23 May 2022
23 May 2022
Status: this preprint is currently under review for the journal AMT.

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

Willi Schimmel1, Heike Kalesse-Los1, Maximilian Maahn1, Teresa Vogl1, Andreas Foth1, Pablo Saavedra Garfias1, and Patric Seifert2 Willi Schimmel et al.
  • 1Institute for Meteorology (LIM), Leipzig University, Leipzig, Germany
  • 2Leibniz Institute for Tropospheric Research (TROPOS), Leipzig, Germany

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 for cloud life time, 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 deep convolutional neural network (CNN)-based retrieval mapping radar Doppler spectra to the probability for 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 pixel 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) is 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) 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 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.

Willi Schimmel et al.

Status: open (until 28 Jul 2022)

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Willi Schimmel et al.

Willi Schimmel et al.


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Short summary
This study demonstrates that the VOODOO method could be a powerful addition to the existing Cloudnet target classification, making the detection of liquid layers beyond complete lidar attenuation possible. In conclusion, VOODOO performs best for (multi-layer) stratiform, deep mixed-phase cloud situations with liquid water path >100g m-2.