J. Appl. Meteor., 35, 313, https://doi.org/10.1175/1520-0450(1996)035<0003:PTRSD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Handwerker, J., and W. Straub, 2011: Optimal determination of parameters for gamma-type drop size distributions based on moments. J. Atmos. Oceanic Technol., 28, 513529, https://doi.org/10.1175/2010JTECHA1474.1.

    • Search Google Scholar
    • Export Citation
  • Heymsfield, A. J., and G. M. McFarquhar, 2002: Mid-latitude and tropical cirrus: Microphysical properties. Cirrus, D. Lynch et al., Eds., Oxford University Press, 78101.

    • Search Google Scholar
    • Export Citation
  • Heymsfield, A. J., A. Bansemer, G. Heymsfield, and A. O. Fierro, 2009: Microphysics of maritime tropical convective updrafts at temperatures from −20° to −60° . J. Atmos. Sci., 66, 35303562, https://doi.org/10.1175/2009JAS3107.1.

    • Search Google Scholar
    • Export Citation
  • Heymsfield, A. J., C. Schmitt, and A. Bansemer, 2013: Ice cloud particle size distributions and pressure-dependent terminal velocities from in situ observations at temperatures from 0° to -86° C. J. Atmos. Sci., 70, 41234154, https://doi.org/10.1175/JAS-D-12-0124.1.

    • Search Google Scholar
    • Export Citation
  • Hu, Y., and Coauthors, 2021: Dependence of ice microphysical properties on environmental parameters: Results from HAIC-HIWC Cayenne field campaign. J. Atmos. Sci., 78, 29572981, https://doi.org/10.1175/JAS-D-21-0015.1.

    • Search Google Scholar
    • Export Citation
  • Huang, Y., Y. Wang, L. Xue, X. Wei, L. Zhang, and H. Li, 2020: Comparison of three microphysics parameterization schemes in the WRF model for an extreme rainfall event in the coastal metropolitan city of Guangzhou, China. Atmos. Res., 240, 10493, https://doi.org/10.1016/j.atmosres.2020.104939.

  • Huang, Y., and Coauthors, 2021: Microphysical processes producing high ice water contents (HIWCs) in tropical convective clouds during the HAIC-HIWC field campaign: Evaluation of simulations using bulk microphysical schemes. Atmos. Chem. Phys., 21, 69196944, https://doi.org/10.5194/acp-21-6919-2021.

    • Search Google Scholar
    • Export Citation
  • Jackson, R. C., G. M. McFarquhar, J. Stith, M. Beals, R. A. Shaw, J. Jensen, J. Fugal, and A. Korolev, 2014: An assessment of the impact of antishattering tips and artifact removal techniques on cloud ice size distributions measured by the 2D cloud probe. J. Atmos. Oceanic Technol., 31, 25672590, https://doi.org/10.1175/JTECH-D-14-00018.1.

    • Search Google Scholar
    • Export Citation
  • Jackson, R. C., G. M. McFarquhar, A. M. Fridlind, and R. Atlas, 2015: The dependence of cirrus gamma size distributions expressed as volumes in N0-λ-μ phase space and bulk cloud properties on environmental conditions: Results from the Small Ice Particles in Cirrus Experiment (SPARTICUS). J. Geophys. Res. Atmos., 120, 10 351–10 377, https://doi.org/10.1002/2015JD023492.

    • Search Google Scholar
    • Export Citation
  • Jakob, C., and S. A. Klein, 1999: The role of vertically varying cloud fraction in the parametrization of microphysical processes in the ECMWF model. Quart. J. Roy. Meteor. Soc., 125, 941965, https://doi.org/10.1002/qj.49712555510.

    • Search Google Scholar
    • Export Citation
  • Jensen, E. J., and Coauthors, 2009: On the importance of small ice crystals in tropical anvil cirrus. Atmos. Chem. Phys., 9, 55195537, https://doi.org/10.5194/acp-9-5519-2009.

    • Search Google Scholar
    • Export Citation
  • Jorgensen, D. P., E. J. Zipser, and M. A. LeMone, 1985: Vertical motions in intense hurricanes. J. Atmos. Sci., 42, 839856, https://doi.org/10.1175/1520-0469(1985)042<0839:VMIIH>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Korolev, A. V., and G. A. Isaac, 2006: Relative humidity in liquid, mixed-phase, and ice clouds. J. Atmos. Sci., 63, 28652880, https://doi.org/10.1175/JAS3784.1.

    • Search Google Scholar
    • Export Citation
  • Korolev, A. V., and P. Field, 2015: Assessment of the performance of the inter-arrival time algorithm to identify ice shattering artifacts in cloud particle probe measurements. Atmos. Meas. Tech., 8, 761777, https://doi.org/10.5194/amt-8-761-2015.

    • Search Google Scholar
    • Export Citation
  • Korolev, A. V., E. F. Emery, J. W. Strapp, S. G. Cober, G. A. Isaac, M. Wasey, and D. Marcotte, 2011: Small ice particles in tropospheric clouds: Fact or artifact? Airborne icing instrumentation evaluation experiment. Bull. Amer. Meteor. Soc., 92, 967973, https://doi.org/10.1175/2010BAMS3141.1.

    • Search Google Scholar
    • Export Citation
  • Korolev, A. V., E. F. Emery, J. W. Strapp, S. G. Cober, and G. A. Isaac, 2013: Quantification of the effects of shattering on airborne ice particle measurements. J. Atmos. Oceanic Technol., 30, 25272553, https://doi.org/10.1175/JTECH-D-13-00115.1.

    • Search Google Scholar
    • Export Citation
  • Korolev, A. V., A. Shashkov, and H. Barker, 2014: Calibrations and performance of the airborne cloud extinction probe. J. Atmos. Oceanic Technol., 31, 326345, https://doi.org/10.1175/JTECH-D-13-00020.1.

    • Search Google Scholar
    • Export Citation
  • Korolev, A. V., I. Heckman, and M. Wolde, 2018: Observation of phase composition and humidity in oceanic mesoscale convective systems. 15th Conf. on Cloud Physics, 236, Vancouver, BC, Canada, Amer. Meteor. Soc., https://ams.confex.com/ams/15CLOUD15ATRAD/webprogram/Paper347111.html.

  • Lance, S., C. A. Brock, D. Rogers, and J. A. Gordon, 2010: Water droplet calibration of the Cloud Droplet Probe (CDP) and in-flight performance in liquid, ice and mixed-phase clouds during ARCPAC. Atmos. Meas. Tech., 3, 16831706, https://doi.org/10.5194/amt-3-1683-2010.

    • Search Google Scholar
    • Export Citation
  • Lawson, R. P., E. Jensen, D. L. Mitchell, B. Barker, Q. Mo, and B. Pilson, 2010: Microphysical and radiative properties of tropical clouds investigated in TC4 and NAMMA. J. Geophys. Res., 115, D00J08, https://doi.org/10.1029/2009JD013017.

    • Search Google Scholar
    • Export Citation
  • Leary, C. A., and R. A. Houze Jr., 1980: The contribution of mesoscale motions to the mass and heat fluxes of an intense tropical convective system. J. Atmos. Sci., 37, 784796, https://doi.org/10.1175/1520-0469(1980)037<0784:TCOMMT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Leroy, D. , and Coauthors, 2016a: HAIC/HIWC field campaigns—Specific findings on ice crystals characteristics in high ice water content cloud regions, 8th AIAA Atmospheric and Space Environments Conf., AIAA 2016-4056, Washington, DC, AIAA, https://doi.org/10.2514/6.2016-4056.

  • Leroy, D., E. Fontaine, A. Schwarzenboeck, and J. W. Strapp, 2016b: Ice crystal sizes in high ice water content clouds. Part I: On the computation of median mass diameters from in situ measurements. J. Atmos. Oceanic Technol., 33, 24612476, https://doi.org/10.1175/JTECH-D-15-0151.1.

    • Search Google Scholar
    • Export Citation
  • Leroy, D., and Coauthors, 2017: Ice crystal sizes in high ice water content clouds. Part 2: Median mass diameter statistics in tropical convection observed during the HAIC/HIWC project. J. Atmos. Oceanic Technol., 34, 117136, https://doi.org/10.1175/JTECH-D-15-0246.1.

    • Search Google Scholar
    • Export Citation
  • Li, X., and Z. Pu, 2008: Sensitivity of numerical simulation of early rapid intensification of Hurricane Emily (2005) to cloud microphysical and planetary boundary layer parameterizations. Mon. Wea. Rev., 136, 48194838, https://doi.org/10.1175/2008MWR2366.1.

    • Search Google Scholar
    • Export Citation
  • Lin, Y., and B. A. Colle, 2011: A new bulk microphysical scheme that includes riming intensity and temperature-dependent ice characteristics. Mon. Wea. Rev., 139, 10131035, https://doi.org/10.1175/2010MWR3293.1.

    • Search Google Scholar
    • Export Citation
  • Locatelli, J. D., and P. V. Hobbs, 1974: Fall speeds and masses of solid precipitation particles. J. Geophys. Res., 79, 21852197, https://doi.org/10.1029/JC079i015p02185.

    • Search Google Scholar
    • Export Citation
  • Lucas, C., E. J. Zipser, and M. A. Lemone, 1994: Vertical velocity in oceanic convection off tropical Australia. J. Atmos. Sci., 51, 31833193, https://doi.org/10.1175/1520-0469(1994)051<3183:VVIOCO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Machado, L. A. T., and W. B. Rossow, 1993: Structural characteristics and radiative properties of tropical cloud clusters. Mon. Wea. Rev., 121, 32343260, https://doi.org/10.1175/1520-0493(1993)121<3234:SCARPO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Mascio, J., G. M. McFarquhar, T. L. Hsieh, M. Freer, A. Dooley, and A. J. Heymsfield, 2020: The use of gamma distributions to quantify the dependence of cloud particle size distributions in hurricanes on cloud and environmental conditions. Quart. J. Roy. Meteor. Soc., 146, 21162137, https://doi.org/10.1002/qj.3782.

    • Search Google Scholar
    • Export Citation
  • Matsui, T., J. Chern, W. Tao, S. Lang, M. Satoh, T. Hashino, and T. Kubota, 2016: On the land–ocean contrast of tropical convection and microphysics statistics derived from TRMM satellite signals and global storm-resolving models. J. Hydrometeor., 17, 14251445, https://doi.org/10.1175/JHM-D-15-0111.1.

    • Search Google Scholar
    • Export Citation
  • Mazin, I. P., A. V. Korolev, A. Heymsfield, G. A. Isaac, and S. G. Cober, 2001: Thermodynamics of icing cylinder for measurements of liquid water content in supercooled clouds. J. Atmos. Oceanic Technol., 18, 543558, https://doi.org/10.1175/1520-0426(2001)018<0543:TOICFM>2.0.CO;2.

    • Search Google Scholar
    • 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.

    • Search Google Scholar
    • Export Citation
  • McFarquhar, G. M., and A. J. Heymsfield, 1996: Microphysical characteristics of three anvils sampled during the Central Equatorial Pacific Experiment. J. Atmos. Sci., 53, 24012423, https://doi.org/10.1175/1520-0469(1996)053,2401:MCOTAS.2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • McFarquhar, G. M., and A. J. Heymsfield, 1997: Parameterization of tropical cirrus ice crystal size distributions and implications for radiative transfer: Results from CEPEX. J. Atmos. Sci., 54, 21872200, https://doi.org/10.1175/1520-0469(1997)054<2187:POTCIC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • McFarquhar, G. M., and A. J. Heymsfield, 1998: The definition and significance of an effective radius for ice clouds. J. Atmos. Sci., 55, 20392052, https://doi.org/10.1175/1520-0469(1998)055<2039:TDASOA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • McFarquhar, G. M., and R. A. Black, 2004: Observations of particle size and phase in tropical cyclones: Implications for mesoscale modeling of microphysical processes. J. Atmos. Sci., 61, 422439, https://doi.org/10.1175/1520-0469(2004)061,0422:OOPSAP.2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • McFarquhar, G. M., H. Zhang, G. Heymsfield, R. Hood, J. Dudhia, J. B. Halverson, and F. Marks, 2006: Factors affecting the evolution of Hurricane Erin (2001) and the distributions of hydrometeors: Role of microphysical processes. J. Atmos. Sci., 63, 127150, https://doi.org/10.1175/JAS3590.1.

    • Search Google Scholar
    • Export Citation
  • McFarquhar, G. M., M. S. Timlin, R. M. Rauber, B. F. Jewett, J. A. Grim, and D. P. Jorgensen, 2007: Vertical variability of cloud hydro-meteors in the stratiform region of mesoscale convective systems and bow echoes. Mon. Wea. Rev., 135, 34053428, https://doi.org/10.1175/MWR3444.1.

    • Search Google Scholar
    • Export Citation
  • McFarquhar, G. M., B. F. Jewett, M. S. Gilmore, S. W. Nesbitt, and T. Hsieh, 2012: Vertical velocity and microphysical distributions related to rapid intensification in a simulation of Hurricane Dennis (2005). J. Atmos. Sci., 69, 35153534, https://doi.org/10.1175/JAS-D-12-016.1.

    • Search Google Scholar
    • Export Citation
  • McFarquhar, G. M., T. L. Hsieh, M. Freer, J. Mascio, and B. F. Jewett, 2015: The characterization of ice hydrometeor gamma size distributions as volumes in N0–λ–μphase space: Implications for microphysical process modeling. J. Atmos. Sci., 72, 892909, https://doi.org/10.1175/JAS-D-14-0011.1.

    • Search Google Scholar
    • Export Citation
  • McFarquhar, G. M., and Coauthors, 2017: Processing of ice cloud in situ data collected by bulk water, scattering, and imaging probes: Fundamentals, uncertainties, and efforts toward consistency. Ice Formation and Evolution in Clouds and Precipitation: Measurement and Modeling Challenges, Meteor. Monogr., No. 58, Amer. Meteor. Soc., https://doi.org/10.1175/AMSMONOGRAPHS-D-16-0007.1.

  • Meyers, M. P., R. L. Walko, J. Y. Harrington, and W. R. Cotton, 1997: New RAMS cloud microphysics. Part II: The two-moment scheme. Atmos. Res., 45, 339, https://doi.org/10.1016/S0169-8095(97)00018-5.

    • Search Google Scholar
    • Export Citation
  • Milbrandt, J. A., and M. K. Yau, 2005: A multimoment bulk microphysics parameterization. Part I: Analysis of the role of the spectral shape parameter. J. Atmos. Sci., 62, 30513064, https://doi.org/10.1175/JAS3534.1.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Mitchell, D. L., 2002: Effective diameter in radiation transfer: General definition, applications and limitations. J. Atmos. Sci., 59, 23302346, https://doi.org/10.1175/1520-0469(2002)059<2330:EDIRTG>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Mitchell, D. L., and W. P. Arnott, 1994: A model predicting the evolution of ice particle size spectra and radiative properties of cirrus clouds. Part II: Dependence of absorption and extinction on ice crystal morphology. J. Atmos. Sci., 51, 817832, https://doi.org/10.1175/1520-0469(1994)051<0817:AMPTEO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Mitchell, D. L., P. Rasch, D. Ivanova, G. McFarquhar, and T. Nousiainen, 2008: Impact of small ice crystal assumptions on ice sedimentation rates in cirrus clouds and GCM simulations. Geophys. Res. Lett., 35, L09806, https://doi.org/10.1029/2008GL033552.

    • Search Google Scholar
    • Export Citation
  • Moisseev, D. N., and V. Chandrasekar, 2007: Examination of the μ–Λ relation suggested for drop size distribution parameters. J. Atmos. Oceanic Technol., 24, 847855, https://doi.org/10.1175/JTECH2010.1.

    • Search Google Scholar
    • Export Citation
  • Morrison, H., and W. Grabowski, 2008: A novel approach for representing ice microphysics in models: Description and tests using a kinematic framework. J. Atmos. Sci., 65, 15281548, https://doi.org/10.1175/2007JAS2491.1.

    • Search Google Scholar
    • Export Citation
  • Morrison, H., and J. A. Milbrandt, 2015: Parameterization of cloud microphysics based on the predictions 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.

    • 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.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Moshtagh, N., 2006: Minimum volume enclosing ellipsoids. MATLAB, accessed 20 December 2019, https://www.mathworks.com/matlabcentral/fileexchange/9542-minimum-volume-enclosing-ellipsoid.

  • Murphy, A. M., R. M. Rauber, G. M. McFarquhar, J. A. Finlon, D. M. Plummer, A. A. Rosenow, and B. F. Jewett, 2017: A microphysical analysis of elevated convection in the comma head region of continental winter cyclones. J. Atmos. Sci., 74, 6991, https://doi.org/10.1175/JAS-D-16-0204.1.

    • Search Google Scholar
    • Export Citation
  • Protat, A., and Coauthors, 2016: The measured relationship between ice water content and cloud radar reflectivity in tropical convective clouds. J. Appl. Meteor. Climatol., 55, 17071729, https://doi.org/10.1175/JAMC-D-15-0248.1.

    • Search Google Scholar
    • Export Citation
  • Qu, Z., and Coauthors, 2018: Evaluation of a high-resolution numerical weather prediction model’s simulated clouds using observations from CloudSat, GOES-13 and in situ aircraft. Quart. J. Roy. Meteor. Soc., 144, 16811694, https://doi.org/10.1002/qj.3318.

    • Search Google Scholar
    • Export Citation
  • Ratvasky, T. P. , and Coauthors, 2019: Summary of the High Ice Water Content (HIWC) RADAR flight campaigns. SAE Tech. Paper 2019-01-2025, NASA/TM-2020-220306, https://doi.org/10.4271/2019-01-2027.

  • Sanderson, B. M., C. Piani, W. J. Ingram, D. A. Stone, and M. R. Allen, 2008: Towards constraining climate sensitivity by linear analysis of feedback patterns in thousands of perturbed-physics GCM simulations. Climate Dyn., 30, 175190, https://doi.org/10.1007/s00382-007-0280-7.

    • Search Google Scholar
    • Export Citation
  • Schlimme, I., A. Macke, and J. Reichardt, 2005: The impact of ice crystal shapes, size distributions, and spatial structures of cirrus clouds on solar radiative fluxes. J. Atmos. Sci., 62, 22742283, https://doi.org/10.1175/JAS3459.1.

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

    • Search Google Scholar
    • 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.

    • 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, https://doi.org/10.1175/JAM2258.1.

    • 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, https://doi.org/10.1175/2009JAMC2114.1.

    • Search Google Scholar
    • Export Citation
  • SPEC, 2011: 2D-S Post-processing using 2D-S View Software: User manual, version 1.1. SPEC Inc., 48 pp.

  • Stanford, M. W., H. Morrison, A. Varble, J. Berner, W. Wu, G. McFarquhar, and J. Milbrandt, 2019: Sensitivity of simulated deep convection to a stochastic ice microphysics framework. J. Adv. Model. Earth Syst., 11, 33623389, https://doi.org/10.1029/2019MS001730.

    • Search Google Scholar
    • Export Citation
  • Stephens, G. L., 2005: Cloud feedbacks in the climate system: A critical review. J. Climate, 18, 237273, https://doi.org/10.1175/JCLI-3243.1.

    • Search Google Scholar
    • Export Citation
  • Straka, J. M., and E. R. Mansell, 2005: A bulk microphysics parameterization with multiple ice precipitation categories. J. Appl. Meteor., 44, 445466, https://doi.org/10.1175/JAM2211.1.

    • Search Google Scholar
    • Export Citation
  • Strapp, J. W. , and Coauthors, 2016a: The high ice water content (HIWC) study of deep convective clouds: Science and technical plan. FAA Rep., DOT/FAA/TC-14/31, 105 pp.

  • Strapp, J. W., L. E. Lilie, T. P. Ratvasky, C. R. Davison, and C. Dumont, 2016b: Isokinetic TWC evaporator probe: Development of the IKP2 and performance testing for the HAIC-HIWC Darwin 2014 and Cayenne Field Campaigns. Proc. Eighth AIAA Atmospheric and Space Environments Conf., AIAA-2016-4059, Washington, DC, American Institute of Aeronautics and Astronautics, http://arc.aiaa.org/doi/10.2514/6.2016-4059.

  • Strapp, J. W. , and Coauthors, 2020: An assessment of cloud total water content and particle size from light test campaign measurements in high ice water content, mixed phase/ice crystal icing conditions: Primary in-situ measurements. FAA Rep., AU5 DOT/FAA/TC-18/1, 262 pp.

  • Strapp, J. W., and Coauthors, 2021: Comparisons of cloud in situ micro-physical properties of deep convective clouds to appendix D/P using data from the high-altitude ice crystals-high ice water content and high ice water content-RADAR I flight campaigns. SAE Int. J. Aerosp., 14, 127159, https://doi.org/10.4271/01-14-02-0007.

    • Search Google Scholar
    • Export Citation
  • Thompson, G., P. R. Field, R. M. Rasmussen, and W. D. Hall, 2008: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization. Mon. Wea. Rev., 136, 50955115, https://doi.org/10.1175/2008MWR2387.1.

    • Search Google Scholar
    • Export Citation
  • Tian, L., G. M. Heymsfield, L. Li, A. J. Heymsfield, A. Bansemer, C. H. Twohy, and R. C. Srivastava, 2010: A study of cirrus ice particle size distribution using TC4 observations. J. Atmos. Sci., 67, 195216, https://doi.org/10.1175/2009JAS3114.1.

    • Search Google Scholar
    • Export Citation
  • Van Weverberg, K., N. P. M. van Lipzig, and L. Delobbe, 2011: The impact of size distribution assumptions in a bulk one-moment microphysics scheme on simulated surface precipitation and storm dynamics during a low-topped supercell case in Belgium. Mon. Wea. Rev., 139, 11311147, https://doi.org/10.1175/2010MWR3481.1.

    • Search Google Scholar
    • Export Citation
  • Varley, D. J., 1978: Cirrus particle distribution study, Part I. Air Force Geophysical Laboratory Rep. AFGL-TR-78-0192, 71 pp.

  • Waitz, F., M. Schnaiter, T. Leisner, and E. Järvinen, 2021: 2021: PHIPS-HALO: The airborne Particle Habit Imaging and Polar Scattering probe—Part 3: Single-particle phase discrimination and particle size distribution based on the angular-scattering function. Atmos. Meas. Tech., 14, 30493070, https://doi.org/10.5194/amt-14-3049-2021.

    • Search Google Scholar
    • Export Citation
  • Walko, R. L., W. R. Cotton, M. P. Meyers, and J. Y. Harrington, 1995: New RAMS cloud microphysics. Part I: The one-moment scheme. Atmos. Res., 38, 2962, https://doi.org/10.1016/0169-8095(94)00087-T.

    • Search Google Scholar
    • Export Citation
  • Wang, Y., 2002: An explicit simulation of tropical cyclones with a triply nested movable mesh primitive equation model: TCM3. Part II: Model refinements and sensitivity to cloud microphysics parameterization. Mon. Wea. Rev., 130, 30223036, https://doi.org/10.1175/1520-0493(2002)130<3022:AESOTC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Welch, R. M., S. K. Cox, and J. M. Davis, 1980: Solar Radiation and Clouds. Meteor. Monogr., No. 39, Amer. Meteor. Soc., 93 pp.

  • Wolde, M., C. Nguyen, A. Korolev, and M. Bastian, 2016: Characterization of the Pilot X-band radar responses to the HIWC environment during the Cayenne HAIC-HIWC 2015 campaign. Eighth AIAA Atmospheric and Space Environments Conf., AIAA 2016-4201, Washington, DC, American Institute of Aeronautics and Astronautics, https://doi.org/10.2514/6.2016-4201.

  • Wolf, V., T. Kuhn, and M. Krämer, 2019: On the dependence of cirrus parametrizations on the cloud origin. Geophys. Res. Lett., 46, 12 56512 571, https://doi.org/10.1029/2019GL083841.

    • Search Google Scholar
    • Export Citation
  • Wong, R. K. W., and N. Chidambaram, 1985: Gamma size distribution and stochastic sampling errors. J. Climate Appl. Meteor., 24, 568579, https://doi.org/10.1175/1520-0450(1985)024,0568:GSDASS.2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wong, R. K. W., N. Chidambaram, L. Cheng, and M. English, 1988: The sampling variations of hailstone size distributions. J. Appl. Meteor., 27, 254260, https://doi.org/10.1175/1520-0450(1988)027<0254:TSVOHS>2.0.CO;2.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Wylie, D., D. L. Jackson, W. P. Menzel, and J. J. Bates, 2005: Trends in global cloud cover in two decades of HIRS observations. J. Climate, 18, 30213031, https://doi.org/10.1175/JCLI3461.1.

    • Search Google Scholar
    • Export Citation
  • Yost, C. R., and Coauthors, 2018: A prototype method for diagnosing high ice water content probability using satellite imager data. Atmos. Meas. Tech., 11, 16151637, https://doi.org/10.5194/amt-11-1615-2018.

    • Search Google Scholar
    • Export Citation
  • Zender, C. S., and J. T. Kiehl, 1997: Sensitivity of climate simulations to radiative effects of tropical anvil structure. J. Geophys. Res., 102, 23 793–23 803, https://doi.org/10.1029/97JD02009.

    • Search Google Scholar
    • Export Citation
  • Zhao, Y., G. G. Mace, and J. M. Comstock, 2010: The occurrence of particle size distribution bimodality in midlatitude cirrus from ground-based remote sensing data. J. Atmos. Sci., 68, 11621167, https://doi.org/10.1175/2010JAS3354.1.

    • Search Google Scholar
    • Export Citation
  • Zhu, T., and D. Zhang, 2006: Numerical simulation of Hurricane Bonnie (1998). Part II: Sensitivity to varying cloud microphysical processes. J. Atmos. Sci., 63, 109126, https://doi.org/10.1175/JAS3599.1.

    • Search Google Scholar
    • Export Citation
  • Zipser, E. J., C. Liu, D. J. Cecil, S. W. Nesbitt, and D. P. Yorty, 2006: Where are the most intense thunderstorms on Earth? Bull. Amer. Meteor. Soc., 87, 10571071, https://doi.org/10.1175/BAMS-87-8-1057.

    • Search Google Scholar
    • Export Citation
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Dependence of Ice Crystal Size Distributions in High Ice Water Content Conditions on Environmental Conditions: Results from the HAIC-HIWC Cayenne Campaign

Yachao HuaDepartment of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China
bCooperative Institute for Severe and High Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma

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Greg M. McFarquharbCooperative Institute for Severe and High Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma
cSchool of Meteorology, University of Oklahoma, Norman, Oklahoma

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Peter BrechnerbCooperative Institute for Severe and High Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma
cSchool of Meteorology, University of Oklahoma, Norman, Oklahoma

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Wei WubCooperative Institute for Severe and High Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma

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Yongjie HuangcSchool of Meteorology, University of Oklahoma, Norman, Oklahoma
dCenter for Analysis and Prediction of Storms, University of Oklahoma, Norman, Oklahoma

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Alexei KoroleveEnvironment and Climate Change Canada, Toronto, Ontario, Canada

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Alain ProtatfAustralian Bureau of Meteorology, Melbourne, Victoria, Australia

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Cuong NguyengNational Research Council Canada, Ottawa, Ontario, Canada

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Mengistu WoldegNational Research Council Canada, Ottawa, Ontario, Canada

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Alfons SchwarzenboeckhLaboratoire de Météorologie Physique, UCA, CNRS, Aubière, France

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Robert M. RauberiDepartment of Atmospheric Sciences, University of Illinois at Urbana–Champaign, Urbana, Illinois

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Hongqing WangaDepartment of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China

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Abstract

A new method that automatically determines the modality of an observed particle size distribution (PSD) and the representation of each mode as a gamma function was used to characterize data obtained during the High Altitude Ice Crystals and High Ice Water Content (HAIC-HIWC) project based out of Cayenne, French Guiana, in 2015. PSDs measured by a 2D stereo probe and a precipitation imaging probe for particles with maximum dimension (Dmax) > 55 μm were used to show how the gamma parameters varied with environmental conditions, including temperature (T) and convective properties such as cloud type, mesoscale convective system (MCS) age, distance away from the nearest convective peak, and underlying surface characteristics. Four kinds of modality PSDs were observed: unimodal PSDs and three types of multimodal PSDs (Bimodal1 with breakpoints 100 ± 20 μm between modes, Bimodal2 with breakpoints 1000 ± 300 μm, and Trimodal PSDs with two breakpoints). The T and ice water content (IWC) are the most important factors influencing the modality of PSDs, with the frequency of multimodal PSDs increasing with increasing T and IWC. An ellipsoid of equally plausible solutions in (Noλ–μ) phase space is defined for each mode of the observed PSDs for different environmental conditions. The percentage overlap between ellipsoids was used to quantify the differences between overlapping ellipsoids for varying conditions. The volumes of the ellipsoid decrease with increasing IWC for most cases, and (Noλ–μ) vary with environmental conditions related to distribution of IWC. HIWC regions are dominated by small irregular ice crystals and columns. The parameters (Noλ–μ) in each mode exhibit mutual dependence.

© 2022 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: Greg McFarquhar, mcfarq@ou.edu

Abstract

A new method that automatically determines the modality of an observed particle size distribution (PSD) and the representation of each mode as a gamma function was used to characterize data obtained during the High Altitude Ice Crystals and High Ice Water Content (HAIC-HIWC) project based out of Cayenne, French Guiana, in 2015. PSDs measured by a 2D stereo probe and a precipitation imaging probe for particles with maximum dimension (Dmax) > 55 μm were used to show how the gamma parameters varied with environmental conditions, including temperature (T) and convective properties such as cloud type, mesoscale convective system (MCS) age, distance away from the nearest convective peak, and underlying surface characteristics. Four kinds of modality PSDs were observed: unimodal PSDs and three types of multimodal PSDs (Bimodal1 with breakpoints 100 ± 20 μm between modes, Bimodal2 with breakpoints 1000 ± 300 μm, and Trimodal PSDs with two breakpoints). The T and ice water content (IWC) are the most important factors influencing the modality of PSDs, with the frequency of multimodal PSDs increasing with increasing T and IWC. An ellipsoid of equally plausible solutions in (Noλ–μ) phase space is defined for each mode of the observed PSDs for different environmental conditions. The percentage overlap between ellipsoids was used to quantify the differences between overlapping ellipsoids for varying conditions. The volumes of the ellipsoid decrease with increasing IWC for most cases, and (Noλ–μ) vary with environmental conditions related to distribution of IWC. HIWC regions are dominated by small irregular ice crystals and columns. The parameters (Noλ–μ) in each mode exhibit mutual dependence.

© 2022 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: Greg McFarquhar, mcfarq@ou.edu
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