Articles | Volume 13, issue 7
https://doi.org/10.5194/amt-13-3835-2020
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, Christian Chwala, Maximilian Graf, and Harald Kunstmann

Viewed

Total article views: 3,298 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,317 915 66 3,298 56 57
  • HTML: 2,317
  • PDF: 915
  • XML: 66
  • Total: 3,298
  • BibTeX: 56
  • EndNote: 57
Views and downloads (calculated since 19 Dec 2019)
Cumulative views and downloads (calculated since 19 Dec 2019)

Viewed (geographical distribution)

Total article views: 3,298 (including HTML, PDF, and XML) Thereof 2,956 with geography defined and 342 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 18 Jun 2024
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.