Articles | Volume 30, issue 21
https://doi.org/10.5194/hess-30-7007-2026
© Author(s) 2026. 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-30-7007-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Nov 2026
Research article |
| 13 Nov 2026
| 13 Nov 2026
Improving weather radar rainfall estimates by merging with commercial microwave link data: a fully reproducible, large-scale method intercomparison
Erlend Øydvin
Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
Elia Covi
Hydro-Meteo-Climate Structure, Arpae Emilia-Romagna, Bologna, Italy
Maximilian Graf
Department of Hydrometeorology, Deutscher Wetterdienst, Offenbach, Germany
Christian Chwala
CORRESPONDING AUTHOR
Institute of Meteorology and Climate Research (IMK-IFU), Karlsruhe Institute of Technology, Garmisch-Partenkirchen, Germany
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Data from so called opportunistic sensors, devices that have not been designed to provide reliable weather data, can offer valuable rainfall information. But good data processing is crucial. For this task, we created the OpenSense software ecosystem, featuring new specialized tools built on a shared foundation. These new tools allow us to collaboratively advance research and improve the operational usage of opportunistic rainfall data to provide more accurate rainfall maps.
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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.
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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.
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Short summary
We developed an open-source tool to combine weather radar and commercial microwave link data, tested on large, public datasets. Merging these measurements produced more accurate rainfall estimates than radar alone, and we show which methods work best in different situations to support improved rainfall monitoring and applications.
We developed an open-source tool to combine weather radar and commercial microwave link data,...
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