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| Titel |
Fluctuations in a quasi-stationary shallow cumulus cloud ensemble |
| VerfasserIn |
M. Sakradzija, A. Seifert, T. Heus |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 22, no. 1 ; Nr. 22, no. 1 (2015-01-28), S.65-85 |
| Datensatznummer |
250120964
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| Publikation (Nr.) |
 copernicus.org/npg-22-65-2015.pdf |
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| Zusammenfassung |
We propose an approach to stochastic parameterisation of shallow cumulus clouds to represent the
convective variability and its dependence on the model resolution. To collect information
about the individual cloud lifecycles and the cloud ensemble as a whole, we employ a large eddy
simulation (LES) model and a cloud tracking algorithm, followed by conditional sampling of clouds
at the cloud-base level. In the case of a shallow cumulus ensemble, the cloud-base mass flux
distribution is bimodal, due to the different shallow cloud subtypes, active and passive clouds.
Each distribution mode can be approximated using a Weibull distribution, which is a generalisation of
exponential distribution by accounting for the change in distribution shape due to the diversity of cloud lifecycles.
The exponential distribution of
cloud mass flux previously suggested for deep convection parameterisation is a special case of the
Weibull distribution, which opens a way towards unification of the statistical convective ensemble
formalism of shallow and deep cumulus clouds.
Based on the empirical and theoretical findings, a stochastic model has been developed to simulate
a shallow convective cloud ensemble. It is formulated as a compound random process, with the
number of convective elements drawn from a Poisson distribution, and the cloud mass flux sampled
from a mixed Weibull distribution. Convective memory is accounted for through the explicit cloud
lifecycles, making the model formulation consistent with the choice of the Weibull cloud mass flux
distribution function. The memory of individual shallow clouds is required to capture the correct
convective variability. The resulting distribution of the subgrid convective states in the
considered shallow cumulus case is scale-adaptive – the smaller the grid size, the broader the
distribution. |
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