Observational Constraints on Warm Cloud Microphysical Processes Using Machine Learning and Optimization Techniques.

ABSTRACT: 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 is 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

SUBMITTER: Chiu JC 

PROVIDER: S-EPMC7900997 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature