Universität zu Köln

Acquistapace, Claudia ORCID: 0000-0002-1144-4753, Loehnert, Ulrich, Maahn, Maximilian ORCID: 0000-0002-2580-9100 and Kollias, Pavlos (2019). A New Criterion to Improve Operational Drizzle Detection with Ground-Based Remote Sensing. J. Atmos. Ocean. Technol., 36 (5). S. 781 - 802. BOSTON: AMER METEOROLOGICAL SOC. ISSN 1520-0426

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Abstract

Light shallow precipitation in the form of drizzle is one of the mechanisms for liquid water removal, affecting cloud lifetime and boundary layer dynamics and thermodynamics. The early formation of drizzle drops is of particular interest for quantifying aerosol-cloud-precipitation interactions. In models, drizzle initiation is represented by the autoconversion, that is, the conversion of liquid water from a cloud liquid water category (where particle sedimentation is ignored) into a precipitating liquid water category. Various autoconversion parameterizations have been proposed in recent years, but their evaluation is challenging due to the lack of proper observations of drizzle development in the cloud. This work presents a new algorithm for Classification of Drizzle Stages (CLADS). CLADS is based on the skewness of the Ka-band radar Doppler spectrum. Skewness is sensitive to the drizzle growth in the cloud: the observed Gaussian Doppler spectrum has skewness zero when only cloud droplets are present without any significant fall velocity. Defining downward velocities positive, skewness turns positive when embryonic drizzle forms and becomes negative when drizzle starts to dominate the spectrum. CLADS identifies spatially coherent structures of positive, zero, and negative skewness in space and time corresponding to drizzle seeding, drizzle growth/nondrizzle, and drizzle mature, respectively. We test CLADS on case studies from the Julich Observatory for Cloud Evolution Core Facility (JOYCE-CF) and the Barbados Cloud Observatory (BCO) to quantitatively estimate the benefits of CLADS compared to the standard Cloudnet target categorization algorithm. We suggest that CLADS can provide additional observational constraints for understanding the processes related to drizzle formation better.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Acquistapace, Claudia UNSPECIFIED orcid.org/0000-0002-1144-4753 UNSPECIFIED Loehnert, Ulrich UNSPECIFIED UNSPECIFIED UNSPECIFIED Maahn, Maximilian UNSPECIFIED orcid.org/0000-0002-2580-9100 UNSPECIFIED Kollias, Pavlos UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-150031
DOI:	10.1175/JTECH-D-18-0158.1
Journal or Publication Title:	J. Atmos. Ocean. Technol.
Volume:	36
Number:	5
Page Range:	S. 781 - 802
Date:	2019
Publisher:	AMER METEOROLOGICAL SOC
Place of Publication:	BOSTON
ISSN:	1520-0426
Language:	English
Faculty:	Faculty of Mathematics and Natural Sciences
Divisions:	Faculty of Mathematics and Natural Sciences > Department of Geosciences > Institute for Geophysics and Meteorology
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language DOPPLER RADAR; MICROPHYSICS PARAMETERIZATION; MICROWAVE RADIOMETER; CLOUD PROPERTIES; MODEL; RETRIEVAL; REFLECTIVITY; TURBULENCE; SIZE Multiple languages Engineering, Ocean; Meteorology & Atmospheric Sciences Multiple languages
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/15003