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

Related authors

Rainfall estimation from a German-wide commercial microwave link network: optimized processing and validation for 1 year of data
Maximilian Graf, Christian Chwala, Julius Polz, and Harald Kunstmann
Hydrol. Earth Syst. Sci., 24, 2931–2950, https://doi.org/10.5194/hess-24-2931-2020,https://doi.org/10.5194/hess-24-2931-2020, 2020
Short summary

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Long-term multi-source precipitation estimation with high resolution (RainGRS Clim)
Anna Jurczyk, Katarzyna Ośródka, Jan Szturc, Magdalena Pasierb, and Agnieszka Kurcz
Atmos. Meas. Tech., 16, 4067–4079, https://doi.org/10.5194/amt-16-4067-2023,https://doi.org/10.5194/amt-16-4067-2023, 2023
Short summary
Retrieval of snow layer and melt pond properties on Arctic sea ice from airborne imaging spectrometer observations
Sophie Rosenburg, Charlotte Lange, Evelyn Jäkel, Michael Schäfer, André Ehrlich, and Manfred Wendisch
Atmos. Meas. Tech., 16, 3915–3930, https://doi.org/10.5194/amt-16-3915-2023,https://doi.org/10.5194/amt-16-3915-2023, 2023
Short summary
Using optimal estimation to retrieve winds from velocity-azimuth display (VAD) scans by a Doppler lidar
Sunil Baidar, Timothy J. Wagner, David D. Turner, and W. Alan Brewer
Atmos. Meas. Tech., 16, 3715–3726, https://doi.org/10.5194/amt-16-3715-2023,https://doi.org/10.5194/amt-16-3715-2023, 2023
Short summary
Angular sampling of a monochromatic, wide-field-of-view camera to augment next-generation Earth radiation budget satellite observations
Jake J. Gristey, K. Sebastian Schmidt, Hong Chen, Daniel R. Feldman, Bruce C. Kindel, Joshua Mauss, Mathew van den Heever, Maria Z. Hakuba, and Peter Pilewskie
Atmos. Meas. Tech., 16, 3609–3630, https://doi.org/10.5194/amt-16-3609-2023,https://doi.org/10.5194/amt-16-3609-2023, 2023
Short summary
Higher-Order Calibration on WindRAD scatterometer winds
Zhen Li, Ad Stoffelen, and Anton Verhoef
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-112,https://doi.org/10.5194/amt-2023-112, 2023
Revised manuscript accepted for AMT
Short summary

Cited articles

Akoglu, H.: User's guide to correlation coefficients, Turkish Journal of Emergency Medicine, 18, 91–93, https://doi.org/10.1016/j.tjem.2018.08.001, 2018. a
Baldi, P., Brunak, S., Chauvin, Y., Andersen, C. A. F., and Nielsen, H.: Assessing the accuracy of prediction algorithms for classification: an overview, Bioinformatics, 16, 412–424, https://doi.org/10.1093/bioinformatics/16.5.412, 2000. a
Bottou, L., Curtis, F. E., and Nocedal, J.: Optimization Methods for Large-Scale Machine Learning, SIAM Rev., 60, 223–311, https://doi.org/10.1137/16M1080173, 2018. a
Bundesnetzagentur: Tätigkeitsbericht Telekommunikation 2016/2017, Tech. rep., Report 2016/2017, Bundesnetzagentur für Elektrizität, Gas, Telekommunikation, Post und Eisenbahnen, Bonn, available at: https://www.bundesnetzagentur.de/SharedDocs/Downloads/DE/Allgemeines/Bundesnetzagentur/Publikationen/Berichte/2017/TB_Telekommunikation20162017.pdf?__blob=publicationFile&v=3 (last access: 2 July 2020), 2017. a
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.