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

Viewed

Total article views: 2,714 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,991 647 76 2,714 89 60
  • HTML: 1,991
  • PDF: 647
  • XML: 76
  • Total: 2,714
  • BibTeX: 89
  • EndNote: 60
Views and downloads (calculated since 23 May 2022)
Cumulative views and downloads (calculated since 23 May 2022)

Viewed (geographical distribution)

Total article views: 2,714 (including HTML, PDF, and XML) Thereof 2,709 with geography defined and 5 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 01 Apr 2025
Download
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.
Read more
Share