A cloud microphysics parameterization for shallow cumulus clouds based on Lagrangian cloud model simulations

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Noh, Y.; Oh, D.; Hoffmann, F.; Raasch, S.: A cloud microphysics parameterization for shallow cumulus clouds based on Lagrangian cloud model simulations. In: Journal of the Atmospheric Sciences 75 (2018), Nr. 7, S. 4031-4047. DOI: https://doi.org/10.1175/JAS-D-18-0080.1

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To cite the version in the repository, please use this identifier: https://doi.org/10.15488/5499

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Sum total of downloads: 135




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Abstract: 
Cloud microphysics parameterizations for shallow cumulus clouds are analyzed based on Lagrangian cloud model (LCM) data, focusing on autoconversion and accretion. The autoconversion and accretion rates,A and C, respectively, are calculated directly by capturing the moment of the conversion of individual Lagrangian droplets from cloud droplets to raindrops, and it results in the reproduction of the formulas of A and C for the first time. Comparison with various parameterizations reveals the closest agreement with Tripoli and Cotton, such as A=αNc -1/3 qc 7/3 H(R2RT) and C=βqcqr, where qc and Nc are the mixing ratio and the number concentration of cloud droplets, qr is the mixing ratio of raindrops, RT is the threshold volume radius, and His the Heaviside function. Furthermore, it is found that a increases linearly with the dissipation rate « and the standard deviation of radius s and that RT decreases rapidly with σ while disappearing at σ > 3.5 μm. The LCMalso reveals that σ and ε increase with time during the period of autoconversion, which helps to suppress the early precipitation by reducing A with smaller a and larger RT in the initial stage. Finally, β is found to be affected by the accumulated collisional growth, which determines the drop size distribution. © 2018 American Meteorological Society.
License of this version: Es gilt deutsches Urheberrecht. Das Dokument darf zum eigenen Gebrauch kostenfrei genutzt, aber nicht im Internet bereitgestellt oder an Außenstehende weitergegeben werden.
Document Type: Article
Publishing status: publishedVersion
Issue Date: 2018
Appears in Collections:Fakultät für Mathematik und Physik

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pos. country downloads
total perc.
1 image of flag of Germany Germany 64 47.41%
2 image of flag of United States United States 33 24.44%
3 image of flag of China China 8 5.93%
4 image of flag of Netherlands Netherlands 5 3.70%
5 image of flag of Czech Republic Czech Republic 5 3.70%
6 image of flag of United Kingdom United Kingdom 4 2.96%
7 image of flag of No geo information available No geo information available 2 1.48%
8 image of flag of India India 2 1.48%
9 image of flag of Canada Canada 2 1.48%
10 image of flag of United Arab Emirates United Arab Emirates 1 0.74%
    other countries 9 6.67%

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