Observational Constraints on Warm Cloud Microphysical Processes Using Machine Learning and Optimization Techniques. Issue 2 (23rd January 2021)

• Record Type: Journal Article
• Authors: Chiu, J. Christine; Yang, C. Kevin; van Leeuwen, Peter Jan; Feingold, Graham; Wood, Robert; Blanchard, Yann; Mei, Fan; Wang, Jian
• Is Part Of: Geophysical research letters. Volume 48:Issue 2(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-01-23
• Subjects: accretion -- autoconversion -- boundary layer cloud -- cloud parameterization -- machine learning -- warm rain; Geophysics -- Periodicals; Planets -- Periodicals; Lunar geology -- Periodicals; 550
• DOI: 10.1029/2020GL091236
• Languages: English
• ISSNs: 0094-8276

Abstract

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 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. Plain Language Summary: Drizzle has been a key element of research, because its formation modulates cloud properties and evolution, and affects the water cycle of the Earth. Since drizzle formation involves cloud droplets of all sizes, it requires extensive computational time. Hence, we often use simplified methods in weather and climate prediction models to obtain a bulk estimate of how fast and how many cloud droplets collide with each other or collide with bigger drops to form drizzle. However, many models continue to have inadequate representation of drizzle formation, calling for the need to improve these simplified methods. We introduce new methods to estimate the rate of those microphysical processes, capitalizing on aircraft measurements and recent advances in machine‐learning techniques. Our techniques outperform the current methods significantly. Importantly, our analyses reveal that the rate of drizzle formation via collisions between cloud drops is related to drizzle drop number concentration itself, which is missing in the existing methods. This relation occurs because drizzle drop number concentration provides information on the stage of evolution of cloud size distribution during drizzle formation. Although this is not a causal relationship, it is important to incorporate this relation into models for better prediction of drizzle formation. Key Points: Machine‐learning trained by in situ data constrains autoconversion and accretion rates with uncertainty of 15% and 5%, respectively There is a surprising relation between autoconversion rate and drizzle number concentration that significantly improves parameterizations The exponent of autoconversion rate dependence on cloud number concentration is 0.75, lower than that in existing parameterizations