Articles | Volume 24, issue 6
https://doi.org/10.5194/hess-24-2931-2020
© 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/hess-24-2931-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
04 Jun 2020
Research article |
| 04 Jun 2020
| 04 Jun 2020
Rainfall estimation from a German-wide commercial microwave link network: optimized processing and validation for 1 year of data
Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Campus Alpin, Garmisch-Partenkirchen, Germany
Christian Chwala
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 Augsburg, Augsburg, Germany
Julius Polz
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 Augsburg, Augsburg, Germany
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- Precipitation Estimates From Commercial Microwave Links: Practical Approaches to Wet-Antenna Correction J. Pastorek et al. 10.1109/TGRS.2021.3110004
- 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
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- Deep Learning for an Improved Prediction of Rainfall Retrievals From Commercial Microwave Links J. Pudashine et al. 10.1029/2019WR026255
- Improved Gridded Precipitation Data Derived from Microwave Link Attenuation M. Silver et al. 10.3390/rs13152953
- Data formats and standards for opportunistic rainfall sensors M. Fencl et al. 10.12688/openreseurope.16068.2
- Data formats and standards for opportunistic rainfall sensors M. Fencl et al. 10.12688/openreseurope.16068.1
48 citations as recorded by crossref.
- Tropical rainfall monitoring with commercial microwave links in Sri Lanka A. Overeem et al. 10.1088/1748-9326/ac0fa6
- Rainfall retrieval algorithm for commercial microwave links: stochastic calibration W. Wolff et al. 10.5194/amt-15-485-2022
- Transboundary Rainfall Estimation Using Commercial Microwave Links N. Blettner et al. 10.1029/2023EA002869
- Harnessing the Radio Frequency Power Level of Cellular Terminals for Weather Parameter Sensing A. Sakkas et al. 10.3390/electronics13050840
- In-City Rain Mapping from Commercial Microwave Links—Challenges and Opportunities R. Janco et al. 10.3390/s23104653
- Intensity estimation after detection for accumulated rainfall estimation T. Weiss et al. 10.3389/frsip.2024.1291878
- Rain event detection in commercial microwave link attenuation data using convolutional neural networks J. Polz et al. 10.5194/amt-13-3835-2020
- Exploring Environmental Information From Smartphone Signals: A Light Indoor Stationary Experimental Study for Rainfall Detection K. Pu et al. 10.1109/LGRS.2024.3455431
- Rainfall retrieval using commercial microwave links: Effect of sampling strategy on retrieval accuracy J. Pudashine et al. 10.1016/j.jhydrol.2021.126909
- Precipitation measurements experiment using microwave links in Eastern China from October 2020 to March 2022 K. Pu & X. Liu 10.1016/j.jhydrol.2024.131922
- Missing Rainfall Extremes in Commercial Microwave Link Data Due To Complete Loss of Signal J. Polz et al. 10.1029/2022EA002456
- Atmospheric Observations of Weather and Climate H. Bluestein et al. 10.1080/07055900.2022.2082369
- Regenmessung im Mobilfunknetz M. Graf et al. 10.1002/piuz.202001602
- Technical note: A simple feedforward artificial neural network for high-temporal-resolution rain event detection using signal attenuation from commercial microwave links E. Øydvin et al. 10.5194/hess-28-5163-2024
- Opportunistic Weather Sensing by Smart City Wireless Communication Networks J. Ostrometzky & H. Messer 10.3390/s24247901
- Hydrological response of a peri-urban catchment exploiting conventional and unconventional rainfall observations: the case study of Lambro Catchment G. Cazzaniga et al. 10.5194/hess-26-2093-2022
- Measuring Precipitation via Microwave Bands with a High-Accuracy Setup A. Sakkas et al. 10.3390/s24248056
- Precipitation Monitoring Using Commercial Microwave Links: Current Status, Challenges and Prospectives P. Zhang et al. 10.3390/rs15194821
- Reconstructing and Nowcasting the Rainfall Field by a CML Network P. Zhang et al. 10.1029/2023EA002909
- Challenges in Diurnal Humidity Analysis from Cellular Microwave Links (CML) over Germany Y. Rubin et al. 10.3390/rs14102353
- Rainfall Monitoring Using a Microwave Links Network: A Long-Term Experiment in East China X. Liu et al. 10.1007/s00376-023-2104-z
- Water vapor estimation based on 1-year data of E-band millimeter wave link in North China S. Zheng et al. 10.5194/amt-15-1675-2022
- 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
- Rainfall Nowcasting Using Commercial Microwave Links R. Imhoff et al. 10.1029/2020GL089365
- Wildfire Smoke Particulate Matter Concentration Measurements Using Radio Links From Cellular Communication Networks A. Guyot et al. 10.1029/2020AV000258
- Low complexity single-layer neural network for enhanced rainfall estimation using microwave links A. Daher et al. 10.2166/hydro.2022.099
- Research on Rainfall Monitoring Based on E-Band Millimeter Wave Link in East China S. Zheng et al. 10.3390/s21051670
- Application of commercial microwave links (CMLs) attenuation for quantitative estimation of precipitation M. Pasierb et al. 10.1002/met.2218
- On the potential of commercial microwave link networks for high spatial resolution rainfall monitoring in urban areas X. Zheng et al. 10.1016/j.atmosres.2022.106289
- Real-Time Rainfall Estimation Using Microwave Links: A Case Study in East China during the Plum Rain Season in 2020 K. Song et al. 10.3390/s21030858
- From proof‐of‐concept to proof‐of‐value: Approaching third‐party data to operational workflows of national meteorological services I. Garcia‐Marti et al. 10.1002/joc.7757
- A Statistical Approach to Retrieve Rainfall Intensity by Attenuation Measurements From Commercial Microwave Links X. Zheng et al. 10.1029/2023EF003718
- New Insights Into the Dynamics of Wet Antenna Attenuation Based on In Situ Estimations Provided by the Dedicated Field Experiment ATTRRA2 J. Tiede et al. 10.1109/LGRS.2023.3320755
- 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
- Frühwarnung mit Mobilfunknetzdaten - HoWa-innovativ U. Müller et al. 10.1007/s35147-023-1909-0
- 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
- Rainfall estimates from opportunistic sensors in Germany across spatio-temporal scales M. Graf et al. 10.1016/j.ejrh.2021.100883
- Comparison of CML Rainfall Data against Rain Gauges and Disdrometers in a Mountainous Environment R. Nebuloni et al. 10.3390/s22093218
- Merging with crowdsourced rain gauge data improves pan-European radar precipitation estimates A. Overeem et al. 10.5194/hess-28-649-2024
- Detection and attribution of cereal yield losses using Sentinel-2 and weather data: A case study in South Australia K. Duan et al. 10.1016/j.isprsjprs.2024.05.021
- Wireless Telecommunication Links for Rainfall Monitoring: Deep Learning Approach and Experimental Results F. Diba et al. 10.1109/ACCESS.2021.3076781
- EURADCLIM: the European climatological high-resolution gauge-adjusted radar precipitation dataset A. Overeem et al. 10.5194/essd-15-1441-2023
- The divergence of energy- and water-balance evapotranspiration estimates in humid regions L. Zhang et al. 10.1016/j.jhydrol.2023.129971
- Evaluation of error components in rainfall retrieval from collocated commercial microwave links A. Špačková et al. 10.5194/amt-16-3865-2023
- Precipitation Estimates From Commercial Microwave Links: Practical Approaches to Wet-Antenna Correction J. Pastorek et al. 10.1109/TGRS.2021.3110004
- 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
- Applicability of the γ–R Relationship in Rainfall Measurement With Microwave Link Under Tilted Conditions: A Simulation Analysis K. Pu et al. 10.1109/TGRS.2023.3330575
- Measuring rainfall using microwave links: the influence of temporal sampling L. van der Valk et al. 10.5194/amt-17-2811-2024
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- Deep Learning for an Improved Prediction of Rainfall Retrievals From Commercial Microwave Links J. Pudashine et al. 10.1029/2019WR026255
- Improved Gridded Precipitation Data Derived from Microwave Link Attenuation M. Silver et al. 10.3390/rs13152953
- Data formats and standards for opportunistic rainfall sensors M. Fencl et al. 10.12688/openreseurope.16068.2
- Data formats and standards for opportunistic rainfall sensors M. Fencl et al. 10.12688/openreseurope.16068.1
Latest update: 25 Dec 2024
Short summary
Commercial microwave links (CMLs), which form large parts of the backhaul from the ubiquitous cellular communication networks, can be used to estimate path-integrated rainfall rates. This study presents the processing and evaluation of the largest CML data set to date, covering the whole of Germany with almost 4000 CMLs. The CML-derived rainfall information compares well to a standard precipitation data set from the German Meteorological Service, which combines radar and rain gauge data.
Commercial microwave links (CMLs), which form large parts of the backhaul from the ubiquitous...
