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.
Zhu, Zeen, et al. "New insights on the prevalence of drizzle in marine stratocumulus clouds based on a machine learning algorithm applied to radar Doppler spectra." Atmospheric Chemistry and Physics (Online), vol. 22, no. 11, Jun. 2022. https://doi.org/10.5194/acp-22-7405-2022
Zhu, Zeen, Kollias, Pavlos, Luke, Edward, & Yang, Fan (2022). New insights on the prevalence of drizzle in marine stratocumulus clouds based on a machine learning algorithm applied to radar Doppler spectra. Atmospheric Chemistry and Physics (Online), 22(11). https://doi.org/10.5194/acp-22-7405-2022
Zhu, Zeen, Kollias, Pavlos, Luke, Edward, et al., "New insights on the prevalence of drizzle in marine stratocumulus clouds based on a machine learning algorithm applied to radar Doppler spectra," Atmospheric Chemistry and Physics (Online) 22, no. 11 (2022), https://doi.org/10.5194/acp-22-7405-2022
@article{osti_1876669,
author = {Zhu, Zeen and Kollias, Pavlos and Luke, Edward and Yang, Fan},
title = {New insights on the prevalence of drizzle in marine stratocumulus clouds based on a machine learning algorithm applied to radar Doppler spectra},
annote = {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.},
doi = {10.5194/acp-22-7405-2022},
url = {https://www.osti.gov/biblio/1876669},
journal = {Atmospheric Chemistry and Physics (Online)},
issn = {ISSN 1680-7324},
number = {11},
volume = {22},
place = {Germany},
publisher = {Copernicus GmbH},
year = {2022},
month = {06}}
Brookhaven National Laboratory (BNL), Upton, NY (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Atmospheric Radiation Measurement (ARM) Data Center
Sponsoring Organization:
USDOE; USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); USDOE Office of Science (SC), Biological and Environmental Research (BER)
Grant/Contract Number:
SC0012704
OSTI ID:
1876669
Alternate ID(s):
OSTI ID: 1874216 OSTI ID: 1877063
Report Number(s):
BNL-223156-2022-JAAM
Journal Information:
Atmospheric Chemistry and Physics (Online), Journal Name: Atmospheric Chemistry and Physics (Online) Journal Issue: 11 Vol. 22; ISSN 1680-7324