Abstract
Abstract. Riming, i.e., the accretion and freezing of supercooled 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 snowfall 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.
Highlights
Mixed-phase clouds are important components of the climate system because they play a major role both for the radiation budget (Tan et al, 2016) and for the hydrological cycle (Mülmenstädt et al, 2015)
This means that attenuation caused by supercooled liquid water (SLW) droplets is only corrected for in the SLW layers detected by the CloudNet classification mask; i.e., it is limited by the lidar signal attenuation
One problem emerges because only a limited number of cases with high FR, i.e., values > 0.7, were observed during the period when Ka-band ARM zenith-pointing radar (KAZR), Marine W-Band ARM Cloud Radar (MWACR), and Precipitation Imaging Package (PIP) were collocated in Hyytiälä
Summary
Mixed-phase clouds are important components of the climate system because they play a major role both for the radiation budget (Tan et al, 2016) and for the hydrological cycle (Mülmenstädt et al, 2015). Only a few sites worldwide are equipped with cloud radars of two or more different frequencies, and correct alignment and volume matching is associated with a certain effort Another approach which does not rely on observations at multiple wavelengths is based on MDV only (Mosimann et al, 1994; Mosimann, 1995). These gravity waves can be temporally persistent and, not be removed by temporal averaging (Radenz et al, 2021), making MDV-based approaches such as the one by Kneifel and Moisseev (2020) unfeasible This is unfortunate, especially considering the fact that riming plays an important role in the microphysics of convective and orographic systems (Woods et al, 2005; Houze and Medina, 2005). These variables include the equivalent radar reflectivity factor (Ze), the MDV, the spectrum width from left to right edge of the spectrum above the noise floor (spectrum edge width – SEW), and the skewness
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