• Bogenschutz, P. A., and S. K. Krueger, 2013: A simplified PDF parameterization of subgrid-scale clouds and turbulence for cloud-resolving models. J. Adv. Model. Earth Syst., 5, 195211, doi:10.1002/jame.20018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chowdhary, K., M. Salloum, B. Debusschere, and V. E. Larson, 2015: Quadrature methods for the calculation of subgrid microphysics moments. Mon. Wea. Rev., 143, 29552972, doi:10.1175/MWR-D-14-00168.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clough, S. A., M. W. Shephard, E. J. Mlawer, J. S. Delamere, M. J. Iacono, K. Cady-Pereira, S. Boukabara, and P. D. Brown, 2005: Atmospheric radiative transfer modeling: A summary of the AER codes. J. Quant. Spectrosc. Radiat. Transfer, 91, 233244, doi:10.1016/j.jqsrt.2004.05.058.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fridlind, A. M., and et al. , 2012: A comparison of TWP-ICE observational data with cloud-resolving model results. J. Geophys. Res., 117, D05204, doi:10.1029/2011JD016595.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gettelman, A., H. Morrison, S. Santos, P. Bogenschutz, and P. M. Caldwell, 2015: Advanced two-moment bulk microphysics for global models. Part II: Global model solutions and aerosol–cloud interactions. J. Climate, 28, 12881307, doi:10.1175/JCLI-D-14-00103.1.

    • Crossref
    • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Griffin, B. M., and V. E. Larson, 2013: Analytic upscaling of a local microphysics scheme. Part II: Simulations. Quart. J. Roy. Meteor. Soc., 139, 5869, doi:10.1002/qj.1966.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Griffin, B. M., and V. E. Larson, 2016: A new subgrid-scale representation of hydrometeor fields using a multivariate PDF. Geosci. Model Dev., 9, 20312053, doi:10.5194/gmd-9-2031-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guo, H., J.-C. Golaz, L. J. Donner, P. Ginoux, and R. S. Hemler, 2014: Multivariate probability density functions with dynamics in the GFDL atmospheric general circulation model: Global tests. J. Climate, 27, 20872108, doi:10.1175/JCLI-D-13-00347.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guo, H., J.-C. Golaz, L. J. Donner, B. Wyman, M. Zhao, and P. Ginoux, 2015: CLUBB as a unified cloud parameterization: Opportunities and challenges. Geophys. Res. Lett., 42, 45404547, doi:10.1002/2015GL063672.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guo, Z., M. Wang, Y. Qian, and V. E. Larson, 2015: Parametric behaviors of CLUBB in simulations of low clouds in the Community Atmosphere Model (CAM). J. Adv. Model. Earth Syst., 7, 10051025, doi:10.1002/2014MS000405.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Iacono, M. J., J. S. Delamere, E. J. Mlawer, M. W. Shephard, S. A. Clough, and W. D. Collins, 2008: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. J. Geophys. Res., 113, D13103, doi:10.1029/2008JD009944.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jensen, M. P., and et al. , 2015: The Midlatitude Continental Convective Clouds Experiment (MC3E) sounding network: Operations, processing and analysis. Atmos. Meas. Tech., 8, 421434, doi:10.5194/amt-8-421-2015.

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

    • Crossref
    • 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.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kogan, Y. L., and D. B. Mechem, 2016: A PDF-based formulation of microphysical variability in cumulus congestus clouds. J. Atmos. Sci., 73, 167184, doi:10.1175/JAS-D-15-0129.1.

    • Crossref
    • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Larson, V. E., and B. M. Griffin, 2013: Analytic upscaling of a local microphysics scheme. Part I: Derivation. Quart. J. Roy. Meteor. Soc., 139, 4657, doi:10.1002/qj.1967.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Larson, V. E., and D. P. Schanen, 2013: The Subgrid Importance Latin Hypercube Sampler (SILHS): A multivariate subcolumn generator. Geosci. Model Dev., 6, 18131829, doi:10.5194/gmd-6-1813-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Larson, V. E., R. Wood, P. R. Field, J.-C. Golaz, T. H. V. Haar, 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Larson, V. E., B. J. Nielsen, J. Fan, and M. Ovchinnikov, 2011: Parameterizing correlations between hydrometeor species in mixed-phase Arctic clouds. J. Geophys. Res., 116, D00T02, doi:10.1029/2010JD015570.

    • Crossref
    • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • May, P. T., J. H. Mather, and G. Vaughan, 2008: The Tropical Warm Pool International Cloud Experiment. Bull. Amer. Meteor. Soc., 89, 629645, doi:10.1175/BAMS-89-5-629.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morrison, H., J. A. Curry, and V. I. Khvorostyanov, 2005: A new double-moment microphysics parameterization for application in cloud and climate models. Part I: Description. J. Atmos. Sci., 62, 16651677, doi:10.1175/JAS3446.1.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Petersen, W., and M. Jensen, 2012: The NASA-GPM and DOE-ARM Midlatitude Continental Convective Clouds Experiment (MC3E). The Earth Observer, Vol. 24, No. 1, Earth Observing System Project Science Office, Greenbelt, MD, 12–18.

  • 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.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Storer, R. L., B. M. Griffin, J. Höft, J. K. Weber, E. Raut, V. E. Larson, M. Wang, and P. J. Rasch, 2015: Parameterizing deep convection using the assumed probability density function method. Geosci. Model Dev., 8, 119, doi:10.5194/gmd-8-1-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thayer-Calder, K., and et al. , 2015: A unified parameterization of clouds and turbulence using CLUBB and subcolumns in the Community Atmosphere Model. Geosci. Model Dev., 8, 38013821, doi:10.5194/gmd-8-3801-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tompkins, A. M., 2002: A prognostic parameterization for the subgrid-scale variability of water vapor and clouds in large-scale models and its use to diagnose cloud cover. J. Atmos. Sci., 59, 19171942, doi:10.1175/1520-0469(2002)059<1917:APPFTS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wong, M., M. Ovchinnikov, and M. Wang, 2015: Evaluation of subgrid-scale hydrometeor transport schemes using a high-resolution cloud-resolving model. J. Atmos. Sci., 72, 37153731, doi:10.1175/JAS-D-15-0060.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xie, S., T. Hume, C. Jakob, S. A. Klein, R. B. McCoy, and M. Zhang, 2010: Observed large-scale structures and diabatic heating and drying profiles during TWP-ICE. J. Climate, 23, 5779, doi:10.1175/2009JCLI3071.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xu, K.-M., and et al. , 2002: An intercomparison of cloud-resolving models with the Atmospheric Radiation Measurement summer 1997 intensive observation period data. Quart. J. Roy. Meteor. Soc., 128, 593624, doi:10.1256/003590002321042117.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, M. H., J. L. Lin, R. T. Cederwall, J. J. Yio, and S. C. Xie, 2001: Objective analysis of ARM IOP data: Method and sensitivity. Mon. Wea. Rev., 129, 295311, doi:10.1175/1520-0493(2001)129<0295:OAOAID>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 28 28 5
PDF Downloads 15 15 3

A PDF-Based Parameterization of Subgrid-Scale Hydrometeor Transport in Deep Convection

View More View Less
  • 1 National Center for Atmospheric Research, Boulder, Colorado
  • | 2 Pacific Northwest National Laboratory, Richland, Washington
© Get Permissions
Restricted access

Abstract

A parameterization scheme is proposed for the subgrid-scale transport of hydrometeors in an assumed probability density function (PDF) scheme. Joint distributions of vertical velocity and hydrometeor mixing ratios are typically unknown, but marginal (1D) PDFs of these variables are available. The parameterization is developed using high-resolution simulations of continental and tropical deep convection. A 3D cloud-resolving model (CRM) providing benchmark solutions has a horizontal grid spacing of 250 m and employs the Morrison microphysics scheme, which treats prognostically mass and number mixing ratios for four types of precipitating hydrometeors (rain, graupel, snow, and ice) as well as cloud droplet number mixing ratio. The subgrid-scale hydrometeor transport scheme assumes input given in the form of marginal PDFs of vertical velocity and hydrometeor mixing ratios; in this study, these marginal distributions are provided by the cloud-resolving model. Conditional sampling and scaling are then applied to the marginal distributions to account for subplume correlations. The parameterized fluxes tested for four episodes of deep convection show good agreement with benchmark fluxes computed directly from the CRM output. The results demonstrate the potential use of the subgrid-scale hydrometeor transport scheme in an assumed PDF scheme to parameterize the covariances of vertical velocity and hydrometeor mixing ratios.

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

© 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 e-mail: May Wong, mwong@ucar.edu; Mikhail Ovchinnikov, mikhail@pnnl.gov

Abstract

A parameterization scheme is proposed for the subgrid-scale transport of hydrometeors in an assumed probability density function (PDF) scheme. Joint distributions of vertical velocity and hydrometeor mixing ratios are typically unknown, but marginal (1D) PDFs of these variables are available. The parameterization is developed using high-resolution simulations of continental and tropical deep convection. A 3D cloud-resolving model (CRM) providing benchmark solutions has a horizontal grid spacing of 250 m and employs the Morrison microphysics scheme, which treats prognostically mass and number mixing ratios for four types of precipitating hydrometeors (rain, graupel, snow, and ice) as well as cloud droplet number mixing ratio. The subgrid-scale hydrometeor transport scheme assumes input given in the form of marginal PDFs of vertical velocity and hydrometeor mixing ratios; in this study, these marginal distributions are provided by the cloud-resolving model. Conditional sampling and scaling are then applied to the marginal distributions to account for subplume correlations. The parameterized fluxes tested for four episodes of deep convection show good agreement with benchmark fluxes computed directly from the CRM output. The results demonstrate the potential use of the subgrid-scale hydrometeor transport scheme in an assumed PDF scheme to parameterize the covariances of vertical velocity and hydrometeor mixing ratios.

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

© 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 e-mail: May Wong, mwong@ucar.edu; Mikhail Ovchinnikov, mikhail@pnnl.gov
Save