New insights on the prevalence of drizzle in marine stratocumulus clouds based on a machine learning algorithm applied to radar Doppler spectra
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Published:2022-06-09
Issue:11
Volume:22
Page:7405-7416
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ISSN:1680-7324
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Container-title:Atmospheric Chemistry and Physics
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language:en
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Short-container-title:Atmos. Chem. Phys.
Author:
Zhu Zeen, Kollias Pavlos, Luke Edward, Yang FanORCID
Abstract
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 paths lower than 50 g m−2. Furthermore, the
drizzle occurrence in marine boundary layer clouds differs among the 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.
Funder
U.S. Department of Energy
Publisher
Copernicus GmbH
Subject
Atmospheric Science
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