Cover Journal of the Atmospheric Sciences
Journal of the Atmospheric Sciences

  • Bechtold, P., E. Bazile, F. Guichard, P. Mascart, and E. Richard, 2001: A mass flux convection scheme for regional and global models. Quart. J. Roy. Meteor. Soc., 127 , 869–886.

    • Search Google Scholar
    • Export Citation

  • Bougeault, P., 1981: Modeling the trade-wind cumulus boundary layer. Part I: Testing the ensemble cloud relations against numerical data. J. Atmos. Sci., 38 , 2414–2428.

    • Search Google Scholar
    • Export Citation

  • Bougeault, P., and P. Lacarrère, 1989: Parameterization of orography-induced turbulence in a mesobeta-scale model. Mon. Wea. Rev., 117 , 1872–1890.

    • Search Google Scholar
    • Export Citation

  • Cederwall, R. T., S. K. Krueger, S. Xie, and J. Yio, 2000: ARM/GCSS Single Column Model (SCM) intercomparison, procedures for case 3: Summer 1997 SCM IOP. Lawrence Livermore National Laboratories Rep. UCRL-ID-141823, 20 pp.

    • Search Google Scholar
    • Export Citation

  • Chaboureau, J-P., J-P. Cammas, P. Mascart, J-P. Pinty, C. Claud, R. Roca, and J-J. Morcrette, 2000: Evaluation of a cloud system life-cycle simulated by Meso-NH during FASTEX using METEOSAT radiances and TOVS-3I cloud retrievals. Quart. J. Roy. Meteor. Soc., 126 , 1735–1750.

    • Search Google Scholar
    • Export Citation

  • Chaboureau, J-P., and J-P. Lafore, 2002: Mesoscale model cloud scheme assessment using satellite observations. J. Geophys. Res., in press.

    • Search Google Scholar
    • Export Citation

  • Cuijpers, J. W. M., and P. Bechtold, 1995: A simple parameterization of cloud water related variables for use in boundary layer models. J. Atmos. Sci., 52 , 2486–2490.

    • Search Google Scholar
    • Export Citation

  • Cusack, S., J. M. Edwards, and R. Kershaw, 1999: Estimating the subgrid variance of saturation, and its parameterization for use in a GCM cloud scheme. Quart. J. Roy. Meteor. Soc., 125 , 3057–3076.

    • Search Google Scholar
    • Export Citation

  • Cuxart, J., P. Bougeault, and J-L. Redelsperger, 2000: A turbulence scheme allowing for mesoscale and large-eddy simulations. Quart. J. Roy. Meteor. Soc., 126 , 1–30.

    • Search Google Scholar
    • Export Citation

  • Ebert, E. E., and J. A. Curry, 1992: A parameterization of ice cloud optical properties for climate models. J. Geophys. Res., 97 , 3831–3836.

    • Search Google Scholar
    • Export Citation

  • Emanuel, K. A., 1994: Atmospheric Convection. Oxford University Press, 592 pp.

  • Fouquart, Y., 1987: Radiative transfer in climate models. Physically-Based Modelling and Simulation of Climate and Climatic Changes, M. E. Schlesinger, Ed., NATO ASI Series, Kluwer Academic, 223–283.

    • Search Google Scholar
    • Export Citation

  • Fouquart, Y., and B. Bonnel, 1980: Computations of solar heating of the earth's atmosphere: A new parameterization. Beitr. Phys. Atmos., 53 , 35–62.

    • Search Google Scholar
    • Export Citation

  • Grabowski, W. W., X. Wu, and M. W. Moncrieff, 1996: Cloud-resolving modeling of tropical cloud systems during phase III of GATE. Part I: Two-dimensional experiments. J. Atmos. Sci., 53 , 3684–3709.

    • Search Google Scholar
    • Export Citation

  • Guichard, F., J-L. Redelsperger, and J-P. Lafore, 2000: Cloud-resolving simulation of convective activity during TOGA-COARE: Sensitivity to external sources of uncertainties. Quart. J. Roy. Meteor. Soc., 126 , 3067–3095.

    • Search Google Scholar
    • Export Citation

  • Holtslag, A. A. M., and F. T. M. Nieuwstadt, 1986: Scaling the atmospheric boundary-layer. Bound.-Layer Meteor., 36 , 201–209.

  • Kristjánsson, J. E., J. M. Edwards, and D. L. Mitchell, 1999: A new parameterization scheme for the optical properties of ice crystals for use in general circulation models of the atmosphere. Phys. Chem. Earth, 24B , 231–236.

    • Search Google Scholar
    • Export Citation

  • Lafore, J-P., and Coauthors. 1998: The Meso-NH Atmospheric Simulation System. Part I: Adiabatic formulation and control simulations. Ann. Geophys., 16 , 90–109.

    • Search Google Scholar
    • Export Citation

  • Lenderink, G., and P. Siebesma, 2000: Combining the mass flux approach with a statistical cloud scheme. Preprints, 14th Symp. on Boundary Layer and Turbulence, Aspen, CO, Amer. Meteor. Soc., 66–69.

    • Search Google Scholar
    • Export Citation

  • Lin, X., and R. H. Johnson, 1996: Kinematic and thermodynamical characteristics of the flow over the western Pacific warm pool during TOGA COARE. J. Atmos. Sci., 53 , 695–715.

    • Search Google Scholar
    • Export Citation

  • Mellor, G. L., 1977: The Gaussian cloud model relations. J. Atmos. Sci., 34 , 356–358.

  • Moncrieff, M., S. K. Krueger, D. Gregory, J. L. Redelsperger, and W-L. Tao, 1997: GEWEX Cloud System Study (GCSS) Working Group 4: Precipitating convective cloud systems. Bull. Amer. Meteor. Soc., 78 , 831–844.

    • Search Google Scholar
    • Export Citation

  • Morcrette, J-J., and Y. Fouquart, 1985: On systematic errors in parameterized calculations of longwave radiation transfer. Quart. J. Roy. Meteor. Soc., 111 , 691–708.

    • Search Google Scholar
    • Export Citation

  • Smith, R. N. B., 1990: A scheme for predicting layer clouds and their water content in a general circulation model. Quart. J. Roy. Meteor. Soc., 116 , 435–460.

    • Search Google Scholar
    • Export Citation

  • Sommeria, G., and J. W. Deardorff, 1977: Subgrid-scale condensation in models of nonprecipitating clouds. J. Atmos. Sci., 34 , 344–355.

    • Search Google Scholar
    • Export Citation

  • Stephens, G. L., 1978: Radiation profiles in extended water clouds. II: Parameterization schemes. J. Atmos. Sci., 35 , 2123–2132.

  • Tiedtke, M., 1993: Representation of clouds in large-scale models. Mon. Wea. Rev., 121 , 3040–3061.

  • Wu, X., W. D. Hall, W. W. Grabowski, M. W. Moncrieff, W. D. Collins, and J. T. Kiehl, 1999: Long-term behavior of cloud systems in TOGA COARE and their interactions with radiative and surface processes. Part II: effects of ice microphysics and cloud-radiation interaction. J. Atmos. Sci., 56 , 3177–3195.

    • Search Google Scholar
    • Export Citation

  • Xu, K-M., and S. K. Krueger, 1991: Evaluation of cloudiness parameterizations using a cumulus ensemble model. Mon. Wea. Rev., 119 , 342–367.

    • Search Google Scholar
    • Export Citation

  • Xu, K-M., and D. A. Randall, 1996: A semiempirical cloudiness parameterization for use in climate models. J. Atmos. Sci., 53 , 3084–3102.

    • Search Google Scholar
    • Export Citation

  • Zhang, C., and M-D. Chou, 1999: Variability of water vapor, infrared radiative cooling, and atmospheric instability for deep convection in the equatorial western Pacific. J. Atmos. Sci., 56 , 711–722.

    • 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 , 1503–1524.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 456 118 10
PDF Downloads 289 99 10
Editorial Type:
Article
Article Type:
Research Article

A Simple Cloud Parameterization Derived from Cloud Resolving Model Data: Diagnostic and Prognostic Applications

Jean-Pierre ChaboureauLaboratoire d'Aerologie, Observatoire Midi-Pyrenees, Toulouse, France

Search for other papers by Jean-Pierre Chaboureau in
Current site
Google Scholar
PubMedClose
and
Peter BechtoldLaboratoire d'Aerologie, Observatoire Midi-Pyrenees, Toulouse, France

Search for other papers by Peter Bechtold in
Current site
Google Scholar
PubMedClose
Print Publication:
01 Aug 2002
DOI:
https://doi.org/10.1175/1520-0469(2002)059<2362:ASCPDF>2.0.CO;2
Page(s):
2362–2372
Restricted access

Abstract

A simple statistical parameterization of cloud water–related variables that has been originally developed for nonprecipitating boundary layer clouds is extended for all cloud types including deep precipitating convection. Based on three-dimensional cloud resolving model (CRM) simulations of observed tropical maritime and continental midlatitude convective periods, expressions for the partial cloudiness and the cloud water content are derived, which are a function of the normalized saturation deficit Q1. It turns out that these relations are equivalent to boundary layer cloud relations described earlier, therefore allowing for a general description of subgrid-scale clouds.

The usefulness of the cloud relations is assessed by applying them diagnostically and prognostically in a mesoscale model for a midlatitude cyclone case and a subtropical case, and comparing the simulated cloud fields to satellite observations and to reference simulations with an explicit microphysical scheme. The comparison uses a model-to-satellite approach where synthetic radiances are computed from the meteorological fields and are compared to Meteosat satellite observations both in the visible and the thermal infrared spectral channels. The impact of the statistical cloud scheme is most pronounced for shallow and deep convective cloud fields (where Q1 < 0), provided that the host models convection parameterization is able to correctly represent the ensemble average water vapor profile in the troposphere. The scheme significantly reduces the biases in the infrared and especially shortwave spectral range with respect to the explicit microphysical scheme. Furthermore, it produces more realistic (smooth) horizontal and vertical condensate distributions in both diagnostic or prognostic applications showing the potential use of this simple parameterization in larger-scale models.

Corresponding author address: Dr. Jean-Pierre Chaboureau, Météo-France, CNRM/GMME/MOANA, 42 av. Coriolis, Toulouse 31057, France. Email: jean-pierre.chaboureau@cnrm.meteo.fr

Abstract

A simple statistical parameterization of cloud water–related variables that has been originally developed for nonprecipitating boundary layer clouds is extended for all cloud types including deep precipitating convection. Based on three-dimensional cloud resolving model (CRM) simulations of observed tropical maritime and continental midlatitude convective periods, expressions for the partial cloudiness and the cloud water content are derived, which are a function of the normalized saturation deficit Q1. It turns out that these relations are equivalent to boundary layer cloud relations described earlier, therefore allowing for a general description of subgrid-scale clouds.

The usefulness of the cloud relations is assessed by applying them diagnostically and prognostically in a mesoscale model for a midlatitude cyclone case and a subtropical case, and comparing the simulated cloud fields to satellite observations and to reference simulations with an explicit microphysical scheme. The comparison uses a model-to-satellite approach where synthetic radiances are computed from the meteorological fields and are compared to Meteosat satellite observations both in the visible and the thermal infrared spectral channels. The impact of the statistical cloud scheme is most pronounced for shallow and deep convective cloud fields (where Q1 < 0), provided that the host models convection parameterization is able to correctly represent the ensemble average water vapor profile in the troposphere. The scheme significantly reduces the biases in the infrared and especially shortwave spectral range with respect to the explicit microphysical scheme. Furthermore, it produces more realistic (smooth) horizontal and vertical condensate distributions in both diagnostic or prognostic applications showing the potential use of this simple parameterization in larger-scale models.

Corresponding author address: Dr. Jean-Pierre Chaboureau, Météo-France, CNRM/GMME/MOANA, 42 av. Coriolis, Toulouse 31057, France. Email: jean-pierre.chaboureau@cnrm.meteo.fr

Save