Articles | Volume 18, issue 10
https://doi.org/10.5194/amt-18-2279-2025
© Author(s) 2025. 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-18-2279-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
27 May 2025
Research article |
| 27 May 2025
| 27 May 2025
Combining commercial microwave links and weather radar for classification of dry snow and rainfall
Erlend Øydvin
CORRESPONDING AUTHOR
Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
Renaud Gaban
Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
Norwegian Meteorological Institute, Oslo, Norway
Jafet Andersson
Swedish Meteorological and Hydrological Institute (SMHI), Norrköping, Sweden
Remco (C. Z.) van de Beek
Swedish Meteorological and Hydrological Institute (SMHI), Norrköping, Sweden
Mareile Astrid Wolff
Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
Norwegian Meteorological Institute, Oslo, Norway
Nils-Otto Kitterød
Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, Ås, Norway
Christian Chwala
Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Campus Alpin, Garmisch-Partenkirchen, Germany
Vegard Nilsen
Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
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Two simple neural networks are trained to detect rainfall events using signal loss from commercial microwave links. Whereas existing rainfall event detection methods have focused on hourly resolution reference data, this study uses weather radar and rain gauges with 5 min and 1 min temporal resolutions, respectively. Our results show that the developed neural networks can detect rainfall events with a higher temporal precision than existing methods.
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A hand sprayer is used to deposit droplets on the radome of an antenna. Before and during the drying process, measurements of the antenna reflection coefficient are performed repeatedly over time and frequency using a vector network analyzer. Continuous drifts from the wet state back to the initial dry state are demonstrated for individual antennas and frequency ranges. The obtained insights qualify the antenna reflection coefficient to be a promising indicator of momentary radome wetness.
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Two simple neural networks are trained to detect rainfall events using signal loss from commercial microwave links. Whereas existing rainfall event detection methods have focused on hourly resolution reference data, this study uses weather radar and rain gauges with 5 min and 1 min temporal resolutions, respectively. Our results show that the developed neural networks can detect rainfall events with a higher temporal precision than existing methods.
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Commercial microwave links (CMLs) can be used for rainfall retrieval. The detection of rainy periods in their attenuation time series is a crucial processing step. We investigate the usage of rainfall data from MSG SEVIRI for this task, compare this approach with existing methods, and introduce a novel combined approach. The results show certain advantages for SEVIRI-based methods, particularly for CMLs where existing methods perform poorly. Our novel combination yields the best performance.
Jafet C. M. Andersson, Jonas Olsson, Remco (C. Z.) van de Beek, and Jonas Hansryd
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This article presents data from three types of sensors for rain measurement, i.e. commercial microwave links (CMLs), gauges, and weather radar. Access to CML data is typically restricted, which limits research and applications. We openly share a large CML database (364 CMLs at 10 s resolution with true coordinates), along with 11 gauges and one radar composite. This opens up new opportunities to study CMLs, to benchmark algorithms, and to investigate how multiple sensors can best be combined.
Judit Lienert, Jafet C. M. Andersson, Daniel Hofmann, Francisco Silva Pinto, and Martijn Kuller
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Many western Africans encounter serious floods every year. The FANFAR project co-designed a pre-operational flood forecasting system (FEWS) with 50 key western African stakeholders. Participatory multi-criteria decision analysis (MCDA) helped prioritize a FEWS that meets their needs: it should provide accurate, clear, and timely flood risk information and work reliably in tough conditions. As a theoretical contribution, we propose an assessment framework for transdisciplinary hydrology research.
Jonas Olsson, Peter Berg, and Remco van de Beek
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We have developed a tool to visualize rainfall observations, based on a combination of meteorological stations and weather radars, over Sweden in near real-time. By accumulating the rainfall in time (1–12 h) and space (hydrological basins), the tool is designed mainly for hydrological applications, e.g. to support flood forecasters and to facilitate post-event analyses. Despite evident uncertainties, different users have confirmed an added value of the tool in case studies.
Judit Lienert, Jafet Andersson, Daniel Hofmann, Francisco Silva Pinto, and Martijn Kuller
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-177, https://doi.org/10.5194/hess-2021-177, 2021
Manuscript not accepted for further review
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West Africa faces serious floods, affecting millions of people every year. The FANFAR project co-designed a flood forecasting and warning system at lively workshops together with 50–60 key West African stakeholders. We prioritized FANFAR system configurations that best meet stakeholders’ needs and expectations. Stakeholders preferred a system producing accurate, clear, and accessible flood risk information, which works reliably under difficult West African conditions.
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Short summary
We present a novel method for classifying rain and snow by combining data from commercial microwave links (CMLs) with weather radar. We compare this to a reference method using dew point temperature for precipitation type classification. Evaluations with nearby disdrometers show that CMLs improve the classification of dry snow and rainfall, outperforming the reference method.
We present a novel method for classifying rain and snow by combining data from commercial...
