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

Chiu, J. C. ORCID: https://orcid.org/0000-0002-8951-6913, Yang, C. K. ORCID: https://orcid.org/0000-0001-7685-4481, Van Leeuwen, P. J. ORCID: https://orcid.org/0000-0003-2325-5340, Feingold, G. ORCID: https://orcid.org/0000-0002-0774-2926, Wood, R. ORCID: https://orcid.org/0000-0002-1401-3828, Blanchard, Y. ORCID: https://orcid.org/0000-0003-0515-3041, Mei, F. ORCID: https://orcid.org/0000-0003-4285-2749 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 doi: 10.1029/2020GL091236

Abstract/Summary

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
URI	https://reading-clone.eprints-hosting.org/id/eprint/94901
Identification Number/DOI	10.1029/2020GL091236
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	American Geophysical Union
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