Articles | Volume 22, issue 11
Research article
 | Highlight paper
09 Jun 2022
Research article | Highlight paper |  | 09 Jun 2022

New insights on the prevalence of drizzle in marine stratocumulus clouds based on a machine learning algorithm applied to radar Doppler spectra

Zeen Zhu, Pavlos Kollias, Edward Luke, and Fan Yang


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2021-1102', Claudia Acquistapace, 02 Mar 2022
  • RC2: 'Comment on acp-2021-1102', Anonymous Referee #2, 08 Mar 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Zeen Zhu on behalf of the Authors (30 Apr 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (04 May 2022) by Barbara Ervens

The requested paper has a corresponding corrigendum published. Please read the corrigendum first before downloading the article.

Executive editor
Marine stratocumulus clouds are highly abundant, and they exert a significant cooling effect on the Earth's atmosphere. Improved understanding of their evolution and abundance has been targeted as critically important for constraining future climates. Droplets in these clouds, if sufficiently large and abundant, can collide to be converted into drizzle drops, acting as a sink for cloud moisture thereby limiting stratocumulus cloud lifetimes. Currently, the primary tool for ground-based detection of the presence of drizzle in stratocumulus is Doppler radar, although the longer wavelengths of the electromagnetic pulses generally limit drizzle detection to larger drops. The current study presents an innovative approach that extends radar detection to fine drizzle by using a machine-learning algorithm trained with aircraft in-situ measurements, identifying skewness in the Doppler radar signal as important for discriminating fine drizzle presence. Based on this method, the authors conclude that drizzle is far more common in marine stratocumulus clouds than previously thought, a result that represents a potentially major advance in our understanding of marine stratocumulus properties. Moreover, the study demonstrates the great potential of machine-learning for extending the capabilities of well-established atmospheric measurement techniques.
Short summary
Drizzle (small rain droplets) is an important component of warm clouds; however, its existence is poorly understood. In this study, we capitalized on a machine-learning algorithm to develop a drizzle detection method. We applied this algorithm to investigate drizzle occurrence and found out that drizzle is far more ubiquitous than previously thought. This study demonstrates the ubiquitous nature of drizzle in clouds and will improve understanding of the associated microphysical process.
Final-revised paper