• Beard, K. V., , and H. T. Ochs, 1993: Warm rain initiation: An overview of microphysical mechanisms. J. Appl. Meteor., 32, 608625, doi:10.1175/1520-0450(1993)032<0608:WRIAOO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Bogenschutz, P. A., , S. K. Krueger, , and M. Khairoutdinov, 2010: Assumed probability density functions for shallow and deep convection. J. Adv. Model. Earth Syst., 2 (10), doi:10.3894/JAMES.2010.2.10.

    • Search Google Scholar
    • Export Citation
  • Casey, S. P. F., , A. E. Dessler, , and C. Schumacher, 2007: Frequency of tropical precipitating clouds as observed by the Tropical Rainfall Measuring Mission Precipitation Radar and ICE Sat/Geoscience Laser Altimeter System. J. Geophys. Res., 112, D14215, doi:10.1029/2007JD008468.

    • Search Google Scholar
    • Export Citation
  • Casey, S. P. F., , E. J. Fetzer, , and B. H. Kahn, 2012: Revised identification of tropical oceanic cumulus congestus as viewed by CloudSat. Atmos. Chem. Phys., 12, 15871595, doi:10.5194/acp-12-1587-2012.

    • Search Google Scholar
    • Export Citation
  • Cheng, C.-P., , and R. A. Houze Jr., 1979: The distribution of convective and mesoscale precipitation in GATE radar echo patterns. Mon. Wea. Rev., 107, 13701381, doi:10.1175/1520-0493(1979)107<1370:TDOCAM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Comstock, K. M., , C. S. Bretherton, , and S. E. Yuter, 2005: Mesoscale variability and drizzle in southeast Pacific stratocumulus. J. Atmos. Sci., 62, 37923807, doi:10.1175/JAS3567.1.

    • Search Google Scholar
    • Export Citation
  • Godfrey, J. S., , R. A. Houze Jr., , R. H. Johnson, , R. Lukas, , J.-L. Redelsperger, , A. Sumi, , and R. Weller, 1998: Coupled Ocean-Atmosphere Response Experiment (COARE): An interim report. J. Geophys. Res., 103, 14 39514 450, doi:10.1029/97JC03120.

    • Search Google Scholar
    • Export Citation
  • Golaz, J.-C., , V. E. Larson, , and W. R. Cotton, 2002: A PDF-based model for boundary layer clouds. Part I: Method and model description. J. Atmos. Sci., 59, 35403551, doi:10.1175/1520-0469(2002)059<3540:APBMFB>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hohenegger, C., , and B. Stevens, 2013: Preconditioning deep convection with cumulus congestus. J. Atmos. Sci., 70, 448464, doi:10.1175/JAS-D-12-089.1.

    • Search Google Scholar
    • Export Citation
  • Hollars, S., , Q. Fu, , J. Comstock, , and T. Ackerman, 2004: Comparison of cloud-top height retrievals from ground-based 35 GHz MMCR and GMS-5 satellite observations at ARM TWP Manus site. Atmos. Res., 72, 169186, doi:10.1016/j.atmosres.2004.03.015.

    • Search Google Scholar
    • Export Citation
  • Jensen, M. P., , and A. D. Del Genio, 2006: Factors limiting convective cloud-top height at the ARM Nauru island climate research facility. J. Climate, 19, 21052117, doi:10.1175/JCLI3722.1.

    • Search Google Scholar
    • Export Citation
  • Johnson, R. H., , T. M. Rickenbach, , S. A. Rutledge, , P. E. Ciesielski, , and W. H. Schubert, 1999: Trimodal characteristics of tropical convection. J. Climate, 12, 23972418, doi:10.1175/1520-0442(1999)012<2397:TCOTC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Khairoutdinov, M. F., , and Y. L. Kogan, 1999: A large eddy simulation model with explicit microphysics: Validation against aircraft observations of a stratocumulus-topped boundary layer. J. Atmos. Sci., 56, 21152131, doi:10.1175/1520-0469(1999)056<2115:ALESMW>2.0.CO;2.

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

    • Search Google Scholar
    • Export Citation
  • Khairoutdinov, M. F., , and D. A. Randall, 2002: Similarity of deep continental cumulus convection as revealed by a three-dimensional cloud-resolving model. J. Atmos. Sci., 59, 25502566, doi:10.1175/1520-0469(2002)059<2550:SODCCC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Khairoutdinov, M. F., , and D. A. Randall, 2003: Cloud resolving modeling of the ARM summer 1997 IOP: Model formulation, results, uncertainties, and sensitivities. J. Atmos. Sci., 60, 607625, doi:10.1175/1520-0469(2003)060<0607:CRMOTA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Khairoutdinov, M. F., , S. K. Krueger, , C.-H. Moeng, , P. A. Bogenschultz, , and D. A. Randall, 2009: Large-eddy simulation of marine deep tropical convection. J. Adv. Model. Earth Syst., 1 (15), doi:10.3894/JAMES.2009.1.15.

    • Search Google Scholar
    • Export Citation
  • Kogan, Y. L., 2013: A cumulus cloud microphysics parameterization for cloud-resolving models. J. Atmos. Sci., 70, 14231436, doi:10.1175/JAS-D-12-0183.1.

    • Search Google Scholar
    • Export Citation
  • Kogan, Y. L., , and D. B. Mechem, 2014: A PDF-based microphysics parameterization for shallow cumulus cloud. J. Atmos. Sci., 71, 10701089, doi:10.1175/JAS-D-13-0193.1.

    • Search Google Scholar
    • Export Citation
  • Kogan, Y. L., , M. P. Khairoutdinov, , D. K. Lilly, , Z. N. Kogan, , and Q. Liu, 1995: Modeling of stratocumulus cloud layers in a large eddy simulation model with explicit microphysics. J. Atmos. Sci., 52, 29232940, doi:10.1175/1520-0469(1995)052<2923:MOSCLI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Kogan, Y. L., , D. B. Mechem, , and K. Choi, 2012: Effects of sea-salt aerosols on precipitation in simulations of shallow cumulus. J. Atmos. Sci., 69, 463483, doi:10.1175/JAS-D-11-031.1.

    • Search Google Scholar
    • Export Citation
  • Larson, V. E., , and J.-C. Golaz, 2005: Using probability density functions to derive consistent closure relationships among higher-order moments. Mon. Wea. Rev., 133, 10231042, doi:10.1175/MWR2902.1.

    • Search Google Scholar
    • Export Citation
  • Larson, V. E., , R. Wood, , P. R. Field, , J.-C. Golaz, , T. H. Vonder Harr, , and W. R. Cotton, 2001: Systematic biases in the microphysics and thermodynamics of numerical models that ignore subgrid-scale variability. J. Atmos. Sci., 58, 11171128, doi:10.1175/1520-0469(2001)058<1117:SBITMA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Larson, V. E., , J.-C. Golaz, , and W. R. Cotton, 2002: Small-scale and mesoscale variability in cloudy boundary layers: Joint probability density functions. J. Atmos. Sci., 59, 35193539, doi:10.1175/1520-0469(2002)059<3519:SSAMVI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Larson, V. E., , D. P. Schanen, , M. Wang, , M. Ovchinnikov, , and S. Ghan, 2012: PDF parameterization of boundary layer clouds in models with horizontal grid spacings from 2 to 16 km. Mon. Wea. Rev., 140, 285306, doi:10.1175/MWR-D-10-05059.1.

    • Search Google Scholar
    • Export Citation
  • Lebsock, M., , H. Morrison, , and A. Gettelman, 2013: Microphysical implications of cloud-precipitation covariance derived from satellite remote sensing. J. Geophys. Res. Atmos., 118, 65216533, doi:10.1002/jgrd.50347.

    • Search Google Scholar
    • Export Citation
  • Li, C., , X. Jia, , J. Ling, , W. Zhou, , and C. Zhang, 2009: Sensitivity of MJO simulations to diabatic heating profiles. Climate Dyn., 32, 167187, doi:10.1007/s00382-008-0455-x.

    • Search Google Scholar
    • Export Citation
  • Liu, C., , and E. J. Zipser, 2009: “Warm rain” in the tropics: Seasonal and regional distributions based on 9 yr of TRMM data. J. Climate, 22, 767779, doi:10.1175/2008JCLI2641.1.

    • Search Google Scholar
    • Export Citation
  • Luo, Z., , G. Y. Liu, , G. L. Stephens, , and R. H. Johnson, 2009: Terminal versus transient cumulus congestus: A CloudSat perspective. Geophys. Res. Lett., 36, L05808, doi:10.1029/2008GL036927.

    • Search Google Scholar
    • Export Citation
  • Madden, R., , and P. Julian, 1971: Detection of a 40–50 day oscillation in the zonal wind in the tropical Pacific. J. Atmos. Sci., 28, 702708, doi:10.1175/1520-0469(1971)028<0702:DOADOI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Madden, R., , and P. Julian, 1972: Description of global-scale circulation cells in the tropics with a 40–50-day period. J. Atmos. Sci., 29, 11091123, doi:10.1175/1520-0469(1972)029<1109:DOGSCC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Malkus, J. S., 1962: Large-scale interactions. Physical Oceanography, M. N. Hill, Ed., The Sea—Ideas and Observations on Progress in the Study of the Seas, Vol. 1, John Wiley and Sons, 88–294.

  • Malkus, J. S., , and H. Riehl, 1964: Cloud Structure and Distributions over the Tropical Pacific Ocean. University of California Press, 229 pp.

  • Mechem, D. B., , and A. J. Oberthaler, 2013: Numerical simulation of tropical cumulus congestus during TOGA COARE. J. Adv. Model. Earth Syst., 5, 623637, doi:10.1002/jame.20043.

    • Search Google Scholar
    • Export Citation
  • Mechem, D. B., , P. C. Robinson, , and Y. L. Kogan, 2006: Processing of cloud condensation nuclei by collision-coalescence in a mesoscale model. J. Geophys. Res., 111, D18204, doi:10.1029/2006JD007183.

    • Search Google Scholar
    • Export Citation
  • Pincus, R., , and S. A. Klein, 2000: Unresolved spatial variability and microphysical process rates in large-scale models. J. Geophys. Res., 105, 27 05927 065, doi:10.1029/2000JD900504.

    • Search Google Scholar
    • Export Citation
  • Rauber, R., , H. T. Ochs III, , L. Di Girolamo, , S. Göke, , and E. Snodgrass, 2007: Rain in Shallow Cumulus over the Ocean—The RICO campaign. Bull. Amer. Meteor. Soc., 88, 19121928, doi:10.1175/BAMS-88-12-1912.

    • Search Google Scholar
    • Export Citation
  • Smolarkiewicz, P. K., , and W. W. Grabowski, 1990: The multidimensional positive definite advection transport algorithm: Non-oscillatory option. J. Comput. Phys., 86, 355375, doi:10.1016/0021-9991(90)90105-A.

    • Search Google Scholar
    • Export Citation
  • Stephens, G. L., , and N. B. Wood, 2007: Properties of tropical convection observed by millimeter-wave radar systems. Mon. Wea. Rev., 135, 821842, doi:10.1175/MWR3321.1.

    • Search Google Scholar
    • Export Citation
  • vanZanten, M. C., and et al. , 2011: Controls on precipitation and cloudiness in simulations of trade-wind cumulus as observed during RICO. J. Adv. Model. Earth Syst., 3, M06001, doi:10.1029/2011MS000056.

    • Search Google Scholar
    • Export Citation
  • Waite, M. L., , and B. Khouider, 2010: The deepening of tropical convection by congestus preconditioning. J. Atmos. Sci., 67, 26012615, doi:10.1175/2010JAS3357.1.

    • Search Google Scholar
    • Export Citation
  • Wood, R., 2006: Rate of loss of cloud droplets by coalescence in warm clouds. J. Geophys. Res., 111, D21205, doi:10.1029/2006JD007553.

  • Wood, R., , P. R. Field, , and W. R. Cotton, 2002: Autoconversion rate bias in stratiform boundary layer cloud parameterizations. Atmos. Res., 65, 109128, doi:10.1016/S0169-8095(02)00071-6.

    • Search Google Scholar
    • Export Citation
  • Zhang, M. H., , and J. L. Lin, 1997: Constrained variational analysis of sounding data based on column-integrated budgets of mass, heat, moisture, and momentum: Approach and application to ARM measurements. J. Atmos. Sci., 54, 15031524, doi:10.1175/1520-0469(1997)054<1503:CVAOSD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 23 23 3
PDF Downloads 22 22 5

A PDF-Based Formulation of Microphysical Variability in Cumulus Congestus Clouds

View More View Less
  • 1 Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, Oklahoma
  • | 2 Atmospheric Science Program, Department of Geography, University of Kansas, Lawrence, Kansas
© Get Permissions
Restricted access

Abstract

Calculating unbiased microphysical process rates over mesoscale model grid volumes necessitates knowledge of the subgrid-scale (SGS) distribution of variables, typically represented as probability distribution functions (PDFs) of the prognostic variables. In the 2014 Journal of the Atmospheric Sciences paper by Kogan and Mechem, they employed large-eddy simulation of Rain in Cumulus over the Ocean (RICO) trade cumulus to develop PDFs and joint PDFs of cloud water, rainwater, and droplet concentration. In this paper, the approach of Kogan and Mechem is extended to deeper, precipitating cumulus congestus clouds as represented by a simulation based on conditions from the TOGA COARE field campaign. The fidelity of various PDF approximations was assessed by evaluating errors in estimating autoconversion and accretion rates. The dependence of the PDF shape on grid-mean variables is much stronger in congestus clouds than in shallow cumulus. The PDFs obtained from the TOGA COARE simulations for the calculation of accretion rates may be applied to both shallow and congestus cumulus clouds. However, applying the TOGA COARE PDFs to calculate autoconversion rates introduces unacceptably large errors in shallow cumulus clouds, thus precluding the use of a “universal” PDF formulation for both cloud types.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JAS-D-15-0129.s1.

Corresponding author address: Yefim Kogan, Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, 120 David L. Boren Blvd., Suite 2100, Norman, OK 73072-7304. E-mail: ykogan@ou.edu

Abstract

Calculating unbiased microphysical process rates over mesoscale model grid volumes necessitates knowledge of the subgrid-scale (SGS) distribution of variables, typically represented as probability distribution functions (PDFs) of the prognostic variables. In the 2014 Journal of the Atmospheric Sciences paper by Kogan and Mechem, they employed large-eddy simulation of Rain in Cumulus over the Ocean (RICO) trade cumulus to develop PDFs and joint PDFs of cloud water, rainwater, and droplet concentration. In this paper, the approach of Kogan and Mechem is extended to deeper, precipitating cumulus congestus clouds as represented by a simulation based on conditions from the TOGA COARE field campaign. The fidelity of various PDF approximations was assessed by evaluating errors in estimating autoconversion and accretion rates. The dependence of the PDF shape on grid-mean variables is much stronger in congestus clouds than in shallow cumulus. The PDFs obtained from the TOGA COARE simulations for the calculation of accretion rates may be applied to both shallow and congestus cumulus clouds. However, applying the TOGA COARE PDFs to calculate autoconversion rates introduces unacceptably large errors in shallow cumulus clouds, thus precluding the use of a “universal” PDF formulation for both cloud types.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JAS-D-15-0129.s1.

Corresponding author address: Yefim Kogan, Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, 120 David L. Boren Blvd., Suite 2100, Norman, OK 73072-7304. E-mail: ykogan@ou.edu

Supplementary Materials

    • Supplemental Materials (TAR 70.0 KB)
Save