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- W4214882191 abstract "<strong class=journal-contentHeaderColor>Abstract.</strong> 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 50âgâm<span class=inline-formula><sup>â2</sup></span>. 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." @default.
- W4214882191 created "2022-03-05" @default.
- W4214882191 creator A5058876319 @default.
- W4214882191 date "2022-03-02" @default.
- W4214882191 modified "2023-09-25" @default.
- W4214882191 title "Comment on acp-2021-1102" @default.
- W4214882191 doi "https://doi.org/10.5194/acp-2021-1102-rc1" @default.
- W4214882191 hasPublicationYear "2022" @default.
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