Articles | Volume 17, issue 7
https://doi.org/10.5194/amt-17-2165-2024
© Author(s) 2024. 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-17-2165-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Improved rain event detection in commercial microwave link time series via combination with MSG SEVIRI data
Maximilian Graf
Institute of Geography (IGUA), University of Augsburg, Alter Postweg 118, 86159 Augsburg, Germany
Andreas Wagner
Institute of Geography (IGUA), University of Augsburg, Alter Postweg 118, 86159 Augsburg, Germany
Julius Polz
Campus Alpin (IMK-IFU), Karlsruhe Institute of Technology, Kreuzeckbahnstraße 19, 82467 Garmisch-Partenkirchen, Germany
Llorenç Lliso
Agencia Estatal de Meteorología (AEMET Spain), Leonardo Prieto Castro 8, 28040 Madrid, Spain
José Alberto Lahuerta
Agencia Estatal de Meteorología (AEMET Spain), Leonardo Prieto Castro 8, 28040 Madrid, Spain
Harald Kunstmann
Institute of Geography (IGUA), University of Augsburg, Alter Postweg 118, 86159 Augsburg, Germany
Campus Alpin (IMK-IFU), Karlsruhe Institute of Technology, Kreuzeckbahnstraße 19, 82467 Garmisch-Partenkirchen, Germany
Christian Chwala
CORRESPONDING AUTHOR
Campus Alpin (IMK-IFU), Karlsruhe Institute of Technology, Kreuzeckbahnstraße 19, 82467 Garmisch-Partenkirchen, Germany
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Christof Lorenz, Tanja C. Portele, Patrick Laux, and Harald Kunstmann
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Semi-arid regions depend on the freshwater resources from the rainy seasons as they are crucial for ensuring security for drinking water, food and electricity. Thus, forecasting the conditions for the next season is crucial for proactive water management. We hence present a seasonal forecast product for four semi-arid domains in Iran, Brazil, Sudan/Ethiopia and Ecuador/Peru. It provides a benchmark for seasonal forecasts and, finally, a crucial contribution for improved disaster preparedness.
Benjamin Fersch, Till Francke, Maik Heistermann, Martin Schrön, Veronika Döpper, Jannis Jakobi, Gabriele Baroni, Theresa Blume, Heye Bogena, Christian Budach, Tobias Gränzig, Michael Förster, Andreas Güntner, Harrie-Jan Hendricks Franssen, Mandy Kasner, Markus Köhli, Birgit Kleinschmit, Harald Kunstmann, Amol Patil, Daniel Rasche, Lena Scheiffele, Ulrich Schmidt, Sandra Szulc-Seyfried, Jannis Weimar, Steffen Zacharias, Marek Zreda, Bernd Heber, Ralf Kiese, Vladimir Mares, Hannes Mollenhauer, Ingo Völksch, and Sascha Oswald
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Short summary
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.
Commercial microwave links (CMLs) can be used for rainfall retrieval. The detection of rainy...