Speaker
Description
Accurate quantitative precipitation estimation (QPE) at high spatiotemporal resolution remains challenging despite advances in observational technology. This study presents the first comprehensive evaluation of rainfall retrievals from a commercial microwave link (CML) network in Belgium, examining whether CML-derived rainfall can complement existing dense rain gauge and weather radar networks. We analyze four intense summer rainfall events in 2023 using over 2800 microwave (sub)links operating across frequencies from 10 to 85 GHz. Through systematic sensitivity experiments, we assess the impact of optimizing the processing procedures. Our results demonstrate that careful processing of CML data is essential: a novel outlier filtering algorithm, radar-based wet-dry classification, rainfall-intensity-dependent wet-antenna correction, and fitting local drop size distributions from three disdrometers substantially improve rainfall retrievals. The optimized CML-derived rainfall estimates match or exceed the performance of a state-of-the-art radar-gauge merged product compared to a dense rain-gauge network, particularly over urban areas with dense high-frequency link coverage, like the Brussels-Capital Region. These findings provide strong evidence that integration of CML information into multi-source precipitation products could yield substantial improvements in high-resolution QPE, particularly for urban hydrological applications and extreme-event
monitoring.