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| Titel |
Development of a cloud microphysical model and parameterizations to describe the effect of CCN on warm cloud |
| VerfasserIn |
N. Kuba, Y. Fujiyoshi |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1680-7316
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Chemistry and Physics ; 6, no. 10 ; Nr. 6, no. 10 (2006-07-10), S.2793-2810 |
| Datensatznummer |
250003999
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| Publikation (Nr.) |
 copernicus.org/acp-6-2793-2006.pdf |
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| Zusammenfassung |
| First, a hybrid cloud microphysical model was developed that incorporates
both Lagrangian and Eulerian frameworks to study quantitatively the effect
of cloud condensation nuclei (CCN) on the precipitation of warm clouds. A
parcel model and a grid model comprise the cloud model. The condensation
growth of CCN in each parcel is estimated in a Lagrangian framework. Changes
in cloud droplet size distribution arising from condensation and coalescence
are calculated on grid points using a two-moment bin method in a
semi-Lagrangian framework. Sedimentation and advection are estimated in the
Eulerian framework between grid points. Results from the cloud model show
that an increase in the number of CCN affects both the amount and the area
of precipitation. Additionally, results from the hybrid microphysical model
and Kessler's parameterization were compared.
Second, new parameterizations were developed that estimate the number and
size distribution of cloud droplets given the updraft velocity and the
number of CCN. The parameterizations were derived from the results of
numerous numerical experiments that used the cloud microphysical parcel
model. The input information of CCN for these parameterizations is only
several values of CCN spectrum (they are given by CCN counter for example).
It is more convenient than conventional parameterizations those need values
concerned with CCN spectrum, C and k in the equation of N=CSk, or, breadth,
total number and median radius, for example. The new parameterizations'
predictions of initial cloud droplet size distribution for the bin method
were verified by using the aforesaid hybrid microphysical model. The newly
developed parameterizations will save computing time, and can effectively
approximate components of cloud microphysics in a non-hydrostatic cloud
model. The parameterizations are useful not only in the bin method in the
regional cloud-resolving model but also both for a two-moment bulk
microphysical model and for a global model. The effects of sea salt,
sulfate, and organic carbon particles were also studied with these
parameterizations and global model. |
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