Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 12, issue 8
Articles | Volume 12, issue 8
https://doi.org/10.5194/amt-12-4591-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-12-4591-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 12, issue 8
Research article
 | 
30 Aug 2019
Research article |  | 30 Aug 2019

Development and validation of a supervised machine learning radar Doppler spectra peak-finding algorithm

Heike Kalesse, Teresa Vogl, Cosmin Paduraru, and Edward Luke

Viewed

Total article views: 3,252 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,912 1,256 84 3,252 88 70
  • HTML: 1,912
  • PDF: 1,256
  • XML: 84
  • Total: 3,252
  • BibTeX: 88
  • EndNote: 70
Views and downloads (calculated since 28 Mar 2019)
Cumulative views and downloads (calculated since 28 Mar 2019)

Viewed (geographical distribution)

Total article views: 3,252 (including HTML, PDF, and XML) Thereof 3,001 with geography defined and 251 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 04 Nov 2024
Download
Short summary
In a cloud, different particles like liquid water droplets and ice particles can exist simultaneously. To study the evolution of cloud particles from cloud top to bottom one has to find out how many different types of particles with different fall velocities are present. This can be done by analyzing the number of peaks in upward-looking cloud radar Doppler spectra. A new machine-learning algorithm (named PEAKO) that determines the number of peaks is introduced and compared to existing methods.
Read more