Evaluating cloud liquid detection using cloud radar Doppler spectra in a pre-trained artificial neural network against Cloudnet liquid detection

Detection of liquid-containing cloud layers in thick mixed-phase clouds or multi-layer cloud situations from ground-basedremote sensing instruments still pose observational challenges yet improvements are crucial since the existence of multi-layerliquid layers in mixed-phase cloud situations influences cloud radiative effects, cloud life time, and precipitation formationprocesses. Hydrometeor target classifications such as Cloudnet that require a lidar signal for the classification of liquid arelimited to the maximum height of lidar signal penetration and thus often lead to underestimations of liquid-containing cloudlayers. Here we evaluate the Cloudnet liquid detection against the approach of Luke et al. (2010) which extracts morphologicalfeatures in cloud-penetrating cloud radar Doppler spectra measurements in a artificial neural network (ANN) approach toclassify liquid beyond full lidar signal attenuation based on the simulation of the two lidar parameters particle backscattercoefficient and particle depolarization ratio. We show that the ANN of Luke et al. (2010) which was trained in Arctic conditionscan successfully be applied to observations in the mid-latitudes obtained during the seven-week long ACCEPT field experimentin Cabauw, the Netherlands, 2014. In a sensitivity study covering the whole duration of the ACCEPT campaign, different liquid-detectionthresholds 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 techniqueis realized by comparisons to observations of microwave radiometer liquid water path, ceilometer liquid-layer base altitude,and radiosonde relative humidity. Four conclusions were drawn from the investigation: First, it was found that the thresholdselection criteria of liquid-related lidar backscatter and depolarization alone control the liquid detection considerably. Second,nevertheless, all threshold values used in the ANN-framework were found to outperform the Cloudnet target classification fordeep or multi-layer cloud situations where the lidar signal is fully attenuated within low liquid layers and the cloud reflectivityin higher cloud layers was sufficiently high to be detectable by the cloud radar. Third, in convective situations for whichlidar data is available and for which the imprint of cloud microphysics on the radar Doppler spectrum is decreased, Cloudnetoutperforms the ANN retrieval. Fourth, in high-level clouds both approaches (Cloudnet and the ANN technique), are limited.

Noninvasive Suspicious Liquid Detection Using Wireless Signals

Conventional liquid detection instruments are very expensive and not conducive to large-scale deployment. In this work, we propose a method for detecting and identifying suspicious liquids based on the dielectric constant by utilizing the radio signals at a 5G frequency band. There are three major experiments: first, we use wireless channel information (WCI) to distinguish between suspicious and nonsuspicious liquids; then we identify the type of suspicious liquids; and finally, we distinguish the different concentrations of alcohol. The K-Nearest Neighbor (KNN) algorithm is used to classify the amplitude information extracted from the WCI matrix to detect and identify liquids, which is suitable for multimodal problems and easy to implement without training. The experimental result analysis showed that our method could detect more than 98% of the suspicious liquids, identify more than 97% of the suspicious liquid types, and distinguish up to 94% of the different concentrations of alcohol.