Articles | Volume 5, issue 7
https://doi.org/10.5194/amt-5-1847-2012
© Author(s) 2012. 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-5-1847-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
31 Jul 2012
Research article |
| 31 Jul 2012
Using Markov switching models to infer dry and rainy periods from telecommunication microwave link signals
Z. Wang
The Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland
M. Schleiss
Laboratoire de Télédétection Environnementale (LTE), École polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland
J. Jaffrain
Laboratoire de Télédétection Environnementale (LTE), École polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland
A. Berne
Laboratoire de Télédétection Environnementale (LTE), École polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland
J. Rieckermann
The Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland
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Cited
45 citations as recorded by crossref.
- Systematic Literature Review With Bibliometric Analysis on Markov Switching Model: Methods and Applications S. Phoong et al. https://doi.org/10.1177/21582440221093062
- 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. https://doi.org/10.5194/hess-28-5163-2024
- Classification of Dry and Wet Periods Using Commercial Microwave Links: A One-Class Classification Machine Learning Approach Based on Autoencoders P. Zhang et al. https://doi.org/10.1109/TGRS.2023.3339682
- AI-Enhanced Rainfall Retrieval Using Commercial Microwave Links in 6G-IoT Networks: Advances, Challenges, and Opportunities M. Wang et al. https://doi.org/10.1109/JIOT.2025.3593645
- Quality control of rain gauge measurements using telecommunication microwave links B. Bianchi et al. https://doi.org/10.1016/j.jhydrol.2013.03.042
- Capitalizing on Cellular Technology—Opportunities and Challenges for Near Ground Weather Monitoring H. Messer https://doi.org/10.3390/environments5070073
- Real-Time Rainfall Estimation Using Microwave Links: A Case Study in East China during the Plum Rain Season in 2020 K. Song et al. https://doi.org/10.3390/s21030858
- Precipitation Monitoring Using Commercial Microwave Links: Current Status, Challenges and Prospectives P. Zhang et al. https://doi.org/10.3390/rs15194821
- Rainfall measurement from the opportunistic use of an Earth–space link in the Ku band L. Barthès & C. Mallet https://doi.org/10.5194/amt-6-2181-2013
- Atmospheric observations with E-band microwave links – challenges and opportunities M. Fencl et al. https://doi.org/10.5194/amt-13-6559-2020
- Enhanced rainfall detection with deep learning: Leveraging cross-domain features from microwave links attenuation time series B. Lian et al. https://doi.org/10.1016/j.jnca.2026.104587
- Extension of the MFLRT to Detect an Unknown Deterministic Signal Using Multiple Sensors, Applied for Precipitation Detection O. Harel & H. Messer https://doi.org/10.1109/LSP.2013.2273455
- Real-time data acquisition of commercial microwave link networks for hydrometeorological applications C. Chwala et al. https://doi.org/10.5194/amt-9-991-2016
- Gauge-adjusted rainfall estimates from commercial microwave links M. Fencl et al. https://doi.org/10.5194/hess-21-617-2017
- Rain event detection in commercial microwave link attenuation data using convolutional neural networks J. Polz et al. https://doi.org/10.5194/amt-13-3835-2020
- The effect of differences between rainfall measurement techniques on groundwater and discharge simulations in a lowland catchment C. Brauer et al. https://doi.org/10.1002/hyp.10898
- Simulation of yearly rainfall time series at microscale resolution with actual properties: Intermittency, scale invariance, and rainfall distribution N. Akrour et al. https://doi.org/10.1002/2014WR016357
- Hydrometeor Identification Using Multiple-Frequency Microwave Links: A Numerical Simulation K. Pu et al. https://doi.org/10.3390/rs12132158
- A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks B. Lian et al. https://doi.org/10.3390/s22124395
- 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
- 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
- The potential of cellular network infrastructures for sudden rainfall monitoring in dry climate regions N. David et al. https://doi.org/10.1016/j.atmosres.2013.01.004
- Combining MWL and MSG SEVIRI Satellite Signals for Rainfall Detection and Estimation K. Kumah et al. https://doi.org/10.3390/atmos11090884
- Have you ever seen the rain? Observing a record convective rainfall with national and local monitoring networks and opportunistic sensors L. Petersson Wårdh et al. https://doi.org/10.5194/amt-19-461-2026
- Retrieval algorithm for rainfall mapping from microwave links in a cellular communication network A. Overeem et al. https://doi.org/10.5194/amt-9-2425-2016
- Rainfall measurement using cell phone links: classification of wet and dry periods using geostationary satellites T. van het Schip et al. https://doi.org/10.1080/02626667.2017.1329588
- 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. https://doi.org/10.5194/essd-13-4219-2021
- The Feasibility Analysis of Cellphone Signal to Detect the Rain: Experimental Study K. Song et al. https://doi.org/10.1109/LGRS.2019.2940854
- 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
- Cellular Network Infrastructure: The Future of Fog Monitoring? N. David et al. https://doi.org/10.1175/BAMS-D-13-00292.1
- A measurement campaign to assess sources of error in microwave link rainfall estimation T. van Leth et al. https://doi.org/10.5194/amt-11-4645-2018
- Application of commercial microwave links (CMLs) attenuation for quantitative estimation of precipitation M. Pasierb et al. https://doi.org/10.1002/met.2218
- Intensity estimation after detection for accumulated rainfall estimation T. Weiss et al. https://doi.org/10.3389/frsip.2024.1291878
- Rainfall Retrieval from Challenging 7 GHz Commercial Microwave Links in Burkina Faso Using a Deep Learning Wet-Dry Classification Framework M. Djibo et al. https://doi.org/10.4236/jst.2026.163004
- Opportunistic remote sensing of rainfall using microwave links from cellular communication networks R. Uijlenhoet et al. https://doi.org/10.1002/wat2.1289
- CNN–GRU-Based High-Resolution Rainfall Measurement Using Download Signals of 5G Cellular Network Y. Mao et al. https://doi.org/10.1109/TIM.2026.3704239
- 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
- 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
- Tropical rainfall monitoring with commercial microwave links in Sri Lanka A. Overeem et al. https://doi.org/10.1088/1748-9326/ac0fa6
- Rainfall Estimation Accuracy of a Nationwide Instantaneously Sampling Commercial Microwave Link Network: Error Dependency on Known Characteristics L. de Vos et al. https://doi.org/10.1175/JTECH-D-18-0197.1
- 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
- The Potential of Microwave Communication Networks to Detect Dew—Experimental Study O. Harel et al. https://doi.org/10.1109/JSTARS.2015.2465909
- Rain Discrimination with Machine Learning Classifiers for Opportunistic Rain Detection System Using Satellite Micro-Wave Links C. Gianoglio et al. https://doi.org/10.3390/s23031202
- Wireless Telecommunication Links for Rainfall Monitoring: Deep Learning Approach and Experimental Results F. Diba et al. https://doi.org/10.1109/ACCESS.2021.3076781
- 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
45 citations as recorded by crossref.
- Systematic Literature Review With Bibliometric Analysis on Markov Switching Model: Methods and Applications S. Phoong et al. https://doi.org/10.1177/21582440221093062
- 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. https://doi.org/10.5194/hess-28-5163-2024
- Classification of Dry and Wet Periods Using Commercial Microwave Links: A One-Class Classification Machine Learning Approach Based on Autoencoders P. Zhang et al. https://doi.org/10.1109/TGRS.2023.3339682
- AI-Enhanced Rainfall Retrieval Using Commercial Microwave Links in 6G-IoT Networks: Advances, Challenges, and Opportunities M. Wang et al. https://doi.org/10.1109/JIOT.2025.3593645
- Quality control of rain gauge measurements using telecommunication microwave links B. Bianchi et al. https://doi.org/10.1016/j.jhydrol.2013.03.042
- Capitalizing on Cellular Technology—Opportunities and Challenges for Near Ground Weather Monitoring H. Messer https://doi.org/10.3390/environments5070073
- Real-Time Rainfall Estimation Using Microwave Links: A Case Study in East China during the Plum Rain Season in 2020 K. Song et al. https://doi.org/10.3390/s21030858
- Precipitation Monitoring Using Commercial Microwave Links: Current Status, Challenges and Prospectives P. Zhang et al. https://doi.org/10.3390/rs15194821
- Rainfall measurement from the opportunistic use of an Earth–space link in the Ku band L. Barthès & C. Mallet https://doi.org/10.5194/amt-6-2181-2013
- Atmospheric observations with E-band microwave links – challenges and opportunities M. Fencl et al. https://doi.org/10.5194/amt-13-6559-2020
- Enhanced rainfall detection with deep learning: Leveraging cross-domain features from microwave links attenuation time series B. Lian et al. https://doi.org/10.1016/j.jnca.2026.104587
- Extension of the MFLRT to Detect an Unknown Deterministic Signal Using Multiple Sensors, Applied for Precipitation Detection O. Harel & H. Messer https://doi.org/10.1109/LSP.2013.2273455
- Real-time data acquisition of commercial microwave link networks for hydrometeorological applications C. Chwala et al. https://doi.org/10.5194/amt-9-991-2016
- Gauge-adjusted rainfall estimates from commercial microwave links M. Fencl et al. https://doi.org/10.5194/hess-21-617-2017
- Rain event detection in commercial microwave link attenuation data using convolutional neural networks J. Polz et al. https://doi.org/10.5194/amt-13-3835-2020
- The effect of differences between rainfall measurement techniques on groundwater and discharge simulations in a lowland catchment C. Brauer et al. https://doi.org/10.1002/hyp.10898
- Simulation of yearly rainfall time series at microscale resolution with actual properties: Intermittency, scale invariance, and rainfall distribution N. Akrour et al. https://doi.org/10.1002/2014WR016357
- Hydrometeor Identification Using Multiple-Frequency Microwave Links: A Numerical Simulation K. Pu et al. https://doi.org/10.3390/rs12132158
- A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks B. Lian et al. https://doi.org/10.3390/s22124395
- 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
- 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
- The potential of cellular network infrastructures for sudden rainfall monitoring in dry climate regions N. David et al. https://doi.org/10.1016/j.atmosres.2013.01.004
- Combining MWL and MSG SEVIRI Satellite Signals for Rainfall Detection and Estimation K. Kumah et al. https://doi.org/10.3390/atmos11090884
- Have you ever seen the rain? Observing a record convective rainfall with national and local monitoring networks and opportunistic sensors L. Petersson Wårdh et al. https://doi.org/10.5194/amt-19-461-2026
- Retrieval algorithm for rainfall mapping from microwave links in a cellular communication network A. Overeem et al. https://doi.org/10.5194/amt-9-2425-2016
- Rainfall measurement using cell phone links: classification of wet and dry periods using geostationary satellites T. van het Schip et al. https://doi.org/10.1080/02626667.2017.1329588
- 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. https://doi.org/10.5194/essd-13-4219-2021
- The Feasibility Analysis of Cellphone Signal to Detect the Rain: Experimental Study K. Song et al. https://doi.org/10.1109/LGRS.2019.2940854
- 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
- Cellular Network Infrastructure: The Future of Fog Monitoring? N. David et al. https://doi.org/10.1175/BAMS-D-13-00292.1
- A measurement campaign to assess sources of error in microwave link rainfall estimation T. van Leth et al. https://doi.org/10.5194/amt-11-4645-2018
- Application of commercial microwave links (CMLs) attenuation for quantitative estimation of precipitation M. Pasierb et al. https://doi.org/10.1002/met.2218
- Intensity estimation after detection for accumulated rainfall estimation T. Weiss et al. https://doi.org/10.3389/frsip.2024.1291878
- Rainfall Retrieval from Challenging 7 GHz Commercial Microwave Links in Burkina Faso Using a Deep Learning Wet-Dry Classification Framework M. Djibo et al. https://doi.org/10.4236/jst.2026.163004
- Opportunistic remote sensing of rainfall using microwave links from cellular communication networks R. Uijlenhoet et al. https://doi.org/10.1002/wat2.1289
- CNN–GRU-Based High-Resolution Rainfall Measurement Using Download Signals of 5G Cellular Network Y. Mao et al. https://doi.org/10.1109/TIM.2026.3704239
- 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
- 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
- Tropical rainfall monitoring with commercial microwave links in Sri Lanka A. Overeem et al. https://doi.org/10.1088/1748-9326/ac0fa6
- Rainfall Estimation Accuracy of a Nationwide Instantaneously Sampling Commercial Microwave Link Network: Error Dependency on Known Characteristics L. de Vos et al. https://doi.org/10.1175/JTECH-D-18-0197.1
- 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
- The Potential of Microwave Communication Networks to Detect Dew—Experimental Study O. Harel et al. https://doi.org/10.1109/JSTARS.2015.2465909
- Rain Discrimination with Machine Learning Classifiers for Opportunistic Rain Detection System Using Satellite Micro-Wave Links C. Gianoglio et al. https://doi.org/10.3390/s23031202
- Wireless Telecommunication Links for Rainfall Monitoring: Deep Learning Approach and Experimental Results F. Diba et al. https://doi.org/10.1109/ACCESS.2021.3076781
- 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
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