Speaker
Description
Accurate precipitation estimation remains inherently uncertain, particularly at high spatial and temporal resolution. Although the Belgian radar network is relatively dense, radar shadow mapping reveals beam blockage of up to 20% in eastern Wallonia. These limitations motivate the integration of complementary observation systems. Commercial Microwave Links (CMLs) provide an opportunistic rainfall sensing approach by converting signal attenuation in telecommunication networks into rainfall estimates. However, the measured attenuation consists of multiple components: baseline dry attenuation, rainfall-induced attenuation, and wet antenna attenuation (WAA). Proper separation of these components is essential, as signal variability can otherwise introduce substantial bias. In this study, we implement a radar-based wet–dry classification method in a dense nationwide network of over 2000 CMLs, defining periods as wet when radar intensity exceeds 0.1 mm h⁻¹. This approach substantially improves rainfall retrieval compared to rolling standard deviation methods. Furthermore, we recalibrate the commonly applied constant WAA correction of 2.3 dB (Overeem et al., 2016) for the Belgian network. Our results indicate that a lower constant value of 1–1.5 dB significantly reduces bias. Finally, we analyse the relationship between rainfall bias and distance to the nearest radar, demonstrating the increasing added value of CMLs in regions affected by radar shadow. These findings highlight the potential of locally calibrated CML data to enhance multimodal precipitation products.