Speaker
Description
An important step in deriving precipitation estimates from commercial microwave links (CMLs) involves separating the attenuation caused by rainfall from the baseline attenuation and wet antenna attenuation (WAA). The baseline is usually estimated from the signal loss preceding a rainfall event, making the baseline sensitive to the estimated starting time for rainfall events. A rainfall event can either be detected by using external data, such as a weather radar, or by analysing the CML data itself. However, different rainfall detection methods capture different aspects of the rainfall event, causing different event timing and thus baseline estimates. Also, as the WAA model is typically estimated by calibrating it to nearby rain gauges, the estimated WAA models might overfit the data, leading to less generalizable baseline-WAA model combinations.
In this work, we explore different rainfall detection models combined with different WAA models. The models are trained and tested using data from CMLs and nearby rain gauges from Norway, Germany, Sweden and Italy. The results show that some baseline-WAA method combinations lead to better precipitation estimates when tested using cross-validation.
| Are you an Early Career Scientist ? | Yes |
|---|
