Lagrangian Investigation of the Precipitation Efficiency of Convective Clouds

Wolfgang Langhans Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California

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Kyongmin Yeo IBM Thomas J. Watson Research Center, Yorktown Heights, New York

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David M. Romps Department of Earth and Planetary Science, University of California, Berkeley, and Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California

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Abstract

The precipitation efficiency of cumulus congestus clouds is investigated with a new Lagrangian particle framework for large-eddy simulations. The framework is designed to track particles representative of individual water molecules. A Monte Carlo approach facilitates the transition of particles between the different water classes (e.g., vapor, rain, or graupel). With this framework, it is possible to reconstruct the pathways of water as it moves from vapor at a particular altitude to rain at the surface. By tracking water molecules through both physical and microphysical space, the precipitation efficiency can be studied in detail as a function of height.

Large-eddy simulations of individual cumulus congestus clouds show that the clouds convert entrained vapor to surface precipitation with an efficiency of around 10%. About two-thirds of all vapor that enters the cloud does so by entrainment in the free troposphere, but free-tropospheric vapor accounts for only one-third to one-half of the surface rainfall, with the remaining surface rainfall originating as vapor entrained through the cloud base. The smaller efficiency with which that laterally entrained water is converted into surface precipitation results from the smaller efficiencies with which it condenses, forms precipitating hydrometeors, and reaches the surface.

Corresponding author address: Wolfgang Langhans, Lawrence Berkeley National Laboratory, Earth Sciences Division, 1 Cyclotron Road, Mail Stop 74R316C, Berkeley, CA 94720. E-mail: wlanghans@lbl.gov

Abstract

The precipitation efficiency of cumulus congestus clouds is investigated with a new Lagrangian particle framework for large-eddy simulations. The framework is designed to track particles representative of individual water molecules. A Monte Carlo approach facilitates the transition of particles between the different water classes (e.g., vapor, rain, or graupel). With this framework, it is possible to reconstruct the pathways of water as it moves from vapor at a particular altitude to rain at the surface. By tracking water molecules through both physical and microphysical space, the precipitation efficiency can be studied in detail as a function of height.

Large-eddy simulations of individual cumulus congestus clouds show that the clouds convert entrained vapor to surface precipitation with an efficiency of around 10%. About two-thirds of all vapor that enters the cloud does so by entrainment in the free troposphere, but free-tropospheric vapor accounts for only one-third to one-half of the surface rainfall, with the remaining surface rainfall originating as vapor entrained through the cloud base. The smaller efficiency with which that laterally entrained water is converted into surface precipitation results from the smaller efficiencies with which it condenses, forms precipitating hydrometeors, and reaches the surface.

Corresponding author address: Wolfgang Langhans, Lawrence Berkeley National Laboratory, Earth Sciences Division, 1 Cyclotron Road, Mail Stop 74R316C, Berkeley, CA 94720. E-mail: wlanghans@lbl.gov
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  • Auer, A. H., and J. D. Marwitz, 1968: Estimates of air and moisture flux into hailstorms on the high plains. J. Appl. Meteor., 7, 196198, doi:10.1175/1520-0450(1968)007<0196:EOAAMF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Batchelor, G. K., 1954: Heat convection and buoyant effects in fluids. Quart. J. Roy. Meteor. Soc., 80, 339358, doi:10.1002/qj.49708034504.

    • Search Google Scholar
    • Export Citation
  • Blyth, A. M., W. A. Cooper, and J. B. Jensen, 1988: A study of the source of entrained air in Montana cumuli. J. Atmos. Sci., 45, 39443964, doi:10.1175/1520-0469(1988)045<3944:ASOTSO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Blyth, A. M., S. G. Lasher-Trapp, and W. A. Cooper, 2005: A study of thermals in cumulus clouds. Quart. J. Roy. Meteor. Soc., 131, 11711190, doi:10.1256/qj.03.180.

    • Search Google Scholar
    • Export Citation
  • Boing, S. J., H. J. J. Jonker, A. P. Siebesma, and W. W. Grabowski, 2012: Influence of the subcloud layer on the development of a deep convective ensemble. J. Atmos. Sci., 69, 26822698, doi:10.1175/JAS-D-11-0317.1.

    • Search Google Scholar
    • Export Citation
  • Braham, R. R., 1952: The water and energy budgets of the thunderstorm and their relation to thunderstorm development. J. Meteor., 9, 227242, doi:10.1175/1520-0469(1952)009<0227:TWAEBO>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, 202–225, doi:10.1175/MWR-D-11-00046.1.

    • Search Google Scholar
    • Export Citation
  • Carpenter, R. L., Jr., K. K. Droegemeier, and A. M. Blyth, 1998: Entrainment and detrainment in numerically simulated cumulus congestus clouds. Part III: Parcel analysis. J. Atmos. Sci., 55, 34403455, doi:10.1175/1520-0469(1998)055<3440:EADINS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Cohen, C., and E. W. McCaul Jr., 2007: Further results on the sensitivity of simulated storm precipitation efficiency to environmental temperature. Mon. Wea. Rev., 135, 16711684, doi:10.1175/MWR3380.1.

    • Search Google Scholar
    • Export Citation
  • Damiani, R., G. Vali, and S. Haimov, 2006: The structure of thermals in cumulus from airborne dual-Doppler radar observations. J. Atmos. Sci., 63, 14321450, doi:10.1175/JAS3701.1.

    • Search Google Scholar
    • Export Citation
  • Emanuel, K. A., 1981: A similarity theory for unsaturated downdrafts within clouds. J. Atmos. Sci., 38, 15411557, doi:10.1175/1520-0469(1981)038<1541:ASTFUD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Fankhauser, J. C., 1988: Estimates of thunderstorm precipitation efficiency from field measurements in CCOPE. Mon. Wea. Rev., 116, 663684, doi:10.1175/1520-0493(1988)116<0663:EOTPEF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Ferrier, B. S., J. Simpson, and W. K. Tao, 1996: Factors responsible for precipitation efficiencies in midlatitude and tropical squall simulations. Mon. Wea. Rev., 124, 21002125, doi:10.1175/1520-0493(1996)124<2100:FRFPEI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Heus, T., G. Van Dijk, H. J. J. Jonker, and H. E. A. Van den Akker, 2008: Mixing in shallow cumulus clouds studied by Lagrangian particle tracking. J. Atmos. Sci., 65, 25812597, doi:10.1175/2008JAS2572.1.

    • Search Google Scholar
    • Export Citation
  • Khain, A. P., N. BenMoshe, and A. Pokrovsky, 2008: Factors determining the impact of aerosols on surface precipitation from clouds: An attempt at classification. J. Atmos. Sci., 65, 17211748, doi:10.1175/2007JAS2515.1.

    • Search Google Scholar
    • Export Citation
  • Kirshbaum, D. J., and R. B. Smith, 2008: Temperature and moist-stability effects on midlatitude orographic precipitation. Quart. J. Roy. Meteor. Soc., 134, 11831199, doi:10.1002/qj.274.

    • Search Google Scholar
    • Export Citation
  • Klemp, J. B., W. C. Skamarock, and J. Dudhia, 2007: Conservative split-explicit time integration methods for the compressible nonhydrostatic equations. Mon. Wea. Rev., 135, 28972913, doi:10.1175/MWR3440.1.

    • Search Google Scholar
    • Export Citation
  • Knight, C. G., and Coauthors, 2007: Association of parameter, software, and hardware variation with large-scale behavior across 57,000 climate models. Proc. Natl. Acad. Sci. USA, 104, 12 25912 264, doi:10.1073/pnas.0608144104.

    • Search Google Scholar
    • Export Citation
  • Krueger, S. K., Q. A. Fu, K. N. Liou, and H. N. S. Chin, 1995: Improvements of an ice-phase microphysics parameterization for use in numerical simulations of tropical convection. J. Appl. Meteor., 34, 281287, doi:10.1175/1520-0450-34.1.281.

    • Search Google Scholar
    • Export Citation
  • Kuang, Z., and C. S. Bretherton, 2006: A mass-flux scheme view of a high-resolution simulation of a transition from shallow to deep cumulus convection. J. Atmos. Sci., 63, 18951909, doi:10.1175/JAS3723.1.

    • Search Google Scholar
    • Export Citation
  • Lasher-Trapp, S. G., W. A. Cooper, and A. M. Blyth, 2005: Broadening of droplet size distributions from entrainment and mixing in a cumulus cloud. Quart. J. Roy. Meteor. Soc., 131, 195220, doi:10.1256/qj.03.199.

    • Search Google Scholar
    • Export Citation
  • Lin, Y.-L., D. F. Richard, and H. D. Orville, 1983: Bulk parameterization of the snow field in a cloud model. J. Appl. Meteor., 22, 10651092, doi:10.1175/1520-0450(1983)022<1065:BPOTSF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Lord, S. J., H. E. Willoughby, and J. M. Piotrowicz, 1984: Role of a parameterized ice-phase microphysics in an axisymmetric, nonhydrostatic tropical cyclone model. J. Atmos. Sci., 41, 28362848, doi:10.1175/1520-0469(1984)041<2836:ROAPIP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Margolin, L. G., W. J. Rider, and F. F. Grinstein, 2006: Modeling turbulent flow with implicit LES. J. Turbul., 7, 127, doi:10.1080/14685240500331595.

    • Search Google Scholar
    • Export Citation
  • Muller, C., P. A. O’Gorman, and L. E. Back, 2011: Intensification of precipitation extremes with warming in a cloud-resolving model. J. Climate, 24, 27842800, doi:10.1175/2011JCLI3876.1.

    • Search Google Scholar
    • Export Citation
  • Murray, F. W., and L. R. Koenig, 1972: Numerical experiments on the relation between microphysics and dynamics in cumulus convection. Mon. Wea. Rev., 100, 717732, doi:10.1175/1520-0493(1972)100<0717:NEOTRB>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • O’Gorman, P. A., and C. J. Muller, 2010: How closely do changes in surface and column water vapor follow Clausius–Clapeyron scaling in climate change simulations? Environ. Res. Lett.,5, 025207, doi:10.1088/1748-9326/5/2/025207.

  • Pauluis, O., and I. M. Held, 2002: Entropy budget of an atmosphere in radiative–convective equilibrium. Part I: Maximum work and frictional dissipation. J. Atmos. Sci., 59, 125139, doi:10.1175/1520-0469(2002)059<0125:EBOAAI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Phillips, V. T. J., and Coauthors, 2005: Anvil glaciation in a deep cumulus updraught over Florida simulated with the Explicit Microphysics Model. I: Impact of various nucleation processes. Quart. J. Roy. Meteor. Soc., 131, 20192046, doi:10.1256/qj.04.85.

    • Search Google Scholar
    • Export Citation
  • Respondek, P. S., A. I. Flossmann, R. R. Alheit, and H. R. Pruppacher, 1995: A theoretical study of the wet removal of atmospheric pollutants. Part V: The uptake, redistribution, and deposition of (NH4)2SO4 by a convective cloud containing ice. J. Atmos. Sci., 52, 21212132, doi:10.1175/1520-0469(1995)052<2121:ATSOTW>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Romps, D. M., 2008: The dry-entropy budget of a moist atmosphere. J. Atmos. Sci., 65, 37793799, doi:10.1175/2008JAS2679.1.

  • Romps, D. M., 2011: Response of tropical precipitation to global warming. J. Atmos. Sci., 68, 123138, doi:10.1175/2010JAS3542.1.

  • Romps, D. M., and Z. Kuang, 2010: Do undiluted convective plumes exist in the upper tropical troposphere? J. Atmos. Sci., 67, 468–484, doi:10.1175/2009JAS3184.1.

    • Search Google Scholar
    • Export Citation
  • Romps, D. M., and Z. Kuang, 2011: A transilient matrix for moist convection. J. Atmos. Sci., 68, 20092025, doi:10.1175/2011JAS3712.1.

    • Search Google Scholar
    • Export Citation
  • Rutledge, S. A., and P. V. Hobbs, 1983: The mesoscale and microscale structure and organization of clouds and precipitation in mid-latitude cyclones. VIII. A model for the “seeder-feeder” process in warm-frontal rainbands. J. Atmos. Sci., 40, 11851206, doi:10.1175/1520-0469(1983)040<1185:TMAMSA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • 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, doi:10.1007/s00382-007-0280-7.

    • Search Google Scholar
    • Export Citation
  • Scorer, R. S., and F. H. Ludlam, 1953: Bubble theory of penetrative convection. Quart. J. Roy. Meteor. Soc., 79, 94, doi:10.1002/qj.49707933908.

    • Search Google Scholar
    • Export Citation
  • Shu, C.-W., 1998: Essentially non-oscillatory and weighted essentially non-oscillatory schemes for hyperbolic conservation laws. Advanced Numerical Approximation of Nonlinear Hyperbolic Equations, A. Quarteroni, Ed., Lecture Notes in Mathematics, Vol. 1697, Springer, 325–432.

  • Shu, C.-W., and S. Osher, 1988: Efficient implementation of essentially non-oscillatory shock-capturing schemes. J. Comput. Phys., 77, 439471, doi:10.1016/0021-9991(88)90177-5.

    • Search Google Scholar
    • Export Citation
  • Simpson, J., and V. Wiggert, 1969: Models of precipitating cumulus towers. Mon. Wea. Rev., 97, 471489, doi:10.1175/1520-0493(1969)097<0471:MOPCT>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • Singh, M. S., and P. A. O’Gorman, 2013: Influence of entrainment on the thermal stratification in simulations of radiative-convective equilibrium. Geophys. Res. Lett., 40, 43984403, doi:10.1002/grl.50796.

    • Search Google Scholar
    • Export Citation
  • Smith, R. B., Q. Jiang, M. G. Fearon, P. Tabary, M. Dorninger, J. Doyle, and R. Benoit, 2003: Orographic precipitation and air mass transformation: An Alpine example. Quart. J. Roy. Meteor. Soc., 129, 433454, doi:10.1256/qj.01.212.

    • Search Google Scholar
    • Export Citation
  • Stull, R. B., 1984: Transilient turbulence theory. Part I: The concept of eddy-mixing across finite distances. J. Atmos. Sci., 41, 33513367, doi:10.1175/1520-0469(1984)041<3351:TTTPIT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Sui, C.-H., X. Li, M.-J. Yang, and H.-L. Huang, 2005: Estimation of oceanic precipitation efficiency in cloud models. J. Atmos. Sci., 62, 43584370, doi:10.1175/JAS3587.1.

    • Search Google Scholar
    • Export Citation
  • Sui, C.-H., X. Li, and M.-J. Yang, 2007: On the definition of precipitation efficiency. J. Atmos. Sci., 64, 45064513, doi:10.1175/2007JAS2332.1.

    • Search Google Scholar
    • Export Citation
  • Thuburn, J., 1996: Multidimensional flux-limited advection schemes. J. Comput. Phys., 123, 7483, doi:10.1006/jcph.1996.0006.

  • van den Heever, S. C., G. L. Stephens, and N. B. Wood, 2011: Aerosol indirect effects on tropical convection characteristics under conditions of radiative–convective equilibrium. J. Atmos. Sci., 68, 699718, doi:10.1175/2010JAS3603.1.

    • Search Google Scholar
    • Export Citation
  • Weisman, M. L., and J. B. Klemp, 1982: The dependence of numerically simulated convective storms on vertical wind shear and buoyancy. Mon. Wea. Rev., 110, 504520, doi:10.1175/1520-0493(1982)110<0504:TDONSC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Yang, B., Y. Qian, G. Lin, R. Leung, Y. Zhang, and Y. Zhang, 2012: Some issues in uncertainty quantification and parameter tuning: a case study of convective parameterization scheme in the WRF regional climate model. Atmos. Phys. Chem., 12, 24092427, doi:10.5194/acp-12-2409-2012.

    • Search Google Scholar
    • Export Citation
  • Yang, B., and Coauthors, 2013: Uncertainty quantification and parameter tuning in the CAM5 Zhang-McFarlane convection scheme and impact of improved convection on the global circulation and climate. J. Geophys. Res. Atmos., 118, 395415, doi:10.1029/2012JD018213.

    • Search Google Scholar
    • Export Citation
  • Yeo, K., and D. M. Romps, 2013: Measurement of convective entrainment using Lagrangian particles. J. Atmos. Sci., 70, 266277, doi:10.1175/JAS-D-12-0144.1.

    • Search Google Scholar
    • Export Citation
  • Zhao, M., and P. H. Austin, 2005: Life cycle of numerically simulated shallow cumulus clouds. Part II: Mixing dynamics. J. Atmos. Sci., 62, 12911310, doi:10.1175/JAS3415.1.

    • Search Google Scholar
    • Export Citation
  • Zipser, E. J., 2003: Some views on hot towers after 50 years of tropical field programs and two years of TRMM data. Cloud Systems, Hurricanes, and the Tropical Rainfall Measuring Mission (TRMM), Meteor. Monogr., No. 51, Amer. Meteor. Soc., 49–58.

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