Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 15, issue 18
Articles | Volume 15, issue 18
https://doi.org/10.5194/amt-15-5343-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-15-5343-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 18
Research article
 | 
21 Sep 2022
Research article |  | 21 Sep 2022

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

Willi Schimmel, Heike Kalesse-Los, Maximilian Maahn, Teresa Vogl, Andreas Foth, Pablo Saavedra Garfias, and Patric Seifert

Data sets

Cloud profiling products: Classification, Categorize; cloud profiling measurements: Lidar, Microwave radiometer, Radar; ecmwf model data; 2018-11-27 to 2019-09-28 CLU https://hdl.handle.net/21.12132/2.d2bcddcd9355409e

Model code and software

remsens-lim/Voodoo: Voodoo (v1.0.0) Willi Schimmel https://doi.org/10.5281/zenodo.5970206

pyLARDA v3.2 (v3.2) Johannes Bühl, Martin Radenz, Willi Schimmel, Teresa Vogl, Johannes Röttenbacher, and Moritz Lochmann https://doi.org/10.5281/zenodo.4721311

CloudnetPy: A Python package for processing cloud remote sensing data (v1.2.4) Simo Tukiainen, Ewan O'Connor, and Anniina Korpinen https://doi.org/10.5281/zenodo.4011843

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Short summary
This study introduces the novel Doppler radar spectra-based machine learning approach VOODOO (reVealing supercOOled liquiD beyOnd lidar attenuatiOn). VOODOO is a powerful probability-based extension to the existing Cloudnet hydrometeor target classification, enabling the detection of liquid-bearing cloud layers beyond complete lidar attenuation via user-defined p* threshold. VOODOO performs best for (multi-layer) stratiform and deep mixed-phase clouds with liquid water path > 100 g m−2.
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