Articles | Volume 15, issue 2
https://doi.org/10.5194/amt-15-365-2022
https://doi.org/10.5194/amt-15-365-2022
Research article
 | 
24 Jan 2022
Research article |  | 24 Jan 2022

Using artificial neural networks to predict riming from Doppler cloud radar observations

Teresa Vogl, Maximilian Maahn, Stefan Kneifel, Willi Schimmel, Dmitri Moisseev, and Heike Kalesse-Los

Viewed

Total article views: 2,980 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,123 766 91 2,980 81 76
  • HTML: 2,123
  • PDF: 766
  • XML: 91
  • Total: 2,980
  • BibTeX: 81
  • EndNote: 76
Views and downloads (calculated since 19 May 2021)
Cumulative views and downloads (calculated since 19 May 2021)

Viewed (geographical distribution)

Total article views: 2,980 (including HTML, PDF, and XML) Thereof 2,877 with geography defined and 103 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 26 Dec 2024
Download
Short summary
We are using machine learning techniques, a type of artificial intelligence, to detect graupel formation in clouds. The measurements used as input to the machine learning framework were performed by cloud radars. Cloud radars are instruments located at the ground, emitting radiation with wavelenghts of a few millimeters vertically into the cloud and measuring the back-scattered signal. Our novel technique can be applied to different radar systems and different weather conditions.