Abstract
Abstract. Detection of liquid-containing cloud layers in thick mixed-phase clouds or multi-layer cloud situations from ground-based remote-sensing instruments still poses observational challenges, yet improvements are crucial since the existence of multi-layer liquid layers in mixed-phase cloud situations influences cloud radiative effects, cloud lifetime, and precipitation formation processes. Hydrometeor target classifications such as from Cloudnet that require a lidar signal for the classification of liquid are limited to the maximum height of lidar signal penetration and thus often lead to underestimations of liquid-containing cloud layers. Here we evaluate the Cloudnet liquid detection against the approach of Luke et al. (2010) which extracts morphological features in cloud-penetrating cloud radar Doppler spectra measurements in an artificial neural network (ANN) approach to classify liquid beyond full lidar signal attenuation based on the simulation of the two lidar parameters particle backscatter coefficient and particle depolarization ratio. We show that the ANN of Luke et al. (2010) which was trained under Arctic conditions can successfully be applied to observations at the mid-latitudes obtained during the 7-week-long ACCEPT field experiment in Cabauw, the Netherlands, in 2014. In a sensitivity study covering the whole duration of the ACCEPT campaign, different liquid-detection thresholds for ANN-predicted lidar variables are applied and evaluated against the Cloudnet target classification. Independent validation of the liquid mask from the standard Cloudnet target classification against the ANN-based technique is realized by comparisons to observations of microwave radiometer liquid-water path, ceilometer liquid-layer base altitude, and radiosonde relative humidity. In addition, a case-study comparison against the cloud feature mask detected by the space-borne lidar aboard the CALIPSO satellite is presented. Three conclusions were drawn from the investigation. First, it was found that the threshold selection criteria of liquid-related lidar backscatter and depolarization alone control the liquid detection considerably. Second, all threshold values used in the ANN framework were found to outperform the Cloudnet target classification for deep or multi-layer cloud situations where the lidar signal is fully attenuated within low liquid layers and the cloud radar is able to detect the microphysical fingerprint of liquid in higher cloud layers. Third, if lidar data are available, Cloudnet is at least as good as the ANN. The times when Cloudnet outperforms the ANN in liquid detections are often associated with situations where cloud dynamics smear the imprint of cloud microphysics on the radar Doppler spectra.
Highlights
In mixed-phase clouds the variable mass ratio between liquid water and ice as well as its spatial distribution within the cloud play an important role in cloud lifetime, precipitation processes, and the radiation budget (Sun and Shine, 1994; Yong-Sang et al, 2014; Morrison et al, 2012)
It should be noted that no further distinction between other liquid-bearing samples such as drizzle/rain is made for the artificial neural network (ANN)-based liquid predictions
The current study shows that synergistic observations of depolarization lidar and cloud Doppler radar in conjunction with machine learning techniques can be used to detect liquid beyond full lidar signal attenuation
Summary
In mixed-phase clouds the variable mass ratio between liquid water and ice as well as its spatial distribution within the cloud play an important role in cloud lifetime, precipitation processes, and the radiation budget (Sun and Shine, 1994; Yong-Sang et al, 2014; Morrison et al, 2012). Kalesse-Los et al.: Evaluating cloud liquid detection against Cloudnet dar, and microwave radiometer (MWR) comprised within the Cloudnet project with global climate models (GCMs) They showed that many GCMs underestimate the presence of midlevel clouds (As, Ac) by at least 30 % and that there is a large spread in the stated frequency of occurrence of liquid water in the models. Tan et al (2016) argued that a realistic representation of the SLF in the GCM is needed to better constrain the equilibrium climate sensitivity They stated that this can only be reached by more accurate observations of the distribution of supercooled liquid in mixed-phase clouds. We argue that it is important to improve the detection of multilayer liquid-layer occurrences
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