Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 13, issue 7
Articles | Volume 13, issue 7
https://doi.org/10.5194/amt-13-3835-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-13-3835-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 13, issue 7
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

Data sets

RADOLAN-RW DWD CDC https://opendata.dwd.de/climate_environment/CDC/grids_germany/hourly/radolan/

Model code and software

cnn_cml_wet-dry_example Julius Polz https://github.com/jpolz/cnn_cml_wet-dry_example

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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.
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