Articles | Volume 13, issue 7
https://doi.org/10.5194/amt-13-3835-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-13-3835-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 Jul 2020
Research article |
| 15 Jul 2020
| 15 Jul 2020
Rain event detection in commercial microwave link attenuation data using convolutional neural networks
Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Campus Alpin, Garmisch-Partenkirchen, Germany
Christian Chwala
CORRESPONDING AUTHOR
Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Campus Alpin, Garmisch-Partenkirchen, Germany
Chair of Regional Climate and Hydrology, Institute of Geography, University of Augsburg, Augsburg, Germany
Maximilian Graf
Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Campus Alpin, Garmisch-Partenkirchen, Germany
Harald Kunstmann
Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Campus Alpin, Garmisch-Partenkirchen, Germany
Chair of Regional Climate and Hydrology, Institute of Geography, University of Augsburg, Augsburg, Germany
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33 citations as recorded by crossref.
- A Convolutional Neural Network (CNN) classification to identify the presence of pores in powder bed fusion images M. Ansari et al. 10.1007/s00170-022-08995-7
- On the Potential of Using Emerging Microwave Links for City Rainfall Monitoring C. Han et al. 10.1109/MCOM.001.2200975
- Artificial Intelligent For Rainfall Estimation In Tropical Region : A Survey R. Mardyansyah et al. 10.1088/1755-1315/1105/1/012024
- Wireless Telecommunication Links for Rainfall Monitoring: Deep Learning Approach and Experimental Results F. Diba et al. 10.1109/ACCESS.2021.3076781
- Improved rain event detection in commercial microwave link time series via combination with MSG SEVIRI data M. Graf et al. 10.5194/amt-17-2165-2024
- Regenmessung im Mobilfunknetz M. Graf et al. 10.1002/piuz.202001602
- Deep Spatial Interpolation of Rain Field for U.K. Satellite Networks G. Yang et al. 10.1109/TAP.2022.3229175
- Harnessing the Radio Frequency Power Level of Cellular Terminals for Weather Parameter Sensing A. Sakkas et al. 10.3390/electronics13050840
- A year of attenuation data from a commercial dual-polarized duplex microwave link with concurrent disdrometer, rain gauge, and weather observations A. Špačková et al. 10.5194/essd-13-4219-2021
- Precipitation Monitoring Using Commercial Microwave Links: Current Status, Challenges and Prospectives P. Zhang et al. 10.3390/rs15194821
- Tropical rainfall monitoring with commercial microwave links in Sri Lanka A. Overeem et al. 10.1088/1748-9326/ac0fa6
- Missing Rainfall Extremes in Commercial Microwave Link Data Due To Complete Loss of Signal J. Polz et al. 10.1029/2022EA002456
- Intensity estimation after detection for accumulated rainfall estimation T. Weiss et al. 10.3389/frsip.2024.1291878
- Short-term rainfall prediction using MLA based on commercial microwave links of mobile telecommunication networks E. Kamtchoum et al. 10.1007/s42865-022-00047-y
- Combining Commercial Microwave Link and Rain Gauge Observations to Estimate Countrywide Precipitation: A Stochastic Reconstruction and Pattern Analysis Approach N. Blettner et al. 10.1029/2022WR032563
- COMPARISON OF MEASURED SLANT PATH ATTENUATION AT 10.982 GHZ WITH LARGE-SCALE PREDICTION MODELS IN A TROPICAL CLIMATE Y. Abdulrahman 10.1615/TelecomRadEng.2023046608
- Frühwarnung mit Mobilfunknetzdaten - HoWa-innovativ U. Müller et al. 10.1007/s35147-023-1909-0
- Rain Discrimination with Machine Learning Classifiers for Opportunistic Rain Detection System Using Satellite Micro-Wave Links C. Gianoglio et al. 10.3390/s23031202
- Error Analysis of Rainfall Inversion Based on Commercial Microwave Links With A–R Relationship Considering the Rainfall Features K. Pu et al. 10.1109/TGRS.2023.3253949
- Analysis of Wet Antenna Losses on 11.843 GHz Slant Path in Nigeria and Comparison with Some Tropical Climates A. Abdulrahman 10.1590/2179-10742022v21i2259250
- Classification of Dry and Wet Periods Using Commercial Microwave Links: A One-Class Classification Machine Learning Approach Based on Autoencoders P. Zhang et al. 10.1109/TGRS.2023.3339682
- Rain Field Retrieval by Ground-Level Sensors of Various Types H. Messer et al. 10.3389/frsip.2022.877336
- A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks B. Lian et al. 10.3390/s22124395
- Rainfall retrieval algorithm for commercial microwave links: stochastic calibration W. Wolff et al. 10.5194/amt-15-485-2022
- A Machine Learning Approach for the Classification of Wet and Dry Periods Using Commercial Microwave Link Data E. Kamtchoum et al. 10.1007/s42979-022-01143-8
- Low complexity single-layer neural network for enhanced rainfall estimation using microwave links A. Daher et al. 10.2166/hydro.2022.099
- Rainfall Monitoring Using a Microwave Links Network: A Long-Term Experiment in East China X. Liu et al. 10.1007/s00376-023-2104-z
- Rainfall intensity in geomorphology: Challenges and opportunities D. Dunkerley 10.1177/0309133320967893
- In-City Rain Mapping from Commercial Microwave Links—Challenges and Opportunities R. Janco et al. 10.3390/s23104653
- Rainfall estimates from opportunistic sensors in Germany across spatio-temporal scales M. Graf et al. 10.1016/j.ejrh.2021.100883
- Rainfall estimation from a German-wide commercial microwave link network: optimized processing and validation for 1 year of data M. Graf et al. 10.5194/hess-24-2931-2020
- Earth-to-Earth Microwave Rain Attenuation Measurements: A Survey On the Recent Literature V. Christofilakis et al. 10.3390/sym12091440
- Deep Learning for an Improved Prediction of Rainfall Retrievals From Commercial Microwave Links J. Pudashine et al. 10.1029/2019WR026255
30 citations as recorded by crossref.
- A Convolutional Neural Network (CNN) classification to identify the presence of pores in powder bed fusion images M. Ansari et al. 10.1007/s00170-022-08995-7
- On the Potential of Using Emerging Microwave Links for City Rainfall Monitoring C. Han et al. 10.1109/MCOM.001.2200975
- Artificial Intelligent For Rainfall Estimation In Tropical Region : A Survey R. Mardyansyah et al. 10.1088/1755-1315/1105/1/012024
- Wireless Telecommunication Links for Rainfall Monitoring: Deep Learning Approach and Experimental Results F. Diba et al. 10.1109/ACCESS.2021.3076781
- Improved rain event detection in commercial microwave link time series via combination with MSG SEVIRI data M. Graf et al. 10.5194/amt-17-2165-2024
- Regenmessung im Mobilfunknetz M. Graf et al. 10.1002/piuz.202001602
- Deep Spatial Interpolation of Rain Field for U.K. Satellite Networks G. Yang et al. 10.1109/TAP.2022.3229175
- Harnessing the Radio Frequency Power Level of Cellular Terminals for Weather Parameter Sensing A. Sakkas et al. 10.3390/electronics13050840
- A year of attenuation data from a commercial dual-polarized duplex microwave link with concurrent disdrometer, rain gauge, and weather observations A. Špačková et al. 10.5194/essd-13-4219-2021
- Precipitation Monitoring Using Commercial Microwave Links: Current Status, Challenges and Prospectives P. Zhang et al. 10.3390/rs15194821
- Tropical rainfall monitoring with commercial microwave links in Sri Lanka A. Overeem et al. 10.1088/1748-9326/ac0fa6
- Missing Rainfall Extremes in Commercial Microwave Link Data Due To Complete Loss of Signal J. Polz et al. 10.1029/2022EA002456
- Intensity estimation after detection for accumulated rainfall estimation T. Weiss et al. 10.3389/frsip.2024.1291878
- Short-term rainfall prediction using MLA based on commercial microwave links of mobile telecommunication networks E. Kamtchoum et al. 10.1007/s42865-022-00047-y
- Combining Commercial Microwave Link and Rain Gauge Observations to Estimate Countrywide Precipitation: A Stochastic Reconstruction and Pattern Analysis Approach N. Blettner et al. 10.1029/2022WR032563
- COMPARISON OF MEASURED SLANT PATH ATTENUATION AT 10.982 GHZ WITH LARGE-SCALE PREDICTION MODELS IN A TROPICAL CLIMATE Y. Abdulrahman 10.1615/TelecomRadEng.2023046608
- Frühwarnung mit Mobilfunknetzdaten - HoWa-innovativ U. Müller et al. 10.1007/s35147-023-1909-0
- Rain Discrimination with Machine Learning Classifiers for Opportunistic Rain Detection System Using Satellite Micro-Wave Links C. Gianoglio et al. 10.3390/s23031202
- Error Analysis of Rainfall Inversion Based on Commercial Microwave Links With A–R Relationship Considering the Rainfall Features K. Pu et al. 10.1109/TGRS.2023.3253949
- Analysis of Wet Antenna Losses on 11.843 GHz Slant Path in Nigeria and Comparison with Some Tropical Climates A. Abdulrahman 10.1590/2179-10742022v21i2259250
- Classification of Dry and Wet Periods Using Commercial Microwave Links: A One-Class Classification Machine Learning Approach Based on Autoencoders P. Zhang et al. 10.1109/TGRS.2023.3339682
- Rain Field Retrieval by Ground-Level Sensors of Various Types H. Messer et al. 10.3389/frsip.2022.877336
- A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks B. Lian et al. 10.3390/s22124395
- Rainfall retrieval algorithm for commercial microwave links: stochastic calibration W. Wolff et al. 10.5194/amt-15-485-2022
- A Machine Learning Approach for the Classification of Wet and Dry Periods Using Commercial Microwave Link Data E. Kamtchoum et al. 10.1007/s42979-022-01143-8
- Low complexity single-layer neural network for enhanced rainfall estimation using microwave links A. Daher et al. 10.2166/hydro.2022.099
- Rainfall Monitoring Using a Microwave Links Network: A Long-Term Experiment in East China X. Liu et al. 10.1007/s00376-023-2104-z
- Rainfall intensity in geomorphology: Challenges and opportunities D. Dunkerley 10.1177/0309133320967893
- In-City Rain Mapping from Commercial Microwave Links—Challenges and Opportunities R. Janco et al. 10.3390/s23104653
- Rainfall estimates from opportunistic sensors in Germany across spatio-temporal scales M. Graf et al. 10.1016/j.ejrh.2021.100883
3 citations as recorded by crossref.
- Rainfall estimation from a German-wide commercial microwave link network: optimized processing and validation for 1 year of data M. Graf et al. 10.5194/hess-24-2931-2020
- Earth-to-Earth Microwave Rain Attenuation Measurements: A Survey On the Recent Literature V. Christofilakis et al. 10.3390/sym12091440
- Deep Learning for an Improved Prediction of Rainfall Retrievals From Commercial Microwave Links J. Pudashine et al. 10.1029/2019WR026255
Latest update: 24 Apr 2024
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.
Commercial microwave link (CML) networks can be used to estimate path-averaged rain rates. This...
