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.

Model code and software

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

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.