Identifying cloud droplets beyond lidar attenuation from vertically pointing cloud radar observations using artificial neural networks

Abstract

Abstract. In mixed-phase clouds, the variable mass ratio between liquid water and ice as well as the spatial distribution within the cloud plays an important role in cloud lifetime, precipitation processes, and the radiation budget. Data sets of vertically pointing Doppler cloud radars and lidars provide insights into cloud properties at high temporal and spatial resolution. Cloud radars are able to penetrate multiple liquid layers and can potentially be used to expand the identification of cloud phase to the entire vertical column beyond the lidar signal attenuation height, by exploiting morphological features in cloud radar Doppler spectra that relate to the existence of supercooled liquid. We present VOODOO (reVealing supercOOled liquiD beyOnd lidar attenuatiOn), a retrieval based on deep convolutional neural networks (CNNs) mapping radar Doppler spectra to the probability of the presence of cloud droplets (CD). The training of the CNN was realized using the Cloudnet processing suite as supervisor. Once trained, VOODOO yields the probability for CD directly at Cloudnet grid resolution. Long-term predictions of 18 months in total from two mid-latitudinal locations, i.e., Punta Arenas, Chile (53.1∘ S, 70.9∘ W), in the Southern Hemisphere and Leipzig, Germany (51.3∘ N, 12.4∘ E), in the Northern Hemisphere, are evaluated. Temporal and spatial agreement in cloud-droplet-bearing pixels is found for the Cloudnet classification to the VOODOO prediction. Two suitable case studies were selected, where stratiform, multi-layer, and deep mixed-phase clouds were observed. Performance analysis of VOODOO via classification-evaluating metrics reveals precision > 0.7, recall ≈ 0.7, and accuracy ≈ 0.8. Additionally, independent measurements of liquid water path (LWP) retrieved by a collocated microwave radiometer (MWR) are correlated to the adiabatic LWP, which is estimated using the temporal and spatial locations of cloud droplets from VOODOO and Cloudnet in connection with a cloud parcel model. This comparison resulted in stronger correlation for VOODOO (≈ 0.45) compared to Cloudnet (≈ 0.22) and indicates the availability of VOODOO to identify CD beyond lidar attenuation. Furthermore, the long-term statistics for 18 months of observations are presented, analyzing the performance as a function of MWR–LWP and confirming VOODOO's ability to identify cloud droplets reliably for clouds with LWP > 100 g m−2. The influence of turbulence on the predictive performance of VOODOO was also analyzed and found to be minor. A synergy of the novel approach VOODOO and Cloudnet would complement each other perfectly and is planned to be incorporated into the Cloudnet algorithm chain in the near future.

Improved cloud phase retrievals based on remote-sensing observations have the potential to decrease the Southern Ocean shortwave cloud radiation bias

“…The focus of this analysis is to quantify the effect of the improved thermodynamic phase classification by VOODOO (Schimmel et al, 2022) compared to the reference retrieval Cloudnet (Illingworth et al, 2007;Tukiainen et al, 2020). In this study, the focus is on the period between 15:00-22:00 UTC on 2 January 2019 in Punta Arenas Chile, when multilayer MPC were observed.…”

“…In this work, we use the latest implementation of CloudnetPy (Tukiainen et al, 2020). A detailed description of instruments and parameters is presented in Schimmel et al (2022). The latter data source (5.…”

“…For this reason, the liquid droplet detection of CloudnetPy beyond full lidar attenuation is not reliable, limiting the application to thin, single layer stratiform clouds. The new machine learning approach bySchimmel et al (2022) is used as add-on to CloudnetPy, for reVealing su-percOOled liquiD layers beyOnd lidar attenuatiOn (VOODOO). The VOODOO algorithm is based on a convolutional neural network.…”

The Virga-Sniffer – a new tool to identify precipitation evaporation using ground-based remote-sensing observations

“…The cloud radar was operated by the Leipzig Institute for Meteorology (LIM) of Leipzig University, and the instrument is named "LIMRAD94" in the following. While it had previously been used for long-term high-resolution cloudprofiling observations on land (Vogl et al, 2022;Schimmel et al, 2022), EUREC 4 A was the first ship deployment of LIMRAD94. To avoid sea spray accumulating on the cloud radar antenna radomes, the radar was placed on the navigation deck of the ship at 15.8 m above sea level, 4.1 m in the starboard direction and 11 m from the center of the ship towards the stern.…”

Determination of the vertical distribution of in-cloud particle shape using SLDR mode 35-GHz scanning cloud radar

“…This notation of the elevation angle range will be used throughout this article and on the figure captions. (Illingworth et al, 2007;Radenz et al, 2021) or as required for Doppler-spectra analysis techniques (Radenz et al, 2019;Bühl et al, 2019;Vogl et al, 2022;Schimmel et al, 2022). A limit of 150 • elevation angle was established to avoid physical barriers like trees or buildings.…”