Speaker
Description
Exploiting Satellite Microwave Links (SMLs) as opportunistic rain sensors is a widely adopted approach, particularly when using Ku-band (10–13 GHz) television broadcasting signals compliant with DVB standards. Most broadcast satellites employ transponders operating with an automatic level control mode (ALCM), which mitigates uplink (UL) power variations and renders almost impossible the observability of UL rain attenuation from ground measurements.
Conversely, Fixed Gain Mode (FGM) transponders do not compensate for UL power fluctuations, making UL-induced losses visible in the end-to-end link quality metric measured at a ground receiver station (GRS). In this work, we show that compensating for downlink (DL) rain attenuation at the GRS enables the isolation of the UL-related attenuation component.
The proposed approach relies on a dry-weather baseline to characterize nominal link conditions and to separate rain-induced effects from slow drifts and common fluctuations. Using measurements collected in Pisa (Italy) and Rambouillet (France), we remove DL rain effects at the Pisa GRS (where rain occurrence is validated by in-situ rain gauges) to enhance the detectability of rain events affecting the feeder station (FS) in Rambouillet. The relationship between the signals observed at Pisa and Rambouillet is identified by leveraging an additional GRS co-located in Rambouillet, providing a reference to capture shared link dynamics.
The co-located GRS at the FS site is required only for an initial training phase to learn this relationship. In a future perspective, data-driven (AI-based) models could perform such training directly from accumulated measurements. Once the relationship between the FS reference and a generic remote GRS is established and provided the location and relevant characteristics of other GRSs operating with the same satellite are known, the sensing task can in principle be carried out from any available GRS independently.
These results indicate that rain parameters (e.g., rain rate and accumulated rainfall) at the FS can be opportunistically inferred from a remote GRS within the satellite footprint. Overall, the proposed method is simple and sustainable, and it supports opportunistic rain sensing over national- and continental-scale distances.
Acknowledgements: This work was supported by the following projects: Space It Up, funded by Italian Space Agency (ASI) and the Italian Ministry of University and Research (MUR) – Contract 2024-5-E.0 - CUP I53D24000060005; FoReLab (Departments of Excellence), funded by MUR.