Combining commercial microwave links and weather radar for classification of dry snow and rainfall

Abstract. Differentiating between snow and rainfall is crucial for hydrological modeling and understanding. Commercial Microwave Links (CMLs) can provide accurate rainfall estimates for liquid precipitation, but show minimal signal attenuation during dry snow events, causing the CML time series during these periods to resemble non-precipitation periods. Weather radars can detect precipitation also for dry snow, yet, they struggle to accurately differentiate between precipitation types. This study introduces a new approach to improve rainfall and dry snow classification by combining weather radar precipitation detection with CML signal attenuation. Specifically, events where the radar detects precipitation, but the CML does not, are classified as dry snow. As a reference method we use weather radar, with the precipitation type identified by the dew point temperature at the CML location. Both methods were evaluated using ground measurements from disdrometers within 8 km of a CML, analysing data from 550 CMLs in December 2021 and 435 CMLs in June 2022. Our results show that using CMLs can enhance the classification of dry snow and rainfall, presenting an advantage over the reference method. Further, our research provides valuable insights into how precipitation at temperatures around zero degrees, such as sleet or wet snow, can affect CMLs, contributing to a better understanding of CML applications in colder climates.