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Observational constraints on warm cloud microphysical processes using machine learning and optimization techniques

Chiu, J. C. ORCID:, Yang, C. K. ORCID:, Van Leeuwen, P. J. ORCID:, Feingold, G. ORCID:, Wood, R. ORCID:, Blanchard, Y. ORCID:, Mei, F. ORCID: and Wang, J. (2021) Observational constraints on warm cloud microphysical processes using machine learning and optimization techniques. Geophysical Research Letters, 48 (2). e2020GL091236. ISSN 0094-8276

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To link to this item DOI: 10.1029/2020GL091236


We introduce new parameterizations for autoconversion and accretion rates that greatly improve representation of the growth processes of warm rain. The new parameterizations capitalize on machine-learning and optimization techniques and are constrained by in-situ cloud probe measurements from the recent Atmospheric Radiation Measurement Program field campaign at Azores. The uncertainty in the new estimates of autoconversion and accretion rates are about 15% and 5%, respectively, outperforming existing parameterizations. Our results confirm that cloud and drizzle water content are the most important factors for determining accretion rates. However, for autoconversion, in addition to cloud water content and droplet number concentration, we discovered a key role of drizzle number concentration that is missing in current parameterizations. The robust relation between autoconversion rate and drizzle number concentration is surprising but real, and furthermore supported by theory. Thus, drizzle number concentration should be considered in parameterizations for improved representation of the autoconversion process.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:94901
Publisher:American Geophysical Union


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