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Original Research Papers

On the selection of prognostic moments in parametrization schemes for drop sedimentation

Authors:

Ulrike Wacker ,

Alfred–Wegener–Institut für Polar– und Meeresforschung, 27515 Bremerhaven, DE
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Christof Lüpkes

Alfred–Wegener–Institut für Polar– und Meeresforschung, 27515 Bremerhaven, DE
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Abstract

Common parametrizationmodels 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 parametrizationmodels. Inmodels 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.

How to Cite: Wacker, U. and Lüpkes, C., 2009. On the selection of prognostic moments in parametrization schemes for drop sedimentation. Tellus A: Dynamic Meteorology and Oceanography, 61(4), pp.498–511. DOI: http://doi.org/10.1111/j.1600-0870.2009.00405.x
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  Published on 01 Jan 2009
 Accepted on 24 Apr 2009            Submitted on 24 Oct 2008

References

  1. 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 .  

  2. Doms , G. , Förstner , J. , Heise , E. and co-authors 2003 . A De-scription of the Nonhydrostatic Regional Model IM. Con-sortium for Small Scale Modelling (COSMO), http://cosmo-model.cscs.ch/content/model/documentation/core/default.htm.  

  3. Geleyn , J.-E , Catry , B. , Bouteloup , Y. and Brozkova , R. 2008 . A statis-tical approach for sedimentation inside a microphysical precipitation scheme . Tellus 60A , 649 – 662 .  

  4. Ferrier , B. S. 1994 . A double-moment multiple-phase four-class bulk ice scheme: Part I: description. J. Atmos. Sc i . 51 , 249 – 280 .  

  5. Kessler , E. 1969 . On the Distribution and Continuity of Water Substance in Atmospheric Circulations . Amer. Meteor. Soc ., Boston .  

  6. Khain , A. , Pokrovsky , A. , Pinsky , M. , Seifert , A. and Phillips , V. 2004 . Simulations of effects of atmospheric aerosols on deep turbu-lent convective clouds by using a spectral microphysics mixed-phase cumulus cloud model. Part I: model description and possible applica-tions . J. Atmos. Sci . 61 , 2963 – 2982 .  

  7. Lüpkes , C. , Beheng , K. B. and Doms , G. 1989 . A parameterization scheme for simulating collision/coalescence of water drops . Beitr Phys. Atmos . 62 , 289 – 306 .  

  8. 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 , https://doi.org/10.1029/2007JD008475 .  

  9. 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 .  

  10. Milbrandt , J. A. and Yau , M. K. 2005a . A multimoment bulk micro-physics parameterization. Part I: analysis of the role of the spectral shape parameter . J. Atmos. Sci . 62 , 3051 – 3064 .  

  11. Milbrandt , J.A. and Yau , M. K. 2005b . A multimoment bulk micro-physics parameterization. Part II: a proposed three-moment closure and scheme description . J. Atmos. Sci . 62 , 3065 – 3081 .  

  12. Roeckner , E. , Bäuml , 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.  

  13. Saito , K. , Fujita , T. , Yamada , Y. and co-authors 2006 . The operational JMA nonhydrostatic mesoscale model. Mon. Wea. Rev . 134 , 1266 - 1298 .  

  14. 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 .  

  15. Tokay , A. , Kruger , A. and Krajewski , W. E 2001 . Comparison of drop size distribution measurements by impact and optical disdrometers . J. AppL Meteor . 40 , 2083 – 2097 .  

  16. Toro , E. F. 1999 . Riemann Solvers and Numerical Methods for Fluid Dynamics . Springer , Berlin .  

  17. Wacker , U. and Seifert , A. 2001 . Evolution of rain water profiles result-ing from pure sedimentation: spectral vs. parameterized description . Atmos. Res . 58 , 19 – 39 .  

  18. Waldvogel , A. 1974 . The No jump of raindrop spectra . J. Atmos. Sci . 31 , 1067 – 1078 .  

  19. Willis , P. 1984 . Functional fits to some observed drop size distributions and parameterization of rain . J.Atmos. Sci . 41 , 1648 – 1661 .  

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