Universität zu Köln

Vogl, Teresa ORCID: 0000-0002-6696-4967, Maahn, Maximilian ORCID: 0000-0002-2580-9100, Kneifel, Stefan ORCID: 0000-0003-2220-2968, Schimmel, Willi ORCID: 0000-0001-8428-6445, Moisseev, Dmitri ORCID: 0000-0002-4575-0409 and Kalesse-Los, Heike ORCID: 0000-0001-6699-7040 (2022). Using artificial neural networks to predict riming from Doppler cloud radar observations. Atmos. Meas. Tech., 15 (2). S. 365 - 382. GOTTINGEN: COPERNICUS GESELLSCHAFT MBH. ISSN 1867-8548

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Abstract

Riming, i.e., the accretion and freezing of super-cooled liquid water (SLW) on ice particles in mixed-phase clouds, is an important pathway for precipitation formation. Detecting and quantifying riming using ground-based cloud radar observations is of great interest; however, approaches based on measurements of the mean Doppler velocity (MDV) are unfeasible in convective and orographically influenced cloud systems. Here, we show how artificial neural networks (ANNs) can be used to predict riming using ground-based, zenith-pointing cloud radar variables as input features. ANNs are a versatile means to extract relations from labeled data sets, which contain input features along with the expected target values. Training data are extracted from a data set acquired during winter 2014 in Finland, containing both Ka-and W-band cloud radar and in situ observations of snow-fall by a Precipitation Imaging Package from which the rime mass fraction (FRPIP) is retrieved. ANNs are trained separately either on the Ka-band radar or the W-band radar data set to predict the rime fraction FRANN. We focus on two configurations of input variables. ANN 1 uses the equivalent radar reflectivity factor (Ze), MDV, the width from left to right edge of the spectrum above the noise floor (spectrum edge width - SEW), and the skewness as input features. ANN 2 only uses Ze, SEW, and skewness. The application of these two ANN configurations to case studies from different data sets demonstrates that both are able to predict strong riming (FRANN > 0.7) and yield low values (FRANN <= 0.4) for unrimed snow. In general, the predictions of ANN 1 and 2 are very similar, advocating the capability of predicting riming without the use of MDV. The predictions of both ANNs for a wintertime convective cloud fit with coinciding in situ observations extremely well, suggesting the possibility to predict riming even within convective systems. Application of ANN 2 to an orographic case yields high FRANN values coinciding with observations of solid graupel particles at the ground.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Vogl, Teresa UNSPECIFIED orcid.org/0000-0002-6696-4967 UNSPECIFIED Maahn, Maximilian UNSPECIFIED orcid.org/0000-0002-2580-9100 UNSPECIFIED Kneifel, Stefan UNSPECIFIED orcid.org/0000-0003-2220-2968 UNSPECIFIED Schimmel, Willi UNSPECIFIED orcid.org/0000-0001-8428-6445 UNSPECIFIED Moisseev, Dmitri UNSPECIFIED orcid.org/0000-0002-4575-0409 UNSPECIFIED Kalesse-Los, Heike UNSPECIFIED orcid.org/0000-0001-6699-7040 UNSPECIFIED
URN:	urn:nbn:de:hbz:38-678671
DOI:	10.5194/amt-15-365-2022
Journal or Publication Title:	Atmos. Meas. Tech.
Volume:	15
Number:	2
Page Range:	S. 365 - 382
Date:	2022
Publisher:	COPERNICUS GESELLSCHAFT MBH
Place of Publication:	GOTTINGEN
ISSN:	1867-8548
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language ICE; FREQUENCY; SNOW; MICROPHYSICS; SPECTRA; PRECIPITATION; ATTENUATION; RADIOMETER; LIQUID; SYSTEM Multiple languages Meteorology & Atmospheric Sciences Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/67867