Articles | Volume 28, issue 23
https://doi.org/10.5194/hess-28-5163-2024
https://doi.org/10.5194/hess-28-5163-2024
Technical note
 | 
29 Nov 2024
Technical note |  | 29 Nov 2024

Technical note: A simple feedforward artificial neural network for high-temporal-resolution rain event detection using signal attenuation from commercial microwave links

Erlend Øydvin, Maximilian Graf, Christian Chwala, Mareile Astrid Wolff, Nils-Otto Kitterød, and Vegard Nilsen

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Combining commercial microwave links and weather radar for classification of dry snow and rainfall
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EGUsphere, https://doi.org/10.5194/egusphere-2024-2625,https://doi.org/10.5194/egusphere-2024-2625, 2024
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
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Cited articles

Andersson, J. C. M., Olsson, J., van de Beek, R. (C. Z.), and Hansryd, J.: OpenMRG: Open data from Microwave links, Radar, and Gauges for rainfall quantification in Gothenburg, Sweden, Earth Syst. Sci. Data, 14, 5411–5426, https://doi.org/10.5194/essd-14-5411-2022, 2022. a
Blettner, N., Fencl, M., Bareš, V., Kunstmann, H., and Chwala, C.: Transboundary Rainfall Estimation Using Commercial Microwave Links, Earth Space Sci., 10, e2023EA002869, https://doi.org/10.1029/2023EA002869, 2023. a, b
Chicco, D. and Jurman, G.: The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation, BMC Genomics, 21, 6, https://doi.org/10.1186/s12864-019-6413-7, 2020. a
Chwala, C. and Kunstmann, H.: Commercial microwave link networks for rainfall observation: Assessment of the current status and future challenges, WIREs Water, 6, e1337, https://doi.org/10.1002/wat2.1337, 2019. a, b
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Two simple neural networks are trained to detect rainfall events using signal loss from commercial microwave links. Whereas existing rainfall event detection methods have focused on hourly resolution reference data, this study uses weather radar and rain gauges with 5 min and 1 min temporal resolutions, respectively. Our results show that the developed neural networks can detect rainfall events with a higher temporal precision than existing methods.