Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 15, issue 2
Articles | Volume 15, issue 2
https://doi.org/10.5194/amt-15-279-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-15-279-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 2
Research article
 | 
20 Jan 2022
Research article |  | 20 Jan 2022

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

Heike Kalesse-Los, Willi Schimmel, Edward Luke, and Patric Seifert

Viewed

Total article views: 2,546 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,881 585 80 2,546 78 82
  • HTML: 1,881
  • PDF: 585
  • XML: 80
  • Total: 2,546
  • BibTeX: 78
  • EndNote: 82
Views and downloads (calculated since 07 Jun 2021)
Cumulative views and downloads (calculated since 07 Jun 2021)

Viewed (geographical distribution)

Total article views: 2,546 (including HTML, PDF, and XML) Thereof 2,464 with geography defined and 82 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 17 Jul 2024
Download
Short summary
It is important to detect the vertical distribution of cloud droplets and ice in mixed-phase clouds. Here, an artificial neural network (ANN) previously developed for Arctic clouds is applied to a mid-latitudinal cloud radar data set. The performance of this technique is contrasted to the Cloudnet target classification. For thick/multi-layer clouds, the machine learning technique is better at detecting liquid than Cloudnet, but if lidar data are available Cloudnet is at least as good as the ANN.
Read more