Preprints
https://doi.org/10.5194/acp-2021-1102
https://doi.org/10.5194/acp-2021-1102
 
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07 Feb 2022
07 Feb 2022
Status: a revised version of this preprint was accepted for the journal ACP and is expected to appear here in due course.

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

Zeen Zhu1, Pavlos Kollias1,2, Edward Luke2, and Fan Yang2 Zeen Zhu et al.
  • 1School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY, USA
  • 2Environmental and Climate Sciences Dept, Brookhaven National Laboratory Upton, NY, USA

Abstract. The detection of the early growth of drizzle particles in marine stratocumulus clouds is important for studying the transition from cloud water to rainwater. Radar reflectivity is commonly used to detect drizzle; however, its utility is limited to larger drizzle particles. Alternatively, radar Doppler spectrum skewness has proven to be a more sensitive quantity for the detection of drizzle embryos. Here, a machine-learning (ML) based technique that uses radar reflectivity and skewness for detecting small drizzle particles is presented. Aircraft in-situ measurements are used to develop and validate the ML algorithm. The drizzle detection algorithm is applied to three Atmospheric Radiation Measurement (ARM) observational campaigns to investigate the drizzle occurrence in marine boundary layer clouds. It is found that drizzle is far more ubiquitous than previously thought, the traditional radar reflectivity-based approach significantly underestimates the drizzle occurrence, especially in thin clouds with liquid water path lower than 50 gm−2. Furthermore, the drizzle occurrence in marine boundary layer clouds differs among three ARM campaigns, indicating that the drizzle formation which is controlled by the microphysical process is regime-dependent. A complete understanding of the drizzle distribution climatology in marine stratocumulus clouds calls for more observational campaigns and continuing investigations.

Zeen Zhu et al.

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

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

Zeen Zhu et al.

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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 droplet) is an important component of the 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 benefit the understanding of the associated microphysical process.
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