Speakers
Description
This study presents the first application of commercial microwave links (CMLs) for estimating rainfall intensity and variability in Rwanda, a tropical country characterized by high elevations (950-4500 m), complex mountainous terrain, and severe weather conditions. These factors significantly affect the performance of conventional rainfall observation systems, including dense automatic rain gauge (ARG) networks, satellite products, and radar measurements.
The research introduces a cost-effective rainfall monitoring approach based on opportunistic sensing (OS), leveraging the existing dense network of microwave links operated by the telecommunications company MTN Rwandacell. As of June 2023, this network comprises approximately 1,198 links, spatially distributed across the entire country, enabling comprehensive national coverage.
In this conference contribution, we present the first analysis based on an MTN dataset covering 2018 and 2019 from MTN Rwandacell at one minute temporal frequency. The analysis was conducted in collaboration with the Swedish Meteorological and Hydrological Institute, Ghent University, Meteo Rwanda, and the University of Rwanda, in the framework of the VLIRUOS Sensor² project and the OpenSense CA20136 EU Cost Action. The ultimate objective is to integrate multiple rainfall data sources, ARGs, radar, satellite observations, and CMLs, into an operational rainfall product with high spatial and temporal resolution for Rwanda.
A crucial initial step in the CML-based rainfall estimation process is rigorous data quality control (QC), including outlier detection and filtering, as well as blackout identification for individual links to assess link performance. Out of 335 links available in the present dataset, 223 were found to be active and sufficiently stable for use in this analysis. The Pycomlink software package was employed for rainfall retrieval, while inverse distance weighting (IDW) was used for spatial interpolation. Initial results, exploring various options for wet-dry classification and wet antenna correction, as well as a comparison against ARGs, will be presented.
Future work will focus on refitting the attenuation - rainfall relation using local disdrometer observations and on merging CML products with existing ARG, radar, and satellite datasets. Additionally, alternative methods for dry–wet period identification using Meteosat Third Generation data and advanced spatial interpolation techniques will be explored. Overall, this research aims to enhance rainfall monitoring accuracy and strengthen early warning systems at the Rwanda Meteorology Agency, thereby reducing risks associated with extreme weather events and improving national disaster preparedness.