Remember Me
Or use your Academic/Social account:


Or use your Academic/Social account:


You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.


Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Wacker Ulrike; Lüpkes Christof (2009)
Publisher: Co-Action Publishing
Journal: Tellus A
Types: Article

Classified by OpenAIRE into

arxiv: Physics::Atmospheric and Oceanic Physics
Common parametrization models for cloud microphysical processes use condensate mass density and/or particle number density as prognostic properties. However, other moments of the particle size distribution can likewise be chosen for prediction. This study deals with parametrization models with one and two, respectively, prognostic moments for the sedimentation of drop ensembles. The spectral resolving model defines the reference solution. The evolution of the vertical profiles of liquid water content, drop number density and rain rate strongly depend on the choice of the prognostic moments in the parametrization models. In models with a single prognostic moment, its vertical profile is copied by all other moments. The moment of most physical pertinence is recommended for prediction. In models with two prognostic moments, the vertical profiles of all moments differ. The orders of the prognostic moments should be chosen close to the order of moments of highest relevance. Otherwise large errors occur. For example, comparison of modelled versus observed radar reflectivity (6th moment with respect to diameter) does not tell much about the quality of other properties if reflectivity is diagnosed from for example, number density and mass density. Furthermore, mass conservation is fulfilled only if mass density is forecasted.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Collins, W. D., Rasch, P. J., Boville, B. A. and co-authors. 2004. Description of the NCAR Community Atmosphere Model (CAM 3.0). NCAR Technical Note TN-464+STR, http://www. ccsm.ucar.edu/models/atm-cam.
    • Doms, G., Fo¨rstner, J., Heise, E. and co-authors 2003. A Description of the Nonhydrostatic Regional Model LM. Consortium for Small Scale Modelling (COSMO), http://cosmomodel.cscs.ch/content/model/documentation/core/default.htm.
    • Geleyn, J.-F., Catry, B., Bouteloup, Y. and Brozkova, R. 2008. A statistical approach for sedimentation inside a microphysical precipitation scheme. Tellus 60A, 649-662.
    • Ferrier, B. S. 1994. A double-moment multiple-phase four-class bulk ice scheme: Part I: description. J. Atmos. Sci. 51, 249-280.
    • Kessler, E. 1969. On the Distribution and Continuity of Water Substance in Atmospheric Circulations. Amer. Meteor. Soc., Boston.
    • Khain, A., Pokrovsky, A., Pinsky, M., Seifert, A. and Phillips, V. 2004. Simulations of effects of atmospheric aerosols on deep turbulent convective clouds by using a spectral microphysics mixed-phase cumulus cloud model. Part I: model description and possible applications. J. Atmos. Sci. 61, 2963-2982.
    • Lu¨pkes, C., Beheng, K. B. and Doms, G. 1989. A parameterization scheme for simulating collision/coalescence of water drops. Beitr. Phys. Atmos. 62, 289-306.
    • Lynn, B. and Khain, A. 2007. Utilization of spectral bin microphysics and bulk parameterization schemes to simulate the cloud structure and precipitation in a mesoscale rain event. J. Geophys. Res. 112, D22205, DOI: 10.1029/2007JD008475.
    • Lynn, B., Khain, A., Dudhia, J. and co-authors 2005. Spectral (bin) microphysics coupled with a mesoscale model (MM5). Part I: model description and first results. Mon. Wea. Rev. 133, 44-58.
    • Milbrandt, J. A. and Yau, M. K. 2005a. A multimoment bulk microphysics parameterization. Part I: analysis of the role of the spectral shape parameter. J. Atmos. Sci. 62, 3051-3064.
    • Milbrandt, J.A. and Yau, M. K. 2005b. A multimoment bulk microphysics parameterization. Part II: a proposed three-moment closure and scheme description. J. Atmos. Sci. 62, 3065-3081.
    • Roeckner, E., Ba¨uml, G., Bonaventura, L. and co-authors 2003. The atmospheric general circulation model ECHAM5. Part. Max Planck Institute for Meteorology Report No. 349, http://www. mpimet.mpg.de/en/wissenschaft/modelle/echam/echam5.html#c2782.
    • Saito, K., Fujita, T., Yamada, Y. and co-authors 2006. The operational JMA nonhydrostatic mesoscale model. Mon. Wea. Rev. 134, 1266- 1298.
    • Seifert, A. and Beheng, K. D. 2005. A two-moment cloud microphysics parameterization for mixed-phase clouds. Part I: model description. Meteorol. Atmos. Phys. 92, 45-66.
    • Tokay, A., Kruger, A. and Krajewski, W. F. 2001. Comparison of drop size distribution measurements by impact and optical disdrometers. J. Appl. Meteor. 40, 2083-2097.
    • Toro, E. F. 1999. Riemann Solvers and Numerical Methods for Fluid Dynamics. Springer, Berlin.
    • Wacker, U. and Seifert, A. 2001. Evolution of rain water profiles resulting from pure sedimentation: spectral vs. parameterized description. Atmos. Res. 58, 19-39.
    • Waldvogel, A. 1974. The N 0 jump of raindrop spectra. J. Atmos. Sci. 31, 1067-1078.
    • Willis, P. 1984. Functional fits to some observed drop size distributions and parameterization of rain. J.Atmos. Sci. 41, 1648-1661.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article

Collected from