Articles | Volume 13, issue 7
Atmos. Meas. Tech., 13, 3835–3853, 2020
https://doi.org/10.5194/amt-13-3835-2020
Atmos. Meas. Tech., 13, 3835–3853, 2020
https://doi.org/10.5194/amt-13-3835-2020

Research article 15 Jul 2020

Research article | 15 Jul 2020

Rain event detection in commercial microwave link attenuation data using convolutional neural networks

Julius Polz et al.

Viewed

Total article views: 1,600 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,156 423 21 1,600 15 14
  • HTML: 1,156
  • PDF: 423
  • XML: 21
  • Total: 1,600
  • BibTeX: 15
  • EndNote: 14
Views and downloads (calculated since 19 Dec 2019)
Cumulative views and downloads (calculated since 19 Dec 2019)

Viewed (geographical distribution)

Total article views: 1,326 (including HTML, PDF, and XML) Thereof 1,290 with geography defined and 36 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Discussed (final revised paper)

Latest update: 08 May 2021
Download
Short summary
Commercial microwave link (CML) networks can be used to estimate path-averaged rain rates. This study evaluates the ability of convolutional neural networks to distinguish between wet and dry periods in CML time series data and the ability to transfer this detection skill to sensors not used for training. Our data set consists of several months of data from 3904 CMLs covering all of Germany. Compared to a previously used detection method, we could show a significant increase in performance.