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.

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Julius Polz on behalf of the Authors (04 May 2020)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (14 May 2020) by Gianfranco Vulpiani
RR by Andreas Scheidegger (18 May 2020)
ED: Publish as is (25 May 2020) by Gianfranco Vulpiani
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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.