Articles | Volume 7, issue 10
https://doi.org/10.5194/amt-7-3549-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/amt-7-3549-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
17 Oct 2014
Research article |
| 17 Oct 2014
New algorithm for integration between wireless microwave sensor network and radar for improved rainfall measurement and mapping
Y. Liberman
Tel Aviv University, School Of Electrical Engineering, Tel Aviv 6997801, Israel
R. Samuels
Tel Aviv University, Porter School for Environmental Studies, Tel Aviv 6997801, Israel
P. Alpert
Tel Aviv University, Porter School for Environmental Studies, Tel Aviv 6997801, Israel
H. Messer
Tel Aviv University, School Of Electrical Engineering, Tel Aviv 6997801, Israel
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Cited
29 citations as recorded by crossref.
- Deep Learning for an Improved Prediction of Rainfall Retrievals From Commercial Microwave Links J. Pudashine et al. https://doi.org/10.1029/2019WR026255
- Rainfall Monitoring Using a Microwave Links Network: A Long-Term Experiment in East China X. Liu et al. https://doi.org/10.1007/s00376-023-2104-z
- Combining MWL and MSG SEVIRI Satellite Signals for Rainfall Detection and Estimation K. Kumah et al. https://doi.org/10.3390/atmos11090884
- Multimodal Data Fusion: An Overview of Methods, Challenges, and Prospects D. Lahat et al. https://doi.org/10.1109/JPROC.2015.2460697
- Improving Quantitative Precipitation Estimation Using Adaptive Z – R Relationship Adjustment: Merging Weather Radar With Commercial Microwave Links P. Zhang et al. https://doi.org/10.1109/TGRS.2025.3552794
- The Feasibility Analysis of Cellphone Signal to Detect the Rain: Experimental Study K. Song et al. https://doi.org/10.1109/LGRS.2019.2940854
- Reconstruction of rainfall fields using Microwave Links, Weather Radar, and Rain Gauge: First Results from the China Field Experiments B. He et al. https://doi.org/10.1088/1755-1315/384/1/012145
- Rainfall estimation from a German-wide commercial microwave link network: optimized processing and validation for 1 year of data M. Graf et al. https://doi.org/10.5194/hess-24-2931-2020
- Using Machine Learning Techniques for Rainfall Estimation Based on Microwave Links of Mobile Telecommunication Networks E. Kamtchoum et al. https://doi.org/10.1007/s42979-022-01458-6
- A review on factors influencing fog formation, classification, forecasting, detection and impacts K. Lakra & K. Avishek https://doi.org/10.1007/s12210-022-01060-1
- High‐Resolution Simulation Study Exploring the Potential of Radars, Crowdsourced Personal Weather Stations, and Commercial Microwave Links to Monitor Small‐Scale Urban Rainfall L. de Vos et al. https://doi.org/10.1029/2018WR023393
- Multimodal data fusion for systems improvement: A review N. Gaw et al. https://doi.org/10.1080/24725854.2021.1987593
- Cellular Network Infrastructure: The Future of Fog Monitoring? N. David et al. https://doi.org/10.1175/BAMS-D-13-00292.1
- Rainfall retrieval algorithm for commercial microwave links: stochastic calibration W. Wolff et al. https://doi.org/10.5194/amt-15-485-2022
- Improved rain event detection in commercial microwave link time series via combination with MSG SEVIRI data M. Graf et al. https://doi.org/10.5194/amt-17-2165-2024
- Capitalizing on Cellular Technology—Opportunities and Challenges for Near Ground Weather Monitoring H. Messer https://doi.org/10.3390/environments5070073
- Precipitation Monitoring Using Commercial Microwave Links: Current Status, Challenges and Prospectives P. Zhang et al. https://doi.org/10.3390/rs15194821
- Utilizing microwave communication data for detecting fog where satellite retrievals are challenged N. David https://doi.org/10.1007/s11069-018-3428-3
- Fast Bayesian Regression Kriging Method for Real‐Time Merging of Radar, Rain Gauge, and Crowdsourced Rainfall Data P. Yang & T. Ng https://doi.org/10.1029/2018WR023857
- Stochastic Reconstruction and Interpolation of Precipitation Fields Using Combined Information of Commercial Microwave Links and Rain Gauges B. Haese et al. https://doi.org/10.1002/2017WR021015
- Experimental Study of Detecting Rainfall Using Microwave Links: Classification of Wet and Dry Periods K. Song et al. https://doi.org/10.1109/JSTARS.2020.3021555
- Using Cellular Communication Networks To Detect Air Pollution N. David & H. Gao https://doi.org/10.1021/acs.est.6b00681
- Two and a half years of country-wide rainfall maps using radio links from commercial cellular telecommunication networks A. Overeem et al. https://doi.org/10.1002/2016WR019412
- Vertical Precipitation Estimation Using Microwave Links in Conjunction with Weather Radar R. Raich et al. https://doi.org/10.3390/environments5070074
- Commercial microwave link networks for rainfall observation: Assessment of the current status and future challenges C. Chwala & H. Kunstmann https://doi.org/10.1002/wat2.1337
- Rainfall Monitoring Based on Next-Generation Millimeter-Wave Backhaul Technologies in a Dense Urban Environment C. Han et al. https://doi.org/10.3390/rs12061045
- Rainfall estimates from opportunistic sensors in Germany across spatio-temporal scales M. Graf et al. https://doi.org/10.1016/j.ejrh.2021.100883
- A Machine Learning Approach for the Classification of Wet and Dry Periods Using Commercial Microwave Link Data E. Kamtchoum et al. https://doi.org/10.1007/s42979-022-01143-8
- Gauging Through the Crowd: A Crowd‐Sourcing Approach to Urban Rainfall Measurement and Storm Water Modeling Implications P. Yang & T. Ng https://doi.org/10.1002/2017WR020682
29 citations as recorded by crossref.
- Deep Learning for an Improved Prediction of Rainfall Retrievals From Commercial Microwave Links J. Pudashine et al. https://doi.org/10.1029/2019WR026255
- Rainfall Monitoring Using a Microwave Links Network: A Long-Term Experiment in East China X. Liu et al. https://doi.org/10.1007/s00376-023-2104-z
- Combining MWL and MSG SEVIRI Satellite Signals for Rainfall Detection and Estimation K. Kumah et al. https://doi.org/10.3390/atmos11090884
- Multimodal Data Fusion: An Overview of Methods, Challenges, and Prospects D. Lahat et al. https://doi.org/10.1109/JPROC.2015.2460697
- Improving Quantitative Precipitation Estimation Using Adaptive Z – R Relationship Adjustment: Merging Weather Radar With Commercial Microwave Links P. Zhang et al. https://doi.org/10.1109/TGRS.2025.3552794
- The Feasibility Analysis of Cellphone Signal to Detect the Rain: Experimental Study K. Song et al. https://doi.org/10.1109/LGRS.2019.2940854
- Reconstruction of rainfall fields using Microwave Links, Weather Radar, and Rain Gauge: First Results from the China Field Experiments B. He et al. https://doi.org/10.1088/1755-1315/384/1/012145
- Rainfall estimation from a German-wide commercial microwave link network: optimized processing and validation for 1 year of data M. Graf et al. https://doi.org/10.5194/hess-24-2931-2020
- Using Machine Learning Techniques for Rainfall Estimation Based on Microwave Links of Mobile Telecommunication Networks E. Kamtchoum et al. https://doi.org/10.1007/s42979-022-01458-6
- A review on factors influencing fog formation, classification, forecasting, detection and impacts K. Lakra & K. Avishek https://doi.org/10.1007/s12210-022-01060-1
- High‐Resolution Simulation Study Exploring the Potential of Radars, Crowdsourced Personal Weather Stations, and Commercial Microwave Links to Monitor Small‐Scale Urban Rainfall L. de Vos et al. https://doi.org/10.1029/2018WR023393
- Multimodal data fusion for systems improvement: A review N. Gaw et al. https://doi.org/10.1080/24725854.2021.1987593
- Cellular Network Infrastructure: The Future of Fog Monitoring? N. David et al. https://doi.org/10.1175/BAMS-D-13-00292.1
- Rainfall retrieval algorithm for commercial microwave links: stochastic calibration W. Wolff et al. https://doi.org/10.5194/amt-15-485-2022
- Improved rain event detection in commercial microwave link time series via combination with MSG SEVIRI data M. Graf et al. https://doi.org/10.5194/amt-17-2165-2024
- Capitalizing on Cellular Technology—Opportunities and Challenges for Near Ground Weather Monitoring H. Messer https://doi.org/10.3390/environments5070073
- Precipitation Monitoring Using Commercial Microwave Links: Current Status, Challenges and Prospectives P. Zhang et al. https://doi.org/10.3390/rs15194821
- Utilizing microwave communication data for detecting fog where satellite retrievals are challenged N. David https://doi.org/10.1007/s11069-018-3428-3
- Fast Bayesian Regression Kriging Method for Real‐Time Merging of Radar, Rain Gauge, and Crowdsourced Rainfall Data P. Yang & T. Ng https://doi.org/10.1029/2018WR023857
- Stochastic Reconstruction and Interpolation of Precipitation Fields Using Combined Information of Commercial Microwave Links and Rain Gauges B. Haese et al. https://doi.org/10.1002/2017WR021015
- Experimental Study of Detecting Rainfall Using Microwave Links: Classification of Wet and Dry Periods K. Song et al. https://doi.org/10.1109/JSTARS.2020.3021555
- Using Cellular Communication Networks To Detect Air Pollution N. David & H. Gao https://doi.org/10.1021/acs.est.6b00681
- Two and a half years of country-wide rainfall maps using radio links from commercial cellular telecommunication networks A. Overeem et al. https://doi.org/10.1002/2016WR019412
- Vertical Precipitation Estimation Using Microwave Links in Conjunction with Weather Radar R. Raich et al. https://doi.org/10.3390/environments5070074
- Commercial microwave link networks for rainfall observation: Assessment of the current status and future challenges C. Chwala & H. Kunstmann https://doi.org/10.1002/wat2.1337
- Rainfall Monitoring Based on Next-Generation Millimeter-Wave Backhaul Technologies in a Dense Urban Environment C. Han et al. https://doi.org/10.3390/rs12061045
- Rainfall estimates from opportunistic sensors in Germany across spatio-temporal scales M. Graf et al. https://doi.org/10.1016/j.ejrh.2021.100883
- A Machine Learning Approach for the Classification of Wet and Dry Periods Using Commercial Microwave Link Data E. Kamtchoum et al. https://doi.org/10.1007/s42979-022-01143-8
- Gauging Through the Crowd: A Crowd‐Sourcing Approach to Urban Rainfall Measurement and Storm Water Modeling Implications P. Yang & T. Ng https://doi.org/10.1002/2017WR020682
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