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

Improved rain event detection in commercial microwave link time series via combination with MSG SEVIRI data
Maximilian Graf, Andreas Wagner, Julius Polz, Llorenç Lliso, José Alberto Lahuerta, Harald Kunstmann, and Christian Chwala
Atmos. Meas. Tech., 17, 2165–2182, https://doi.org/10.5194/amt-17-2165-2024,https://doi.org/10.5194/amt-17-2165-2024, 2024
Short summary
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
Observations of tall-building wakes using a scanning Doppler lidar
Natalie E. Theeuwes, Janet F. Barlow, Antti Mannisenaho, Denise Hertwig, Ewan O'Connor, and Alan Robins
Atmos. Meas. Tech., 18, 1355–1371, https://doi.org/10.5194/amt-18-1355-2025,https://doi.org/10.5194/amt-18-1355-2025, 2025
Short summary
Mid-Atlantic nocturnal low-level jet characteristics: a machine learning analysis of radar wind profiles
Maurice Roots, John T. Sullivan, and Belay Demoz
Atmos. Meas. Tech., 18, 1269–1282, https://doi.org/10.5194/amt-18-1269-2025,https://doi.org/10.5194/amt-18-1269-2025, 2025
Short summary
Mitigating radome-induced bias in X-band weather radar polarimetric moments using an adaptive discrete Fourier transform algorithm
Padmanabhan Thiruvengadam, Guillaume Lesage, Ambinintsoa Volatiana Ramanamahefa, and Joël Van Baelen
Atmos. Meas. Tech., 18, 1185–1191, https://doi.org/10.5194/amt-18-1185-2025,https://doi.org/10.5194/amt-18-1185-2025, 2025
Short summary
GNSS-RO residual ionospheric error (RIE): a new method and assessment
Dong L. Wu, Valery A. Yudin, Kyu-Myong Kim, Mohar Chattopadhyay, Lawrence Coy, Ruth S. Lieberman, C. C. Jude H. Salinas, Jae N. Lee, Jie Gong, and Guiping Liu
Atmos. Meas. Tech., 18, 843–863, https://doi.org/10.5194/amt-18-843-2025,https://doi.org/10.5194/amt-18-843-2025, 2025
Short summary
Benchmarking KDP in rainfall: a quantitative assessment of estimation algorithms using C-band weather radar observations
Miguel Aldana, Seppo Pulkkinen, Annakaisa von Lerber, Matthew R. Kumjian, and Dmitri Moisseev
Atmos. Meas. Tech., 18, 793–816, https://doi.org/10.5194/amt-18-793-2025,https://doi.org/10.5194/amt-18-793-2025, 2025
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.
Share