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| Titel |
A refined statistical cloud closure using double-Gaussian probability density functions |
| VerfasserIn |
A. K. Naumann, A. Seifert, J. P. Mellado |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1991-959X
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| Digitales Dokument |
URL |
| Erschienen |
In: Geoscientific Model Development ; 6, no. 5 ; Nr. 6, no. 5 (2013-10-08), S.1641-1657 |
| Datensatznummer |
250084998
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| Publikation (Nr.) |
 copernicus.org/gmd-6-1641-2013.pdf |
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| Zusammenfassung |
| We introduce a probability density function (PDF)-based scheme to
parameterize cloud fraction, average liquid water and liquid water flux in
large-scale models, that is developed from and tested against large-eddy
simulations and observational data. Because the tails of the PDFs are crucial
for an appropriate parameterization of cloud properties, we use
a double-Gaussian distribution that is able to represent the observed, skewed
PDFs properly. Introducing two closure equations, the resulting
parameterization relies on the first three moments of the subgrid variability
of temperature and moisture as input parameters. The parameterization is
found to be superior to a single-Gaussian approach in diagnosing the
cloud fraction and average liquid water profiles. A priori testing also suggests
improved accuracy compared to existing
double-Gaussian closures. Furthermore, we find that the error of the new parameterization
is smallest for a horizontal resolution of about 5–20 km and also
depends on the appearance of mesoscale structures that are accompanied by
higher rain rates. In combination with simple autoconversion schemes that
only depend on the liquid water, the error introduced by the new
parameterization is orders of magnitude smaller than the difference between
various autoconversion schemes. For the liquid water flux, we introduce
a parameterization that is depending on the skewness of the subgrid
variability of temperature and moisture and that reproduces the profiles of
the liquid water flux well. |
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