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

Yign Noh, Donggun Oh, Fabian Hoffmanna, Siegfried Raasch

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)4031-4047
Number of pages17
JournalJournal of the Atmospheric Sciences
Volume75
Issue number11
DOIs
Publication statusPublished - 2018 Nov 1

Bibliographical note

Funding Information:
Acknowledgments. This work was funded by the Korea Meteorological Administration Research and Development Program under Grants KMI 2015-10410 and KMI 2018-07210. This LES/LCM used in this study (revision 1891) is publicly available (https://palm.muk.uni-hannover. de/trac/browser/palm?rev51891). For analysis, the model has been extended, and additional analysis tools have been developed. The code is available from the authors on request. Most of the simulations have been carried out on the Cray XC-30 systems of the North-German Supercomputing Alliance (HLRN) and the supercomputer system supported by the National Center for Meteorological Supercomputer of Korea Meteorological Administration (KMA).

Publisher Copyright:
© 2018 American Meteorological Society.

All Science Journal Classification (ASJC) codes

  • Atmospheric Science

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