1,2,2,3,4,5,7,9,10,11- 1Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Campus Alpin, Garmisch-Partenkirchen, Germany
- 2Czech Technical University in Prague, Faculty of Civil Engineering, Dept. Hydraulics and Hydrology, Prague 6, Czechia (baresvoj@cvut.cz)
- 3Royal Netherlands Meteorological Institute (KNMI), R&D Observations and Data Technology, the Netherlands
- 4Delft University of Technology (TU Delft), Department of Water Management
- 5National Research Council, Institute of Electronics, Computer and Telecommunication Engineering (CNR-IEIIT), Milan, Italy
- 6Deutscher Wetterdienst, Department of Hydrometeorology, Offenbach am Main, Germany
- 7School of Electrical and Computer Engineering, Tel Aviv University, Israel
- 8Swedish Meteorological and Hydrological Institute, Norrköping, Sweden
- 9Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
- 10Royal Meteorological Institute of Belgium, Belgium
- 11Institut de Recherche pour le Développement, GET, Toulouse, France
Accurate quantitative precipitation estimation (QPE) remains a major challenge in many data-scarce regions, particularly across low- and middle-income countries where conventional rain gauge and radar networks are sparse or absent. Commercial Microwave Links (CMLs), widely deployed for telecommunication purposes, provide an opportunistic measurement technique that enables high-temporal-resolution rainfall observations (1–15 minutes) with large spatial coverage, especially in densely populated areas.
Recent studies in regions including Sri Lanka, Burkina Faso, Zambia, Nigeria, Ghana, and Cameroon have demonstrated that CML-based rainfall retrieval can complement and, in some cases, outperform satellite-based products such as IMERG in terms of accuracy and spatial representativeness. These results highlight the strong potential of CMLs to enhance QPE, particularly in regions where traditional observation infrastructure is limited.
Despite this demonstrated potential, large-scale operational use of CML data is still constrained by technical, legal, and organizational barriers. To address these challenges, the Global Microwave Data Collection Initiative (GMDI), developed within the framework of the COST Action OpenSense, aims to establish a scalable and sustainable system for global collection and use of CML data for rainfall observation.
In this contribution, we present first results from pilot implementations in Europe and Africa of the GMDI core system, the Data Collection, Archiving, and Processing (CAP) system. The CAP system enables near-real-time acquisition, storage, and processing of large volumes of CML data and supports their integration with complementary meteorological datasets. Furthermore, we discuss key challenges related to data quality and standardization, as well as the development of long-term data-sharing frameworks with mobile network operators.
By improving access to high-resolution rainfall observations, particularly in the Global South, GMDI has the potential to significantly enhance QPE capabilities. This will facilitate the integration of CML and satellite-based rainfall estimates, ultimately strengthening flood early warning systems, water resource management, and climate adaptation strategies worldwide.
How to cite: Christisn, C., Fencl, M., Bareš, V., Overeem, A., Uijlenhoet, R., Nebuloi, R., Winterrath, T., Ostrometzky, J., Messer, H., van de Beek, R., Øydvin, E., Van Weverberg, K., and Gosset, M.: Standardized collection and processing of rainfall data from telecommunication networks: First Results from the Global Microwave Data Collection Initiative (GMDI), EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-554, https://doi.org/10.5194/ems2026-554, 2026.
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