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.
Research article
| 
17 Apr 2024
Research article |
| 17 Apr 2024
| 17 Apr 2024
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
Related authors
Christian Chwala, Aart Overeem, Erlend Øydvin, Louise Petersson Wårdh, Jochen Seidel, Maximilian Graf, Bas Walraven, Elia Covi, Hai Victor Habi, Martin Fencl, Lotte de Vos, Filippo Giannetti, Amy Green, Tess O’Hara, Nico Blettner, Tom Keel, Georges Schutz, Abbas El Hachem, Nicholas Illich, Julius Polz, Taoufiq Shit, Lukáš Kaleta, Damaris Zulkarnaen, and Vojtěch Bareš
EGUsphere, https://doi.org/10.5194/egusphere-2025-5438, https://doi.org/10.5194/egusphere-2025-5438, 2026
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
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.
Erlend Øydvin, Elia Covi, Maximilian Graf, and Christian Chwala
EGUsphere, https://doi.org/10.5194/egusphere-2025-6371, https://doi.org/10.5194/egusphere-2025-6371, 2025
Short summary
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.
Roberto Nebuloni, Maximilian Graf, Greta Cazzaniga, François Mercier, and Maxime Turko
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-537, https://doi.org/10.5194/essd-2025-537, 2025
Preprint under review for ESSD
Short summary
Short summary
We describe the OpenSat4Weather public dataset, which features time series of received power from 215 TV-SAT satellite microwave links located in Southern France over a five-month observation period and at 1-min resolution. These data can be processed to gather indirect rainfall intensity measurements. OpenSat4Weather also includes concurrent data from operational rain gauges and weather radars, and ERA5 0°C isotherm height data, to derive the wet fraction of the satellite-to-ground path.
Erlend Øydvin, Maximilian Graf, Christian Chwala, Mareile Astrid Wolff, Nils-Otto Kitterød, and Vegard Nilsen
Hydrol. Earth Syst. Sci., 28, 5163–5171, https://doi.org/10.5194/hess-28-5163-2024, https://doi.org/10.5194/hess-28-5163-2024, 2024
Short summary
Short summary
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.
Christian Chwala, Aart Overeem, Erlend Øydvin, Louise Petersson Wårdh, Jochen Seidel, Maximilian Graf, Bas Walraven, Elia Covi, Hai Victor Habi, Martin Fencl, Lotte de Vos, Filippo Giannetti, Amy Green, Tess O’Hara, Nico Blettner, Tom Keel, Georges Schutz, Abbas El Hachem, Nicholas Illich, Julius Polz, Taoufiq Shit, Lukáš Kaleta, Damaris Zulkarnaen, and Vojtěch Bareš
EGUsphere, https://doi.org/10.5194/egusphere-2025-5438, https://doi.org/10.5194/egusphere-2025-5438, 2026
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
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.
Yining Zang, Pauline C. Treble, Kei Yoshimura, Jayson Gabriel Pinza, Fengbo Zhang, Kübra Özdemir Çallı, Xiaojun Mei, Admin Husic, Alena Gessert, Andrej Stroj, Bartolomé Andreo, Bernard Ladouche, Christine Stumpp, Diana Mance, Eleni Zagana, Fen Huang, Giuseppe Sappa, Harald Kunstmann, Heike Brielmann, Hong Zhou, Huaying Wu, Jakob Garvelmann, James Berglund, Jean-Baptiste Charlier, Jens Lange, Juan Antonio Barberá Fornell, Junbing Pu, Konstantina Katsanou, Kun Ren, Laura Toran, Laurence Gill, Maria Filippini, Martin Kralik, Matías Mudarra Martínez, Min Zhao, Mingming Luo, Nico Goldscheider, Nikolaos Lambrakis, Pantaleone De Vita, Qiong Xiao, Shi Yu, Silvia Iacurto, Silvio Coda, Ted McCormack, Vincenzo Allocca, W. George Darling, Walter D’Alessandro, Xulei Guo, Yundi Hu, Zhijun Wang, Eva Kaminsky, Jiří Faimon, Marek Lang, Pavel Pracný, and Andreas Hartmann
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-812, https://doi.org/10.5194/essd-2025-812, 2026
Preprint under review for ESSD
Short summary
Short summary
We developed the first global database of water from karst springs and cave drips that records different forms of oxygen and hydrogen, which naturally trace how rainwater moves through rocks. By gathering and checking thousands of measurements from around the globe and linking them with flow and rainfall data, the database provides a comprehensive view of water movement, allows scientists to compare regions, understand groundwater processes, and support sustainable water management worldwide.
Erlend Øydvin, Elia Covi, Maximilian Graf, and Christian Chwala
EGUsphere, https://doi.org/10.5194/egusphere-2025-6371, https://doi.org/10.5194/egusphere-2025-6371, 2025
Short summary
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.
Roberto Nebuloni, Maximilian Graf, Greta Cazzaniga, François Mercier, and Maxime Turko
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-537, https://doi.org/10.5194/essd-2025-537, 2025
Preprint under review for ESSD
Short summary
Short summary
We describe the OpenSat4Weather public dataset, which features time series of received power from 215 TV-SAT satellite microwave links located in Southern France over a five-month observation period and at 1-min resolution. These data can be processed to gather indirect rainfall intensity measurements. OpenSat4Weather also includes concurrent data from operational rain gauges and weather radars, and ERA5 0°C isotherm height data, to derive the wet fraction of the satellite-to-ground path.
Ling Zhang, Lu Li, Zhongshi Zhang, Joël Arnault, Stefan Sobolowski, Xiaoling Chen, Jianzhong Lu, Anthony Musili Mwanthi, Pratik Kad, Mohammed Abdullahi Hassan, Tanja Portele, Harald Kunstmann, and Zhengkang Zuo
Hydrol. Earth Syst. Sci., 29, 4109–4132, https://doi.org/10.5194/hess-29-4109-2025, https://doi.org/10.5194/hess-29-4109-2025, 2025
Short summary
Short summary
To address challenges related to unreliable hydrological simulations, we present an enhanced hydrological simulation with a refined climate model and a more comprehensive hydrological model. The model with the two parts outperforms that without, especially in migrating bias in peak flow and dry-season flow. Our findings highlight the enhanced hydrological simulation capability, with the refined climate and lake module contributing 24 % and 76 % improvement, respectively.
Yinchi Zhang, Wanling Xu, Chao Deng, Shao Sun, Miaomiao Ma, Jianhui Wei, Ying Chen, Harald Kunstmann, and Lu Gao
EGUsphere, https://doi.org/10.5194/egusphere-2025-2438, https://doi.org/10.5194/egusphere-2025-2438, 2025
Short summary
Short summary
Most studies of compound extremes assume stable climate conditions. We use high-resolution regional climate modeling and non-stationary statistical methods to assess future changes in southeastern China. Our results show that non-stationary models better capture shifts in the risk of compound extremes, highlighting that traditional methods may underestimate future threats.
Erlend Øydvin, Renaud Gaban, Jafet Andersson, Remco (C. Z.) van de Beek, Mareile Astrid Wolff, Nils-Otto Kitterød, Christian Chwala, and Vegard Nilsen
Atmos. Meas. Tech., 18, 2279–2293, https://doi.org/10.5194/amt-18-2279-2025, https://doi.org/10.5194/amt-18-2279-2025, 2025
Short summary
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.
Ningpeng Dong, Haoran Hao, Mingxiang Yang, Jianhui Wei, Shiqin Xu, and Harald Kunstmann
Hydrol. Earth Syst. Sci., 29, 2023–2042, https://doi.org/10.5194/hess-29-2023-2025, https://doi.org/10.5194/hess-29-2023-2025, 2025
Short summary
Short summary
Hydrometeorological forecasting is crucial for managing water resources and mitigating extreme weather events, yet current long-term forecast products are often embedded with uncertainties. We develop a deep-learning-based modelling framework to improve 30 d rainfall and streamflow forecasts by combining advanced neural networks and physical models. With the flow forecast error reduced by up to 33 %, the framework has the potential to enhance water management and disaster prevention.
Jonas Tiede, Christian Chwala, Uwe Siart, and Thomas F. Eibert
Adv. Radio Sci., 23, 13–19, https://doi.org/10.5194/ars-23-13-2025, https://doi.org/10.5194/ars-23-13-2025, 2025
Short summary
Short summary
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.
Erlend Øydvin, Maximilian Graf, Christian Chwala, Mareile Astrid Wolff, Nils-Otto Kitterød, and Vegard Nilsen
Hydrol. Earth Syst. Sci., 28, 5163–5171, https://doi.org/10.5194/hess-28-5163-2024, https://doi.org/10.5194/hess-28-5163-2024, 2024
Short summary
Short summary
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.
Patrick Olschewski, Qi Sun, Jianhui Wei, Yu Li, Zhan Tian, Laixiang Sun, Joël Arnault, Tanja C. Schober, Brian Böker, Harald Kunstmann, and Patrick Laux
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-95, https://doi.org/10.5194/hess-2024-95, 2024
Revised manuscript not accepted
Short summary
Short summary
There are indications that typhoon intensities may increase under global warming. However, further research on these projections and their uncertainties is necessary. We study changes in typhoon intensity under SSP5-8.5 for seven events affecting the Pearl River Delta using Pseudo-Global Warming and a storyline approach based on 16 CMIP6 models. Results show intensified wind speed, sea level pressure drop and precipitation levels for six events with amplified increases for individual storylines.
Patrick Olschewski, Mame Diarra Bousso Dieng, Hassane Moutahir, Brian Böker, Edwin Haas, Harald Kunstmann, and Patrick Laux
Nat. Hazards Earth Syst. Sci., 24, 1099–1134, https://doi.org/10.5194/nhess-24-1099-2024, https://doi.org/10.5194/nhess-24-1099-2024, 2024
Short summary
Short summary
We applied a multivariate and dependency-preserving bias correction method to climate model output for the Greater Mediterranean Region and investigated potential changes in false-spring events (FSEs) and heat–drought compound events (HDCEs). Results project an increase in the frequency of FSEs in middle and late spring as well as increases in frequency, intensity, and duration for HDCEs. This will potentially aggravate the risk of crop loss and failure and negatively impact food security.
Qi Sun, Patrick Olschewski, Jianhui Wei, Zhan Tian, Laixiang Sun, Harald Kunstmann, and Patrick Laux
Hydrol. Earth Syst. Sci., 28, 761–780, https://doi.org/10.5194/hess-28-761-2024, https://doi.org/10.5194/hess-28-761-2024, 2024
Short summary
Short summary
Tropical cyclones (TCs) often cause high economic loss due to heavy winds and rainfall, particularly in densely populated regions such as the Pearl River Delta (China). This study provides a reference to set up regional climate models for TC simulations. They contribute to a better TC process understanding and assess the potential changes and risks of TCs in the future. This lays the foundation for hydrodynamical modelling, from which the cities' disaster management and defence could benefit.
Dragan Petrovic, Benjamin Fersch, and Harald Kunstmann
Nat. Hazards Earth Syst. Sci., 24, 265–289, https://doi.org/10.5194/nhess-24-265-2024, https://doi.org/10.5194/nhess-24-265-2024, 2024
Short summary
Short summary
The influence of model resolution and settings on the reproduction of heat waves in Germany between 1980–2009 is analyzed. Outputs from a high-resolution model with settings tailored to the target region are compared to those from coarser-resolution models with more general settings. Neither the increased resolution nor the tailored model settings are found to add significant value to the heat wave simulation. The models exhibit a large spread, indicating that the choice of model can be crucial.
Mohsen Soltani, Bert Hamelers, Abbas Mofidi, Christopher G. Fletcher, Arie Staal, Stefan C. Dekker, Patrick Laux, Joel Arnault, Harald Kunstmann, Ties van der Hoeven, and Maarten Lanters
Earth Syst. Dynam., 14, 931–953, https://doi.org/10.5194/esd-14-931-2023, https://doi.org/10.5194/esd-14-931-2023, 2023
Short summary
Short summary
The temporal changes and spatial patterns in precipitation events do not show a homogeneous tendency across the Sinai Peninsula. Mediterranean cyclones accompanied by the Red Sea and Persian troughs are responsible for the majority of Sinai's extreme rainfall events. Cyclone tracking captures 156 cyclones (rainfall ≥10 mm d-1) either formed within or transferred to the Mediterranean basin precipitating over Sinai.
Efi Rousi, Andreas H. Fink, Lauren S. Andersen, Florian N. Becker, Goratz Beobide-Arsuaga, Marcus Breil, Giacomo Cozzi, Jens Heinke, Lisa Jach, Deborah Niermann, Dragan Petrovic, Andy Richling, Johannes Riebold, Stella Steidl, Laura Suarez-Gutierrez, Jordis S. Tradowsky, Dim Coumou, André Düsterhus, Florian Ellsäßer, Georgios Fragkoulidis, Daniel Gliksman, Dörthe Handorf, Karsten Haustein, Kai Kornhuber, Harald Kunstmann, Joaquim G. Pinto, Kirsten Warrach-Sagi, and Elena Xoplaki
Nat. Hazards Earth Syst. Sci., 23, 1699–1718, https://doi.org/10.5194/nhess-23-1699-2023, https://doi.org/10.5194/nhess-23-1699-2023, 2023
Short summary
Short summary
The objective of this study was to perform a comprehensive, multi-faceted analysis of the 2018 extreme summer in terms of heat and drought in central and northern Europe, with a particular focus on Germany. A combination of favorable large-scale conditions and locally dry soils were related with the intensity and persistence of the events. We also showed that such extremes have become more likely due to anthropogenic climate change and might occur almost every year under +2 °C of global warming.
Dragan Petrovic, Benjamin Fersch, and Harald Kunstmann
Nat. Hazards Earth Syst. Sci., 22, 3875–3895, https://doi.org/10.5194/nhess-22-3875-2022, https://doi.org/10.5194/nhess-22-3875-2022, 2022
Short summary
Short summary
The influence of model resolution and settings on drought reproduction in Germany between 1980–2009 is investigated here. Outputs from a high-resolution model with settings tailored to the target region are compared to those from coarser-resolution models with more general settings. Gridded observational data sets serve as reference. Regarding the reproduction of drought characteristics, all models perform on a similar level, while for trends, only the modified model produces reliable outputs.
Benjamin Fersch, Andreas Wagner, Bettina Kamm, Endrit Shehaj, Andreas Schenk, Peng Yuan, Alain Geiger, Gregor Moeller, Bernhard Heck, Stefan Hinz, Hansjörg Kutterer, and Harald Kunstmann
Earth Syst. Sci. Data, 14, 5287–5307, https://doi.org/10.5194/essd-14-5287-2022, https://doi.org/10.5194/essd-14-5287-2022, 2022
Short summary
Short summary
In this study, a comprehensive multi-disciplinary dataset for tropospheric water vapor was developed. Geodetic, photogrammetric, and atmospheric modeling and data fusion techniques were used to obtain maps of water vapor in a high spatial and temporal resolution. It could be shown that regional weather simulations for different seasons benefit from assimilating these maps and that the combination of the different observation techniques led to positive synergies.
Heye Reemt Bogena, Martin Schrön, Jannis Jakobi, Patrizia Ney, Steffen Zacharias, Mie Andreasen, Roland Baatz, David Boorman, Mustafa Berk Duygu, Miguel Angel Eguibar-Galán, Benjamin Fersch, Till Franke, Josie Geris, María González Sanchis, Yann Kerr, Tobias Korf, Zalalem Mengistu, Arnaud Mialon, Paolo Nasta, Jerzy Nitychoruk, Vassilios Pisinaras, Daniel Rasche, Rafael Rosolem, Hami Said, Paul Schattan, Marek Zreda, Stefan Achleitner, Eduardo Albentosa-Hernández, Zuhal Akyürek, Theresa Blume, Antonio del Campo, Davide Canone, Katya Dimitrova-Petrova, John G. Evans, Stefano Ferraris, Félix Frances, Davide Gisolo, Andreas Güntner, Frank Herrmann, Joost Iwema, Karsten H. Jensen, Harald Kunstmann, Antonio Lidón, Majken Caroline Looms, Sascha Oswald, Andreas Panagopoulos, Amol Patil, Daniel Power, Corinna Rebmann, Nunzio Romano, Lena Scheiffele, Sonia Seneviratne, Georg Weltin, and Harry Vereecken
Earth Syst. Sci. Data, 14, 1125–1151, https://doi.org/10.5194/essd-14-1125-2022, https://doi.org/10.5194/essd-14-1125-2022, 2022
Short summary
Short summary
Monitoring of increasingly frequent droughts is a prerequisite for climate adaptation strategies. This data paper presents long-term soil moisture measurements recorded by 66 cosmic-ray neutron sensors (CRNS) operated by 24 institutions and distributed across major climate zones in Europe. Data processing followed harmonized protocols and state-of-the-art methods to generate consistent and comparable soil moisture products and to facilitate continental-scale analysis of hydrological extremes.
Bernd Schalge, Gabriele Baroni, Barbara Haese, Daniel Erdal, Gernot Geppert, Pablo Saavedra, Vincent Haefliger, Harry Vereecken, Sabine Attinger, Harald Kunstmann, Olaf A. Cirpka, Felix Ament, Stefan Kollet, Insa Neuweiler, Harrie-Jan Hendricks Franssen, and Clemens Simmer
Earth Syst. Sci. Data, 13, 4437–4464, https://doi.org/10.5194/essd-13-4437-2021, https://doi.org/10.5194/essd-13-4437-2021, 2021
Short summary
Short summary
In this study, a 9-year simulation of complete model output of a coupled atmosphere–land-surface–subsurface model on the catchment scale is discussed. We used the Neckar catchment in SW Germany as the basis of this simulation. Since the dataset includes the full model output, it is not only possible to investigate model behavior and interactions between the component models but also use it as a virtual truth for comparison of, for example, data assimilation experiments.
Christof Lorenz, Tanja C. Portele, Patrick Laux, and Harald Kunstmann
Earth Syst. Sci. Data, 13, 2701–2722, https://doi.org/10.5194/essd-13-2701-2021, https://doi.org/10.5194/essd-13-2701-2021, 2021
Short summary
Short summary
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.
Cited articles
Atlas, D.: Radar in Meteorology – Battan Memorial and 40th Anniversary Radar Meteorology Conference, Boston, USA, 9–13 November 1987, Amer. Meteor. Soc., ISBN 0933876866, 1990. a
Atlas, D. and Ulbrich, C. W.: Path- and Area-Integrated Rainfall Measurement by Microwave Attenuation in the 1–3 cm Band, J. Appl. Meteorol. Clim., 16, 1322–1331, https://doi.org/10.1175/1520-0450(1977)016<1322:PAAIRM>2.0.CO;2, 1977. a
Bartels, H., Weigl, E., Reich, T., Lang, P., Wagner, A., Kohler, O., and Gerlach, N.: Projekt RADOLAN – Routineverfahren zur Online-Aneichung der Radarniederschlagsdaten mit Hilfe von automatischen Bodenniederschlagsstationen (Ombrometer), Deutscher Wetterdienst, Hydrometeorologie, https://www.dwd.de/DE/leistungen/radolan/radolan_info/home_abschlussbericht.html (last access: 28 July 2023), 2004. a, b, c
Bruni, G., Reinoso, R., van de Giesen, N. C., Clemens, F. H. L. R., and ten Veldhuis, J. A. E.: On the sensitivity of urban hydrodynamic modelling to rainfall spatial and temporal resolution, Hydrol. Earth Syst. Sci., 19, 691–709, https://doi.org/10.5194/hess-19-691-2015, 2015. a
Chwala, C. and Kunstmann, H.: Commercial microwave link networks for rainfall observation: Assessment of the current status and future challenges, WIREs Water, 6, e1337, https://doi.org/10.1002/wat2.1337, 2019. a
Chwala, C., Gmeiner, A., Qiu, W., Hipp, S., Nienaber, D., Siart, U., Eibert, T., Pohl, M., Seltmann, J., Fritz, J., and Kunstmann, H.: Precipitation observation using microwave backhaul links in the alpine and pre-alpine region of Southern Germany, Hydrol. Earth Syst. Sci., 16, 2647–2661, https://doi.org/10.5194/hess-16-2647-2012, 2012. a
Chwala, C., Keis, F., and Kunstmann, H.: Real-time data acquisition of commercial microwave link networks for hydrometeorological applications, Atmos. Meas. Tech., 9, 991–999, https://doi.org/10.5194/amt-9-991-2016, 2016. a
Cristiano, E., ten Veldhuis, M.-C., and van de Giesen, N.: Spatial and temporal variability of rainfall and their effects on hydrological response in urban areas – a review, Hydrol. Earth Syst. Sci., 21, 3859–3878, https://doi.org/10.5194/hess-21-3859-2017, 2017. a
D'Amico, M., Manzoni, A., and Solazzi, G. L.: Use of Operational Microwave Link Measurements for the Tomographic Reconstruction of 2-D Maps of Accumulated Rainfall, IEEE Geosci. Remote S., 13, 1827–1831, https://doi.org/10.1109/LGRS.2016.2614326, 2016. a
Djibo, M., Chwala, C., Graf, M., Polz, J., Kunstmann, H., and Zougmoré, F.: High-resolution rainfall maps from commercial microwave links for a data-scarce region in West Africa, J. Hydrometeorol., 1, 1847–1861, https://doi.org/10.1175/JHM-D-23-0015.1, 2023. a, b
DWD: Niederschlagsintensität, German Met. Service Glossary, German Meteorological Service, https://www.dwd.de/DE/service/lexikon/Functions/glossar.html?lv2=101812&lv3=101906, (last access: 28 July 2023), 2023a. a
DWD: Index of /climate_environment/CDC/grids_germany/ 5_minutes/radolan/reproc/2017_002/, Deutscher Wetterdienst [data set], https://opendata.dwd.de/climate_environment/CDC/grids_germany/5_minutes/radolan/reproc/2017_002/ (last access: 28 July 2023), 2023b. a
Fu, S., Sonnenborg, T. O., Jensen, K. H., and He, X.: Impact of Precipitation Spatial Resolution on the Hydrological Response of an Integrated Distributed Water Resources Model, Vadose Zone J., 10, 25–36, 2011. a
Giannetti, F., Moretti, M., Reggiannini, R., and Vaccaro, A.: The NEFOCAST System for Detection and Estimation of Rainfall Fields by the Opportunistic Use of Broadcast Satellite Signals, IEEE Aero. El. Sys. Mag., 34, 16–27, https://doi.org/10.1109/MAES.2019.2916292, 2019. a
Giro, R. A., Luini, L., Riva, C. G., Pimienta-del Valle, D., and Riera Salis, J. M.: Real-Time Rainfall Estimation Using Satellite Signals: Development and Assessment of a New Procedure, IEEE T. Instrum. Meas., 71, 1–10, https://doi.org/10.1109/TIM.2022.3165840, 2022. a
Graf, M., El Hachem, A., Eisele, M., Seidel, J., Chwala, C., Kunstmann, H., and Bárdossy, A.: Rainfall estimates from opportunistic sensors in Germany across spatio-temporal scales, J. Hydrol.: Regional Studies, 37, 100883, https://doi.org/10.1016/j.ejrh.2021.100883, 2021. a
Habi, H. V. and Messer, H.: Wet-Dry Classification Using LSTM and Commercial Microwave Links, in: 2018 IEEE 10th Sensor Array and Multichannel Signal Processing Workshop (SAM), 8–11 July 2018, Sheffield, UK, IEEE, 149–153, https://doi.org/10.1109/SAM.2018.8448679, 2018. a
Haese, B., Hörning, S., Chwala, C., Bárdossy, A., Schalge, B., and Kunstmann, H.: Stochastic Reconstruction and Interpolation of Precipitation Fields Using Combined Information of Commercial Microwave Links and Rain Gauges, Water Resour. Res., 53, 10740–10756, https://doi.org/10.1002/2017WR021015, 2017. a
Hernanz, A., Lahuerta García, J. A., Calbet, X., and Rípodas, P.: Algorithm Theoretical Basis Document for the Precipitation Product Processors of the NWC/GEO, NWCSAF, https://www.nwcsaf.org/Downloads/GEO/2021/Documents/Scientific_Docs/NWC-CDOP3-GEO-AEMET-SCI-ATBD-Precipitation_v1.0.1.pdf, (last access: 28 July 2023), 2019. a, b
ITU-R: Specific attenuation model for rain for use in prediction methods (Recommendation P.838-3), ITU-R, https://www.itu.int/rec/R-REC-P.838-3-200503-I/en, (last access: 28 July 2023), 2005. a
Kreklow, J., Tetzlaff, B., Burkhard, B., and Kuhnt, G.: Radar-Based Precipitation Climatology in Germany – Developments, Uncertainties and Potentials, Atmosphere, 11, 217, https://doi.org/10.3390/atmos11020217, 2020. a
Kumah, K., Maathuis, B., Hoedjes, J., and Su, Z.: Near real-time estimation of high spatiotemporal resolution rainfall from cloud top properties of the MSG satellite and commercial microwave link rainfall intensities, Atmos. Res., 279, 106357, https://doi.org/10.1016/j.atmosres.2022.106357, 2022. a
Kumah, K. K., Hoedjes, J. C. B., David, N., Maathuis, B. H. P., Gao, H. O., and Su, B. Z.: The MSG Technique: Improving Commercial Microwave Link Rainfall Intensity by Using Rain Area Detection from Meteosat Second Generation, Remote Sens., 13, 3274, https://doi.org/10.3390/rs13163274, 2021. a, b
Lahuerta García, J. A.: Scientific and Validation Report for the Precipitation Product Processors of the NWC/GEO, Agencia Estatal de Meteorología, https://www.nwcsaf.org/AemetWebContents/ScientificDocumentation/Documentation/GEO/v2016/NWC-CDOP2-GEO-AEMET-SCI-VR-Precipitation_v1.0.pdf, (last access: 28 July 2023), 2019. a
Lahuerta García, J. A.: Algorithm theoretical basis document for the precipitation product processors of the NWC/GEO, Agencia Estatal de Meteorología, https://www.nwcsaf.org/Downloads/GEO/2021/Documents/Scientific_Docs/NWC-CDOP3-GEO-AEMET-SCI-ATBD-Precipitation_v1.0.1.pdf, (last access: 28 July 2023), 2021. a
Leijnse, H., Uijlenhoet, R., and Stricker, J. N. M.: Rainfall measurement using radio links from cellular communication networks, Water Resour. Res., 3, W03201, https://doi.org/10.1029/2006WR005631, 2007. a
Leijnse, H., Uijlenhoet, R., and Stricker, J.: Microwave link rainfall estimation: Effects of link length and frequency, temporal sampling, power resolution, and wet antenna attenuation, Adv. Water Resour., 31, 1481–1493, https://doi.org/10.1016/j.advwatres.2008.03.004, 2008. a, b, c
Liberman, Y., Samuels, R., Alpert, P., and Messer, H.: New algorithm for integration between wireless microwave sensor network and radar for improved rainfall measurement and mapping, Atmos. Meas. Tech., 7, 3549–3563, https://doi.org/10.5194/amt-7-3549-2014, 2014. a
Maggioni, V., Meyers, P. C., and Robinson, M. D.: A Review of Merged High-Resolution Satellite Precipitation Product Accuracy during the Tropical Rainfall Measuring Mission (TRMM) Era, J. Hydrometeorol., 17, 1101–1117, https://doi.org/10.1175/JHM-D-15-0190.1, 2016. a
Messer, H. and Sendik, O.: A New Approach to Precipitation Monitoring: A critical survey of existing technologies and challenges, IEEE Signal Proc. Mag., 32, 110–122, https://doi.org/10.1109/MSP.2014.2309705, 2015. a
Messer, H., Zinevich, A., and Alpert, P.: Environmental Monitoring by Wireless Communication Networks, Science, 312, 713–713, https://doi.org/10.1126/science.1120034, 2006. a
NWC SAF: PC and PC-Ph products, Nowcasting and Very Short Range Forecasting Satellite Application Facility (NWC SAF) [data set], https://www.nwcsaf.org/web/guest/nwc/geo-geostationary-near-real-time-v2021 (last access: 20 December 2023), 2023. a
Overeem, A., Leijnse, H., and Uijlenhoet, R.: Measuring urban rainfall using microwave links from commercial cellular communication networks, Water Resour. Res., 47, W12505, https://doi.org/10.1029/2010WR010350, 2011. a
Overeem, A., Leijnse, H., and Uijlenhoet, R.: Country-wide rainfall maps from cellular communication networks, P. Natl. Acad. Sci. USA, 110, 2741–2745, https://doi.org/10.1073/pnas.1217961110, 2013. a
Overeem, A., Leijnse, H., and Uijlenhoet, R.: Two and a half years of country-wide rainfall maps using radio links from commercial cellular telecommunication networks, Water Resour. Res., 52, 8039–8065, https://doi.org/10.1002/2016WR019412, 2016. a, b, c
Pollock, M. D., O'Donnell, G., Quinn, P., Dutton, M., Black, A., Wilkinson, M. E., Colli, M., Stagnaro, M., Lanza, L. G., Lewis, E., Kilsby, C. G., and O'Connell, P. E.: Quantifying and Mitigating Wind-Induced Undercatch in Rainfall Measurements, Water Resour. Res., 54, 3863–3875, https://doi.org/10.1029/2017WR022421, 2018. a
Rafieeinasab, A., Norouzi, A., Kim, S., Habibi, H., Nazari, B., Seo, D.-J., Lee, H., Cosgrove, B., and Cui, Z.: Toward high-resolution flash flood prediction in large urban areas – Analysis of sensitivity to spatiotemporal resolution of rainfall input and hydrologic modeling, J. Hydrol., 531, 370–388, https://doi.org/10.1016/j.jhydrol.2015.08.045, 2015. a
Rios Gaona, M. F., Overeem, A., Leijnse, H., and Uijlenhoet, R.: Measurement and interpolation uncertainties in rainfall maps from cellular communication networks, Hydrol. Earth Syst. Sci., 19, 3571–3584, https://doi.org/10.5194/hess-19-3571-2015, 2015. a
Roebeling, R. A. and Holleman, I.: SEVIRI rainfall retrieval and validation using weather radar observations, J. Geophys. Res.-Atmos., 114, D21202, https://doi.org/10.1029/2009JD012102, 2009. a
Roebeling, R. A., Deneke, H. M., and Feijt, A. J.: Validation of Cloud Liquid Water Path Retrievals from SEVIRI Using One Year of CloudNET Observations, J. Appl. Meteorol. Clim., 47, 206–222, https://doi.org/10.1175/2007JAMC1661.1, 2008. a, b, c
Roversi, G., Alberoni, P. P., Fornasiero, A., and Porcù, F.: Commercial microwave links as a tool for operational rainfall monitoring in Northern Italy, Atmos. Meas. Tech., 13, 5779–5797, https://doi.org/10.5194/amt-13-5779-2020, 2020. a
Schleiss, M. and Berne, A.: Identification of Dry and Rainy Periods Using Telecommunication Microwave Links, IEEE Geosci. Remote S., 7, 611–615, https://doi.org/10.1109/LGRS.2010.2043052, 2010. a, b
Schleiss, M., Olsson, J., Berg, P., Niemi, T., Kokkonen, T., Thorndahl, S., Nielsen, R., Ellerbæk Nielsen, J., Bozhinova, D., and Pulkkinen, S.: The accuracy of weather radar in heavy rain: a comparative study for Denmark, the Netherlands, Finland and Sweden, Hydrol. Earth Syst. Sci., 24, 3157–3188, https://doi.org/10.5194/hess-24-3157-2020, 2020. a
Schmid, J.: The SEVIRI instrument, in: Proceedings of the 2000 EUMETSAT meteorological satellite data user's conference, Bologna, Italy, 29 May–2 June 2000, AEMET, vol. 29, 13–32, 2000. a
Sevruk, B.: Rainfall Measurement: Gauges, Chap. 35, John Wiley & Sons, Ltd, ISBN 9780470848944, https://doi.org/10.1002/0470848944.hsa038, 2006. a
Song, K., Liu, X., Zou, M., Zhou, D., Wu, H., and Ji, F.: Experimental Study of Detecting Rainfall Using Microwave Links: Classification of Wet and Dry Periods, IEEE J. Sel. Top. Appl., 13, 5264–5271, https://doi.org/10.1109/JSTARS.2020.3021555, 2020. a
Steiner, M., Smith, J. A., and Uijlenhoet, R.: A Microphysical Interpretation of Radar Reflectivity-Rain Rate Relationships, J. Atmos. Sci., 61, 1114–1131, 2004. a
Thoss, A.: Algorithm Theoretical Basis Document for Precipitating Clouds of the NWC/PPS, NWCSAF, https://www.nwcsaf.org/AemetWebContents/ScientificDocumentation/Documentation/PPS/v2014/NWC-CDOP2-PPS-SMHI-SCI-ATBD-4_v1_0.pdf, (last access: 28 July 2023), 2014. a
Tiede, J., Chwala, C., and Siart, U.: New Insights Into the Dynamics of Wet Antenna Attenuation Based on In Situ Estimations Provided by the Dedicated Field Experiment ATTRRA2, IEEE Geosci. Remote S., 20, 1–5, https://doi.org/10.1109/LGRS.2023.3320755, 2023. a
Uijlenhoet, R., Steiner, M., and Smith, J. A.: Variability of Raindrop Size Distributions in a Squall Line and Implications for Radar Rainfall Estimation, J. Hydrometeor., 4, 43–61, 2003. a
Uijlenhoet, R., Overeem, A., and Leijnse, H.: Opportunistic remote sensing of rainfall using microwave links from cellular communication networks, WIREs Water, 5, e1289, https://doi.org/10.1002/wat2.1289, 2018. a
UNFCCC: Sharm el-Sheikh Implementation Plan, Revised draft decision-/CMA.4, UNFCCC, https://unfccc.int/documents/621908 (last access: 12 April 2024), 2022. a
van de Beek, R. (C. Z.)., Olsson, J., and Andersson, J.: Optimal grid resolution for precipitation maps from commercial microwave link networks, Adv. Sci. Res., 17, 79–85, https://doi.org/10.5194/asr-17-79-2020, 2020. a
van het Schip, T. I., Overeem, A., Leijnse, H., Uijlenhoet, R., Meirink, J. F., and van Delden, A. J.: Rainfall measurement using cell phone links: classification of wet and dry periods using geostationary satellites, Hydrolog. Sci. J., 62, 1343–1353, https://doi.org/10.1080/02626667.2017.1329588, 2017. a, b
Villarini, G. and Krajewski, W. F.: Review of the Different Sources of Uncertainty in Single Polarization Radar-Based Estimates of Rainfall, Surv. Geophys., 31, 107–129, https://doi.org/10.1007/s10712-009-9079-x, 2010. a
Wagner, A.: Spatiotemporal Variability of Precipitation: Measurements – Simulations – Limitations, doctoralthesis, Universität Augsburg, https://opus.bibliothek.uni-augsburg.de/opus4/files/38014/Wagner_Diss.pdf, (last access: 28 July 2023), 2018. a
Wagner, A., Seltmann, J., and Kunstmann, H.: Joint statistical correction of clutters, spokes and beam height for a radar derived precipitation climatology in southern Germany, Hydrol. Earth Syst. Sci., 16, 4101–4117, https://doi.org/10.5194/hess-16-4101-2012, 2012. a
Wang, Z., Schleiss, M., Jaffrain, J., Berne, A., and Rieckermann, J.: Using Markov switching models to infer dry and rainy periods from telecommunication microwave link signals, Atmos. Meas. Tech., 5, 1847–1859, https://doi.org/10.5194/amt-5-1847-2012, 2012. a
Winterrath, T., Rosenow, W., and Weigl, E.: On the DWD quantitative precipitation analysis and nowcasting system for real-time application in German flood risk management, Weather Radar and Hydrology, IAHS Publ. 351, 323–329, https://www.dwd.de/DE/leistungen/radolan/radolan_info/Winterrath_German_flood_risk_management_pdf.html?blob=publicationFile&v=4 (last access: 28 July 2023), 2012. a, b, c
Winterrath, T., Brendel, C., Hafer, M., Junghänel, T., Klameth, A., Lengfeld, K., Walawender, E., Weigl, E., and Becker, A.: RADKLIM Version 2017.002: Reprocessed quasi gauge-adjusted radar data, 5-minute precipitation sums (YW), Deutscher Wetterdienst (DWD), https://doi.org/10.5676/DWD/RADKLIM_YW_V2017.002, 2018. a
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...
