Detecting Signatures of Convective Storm Events in GNSS-SNR: Two Case Studies From Summer 2021 in Switzerland

Global Navigation Satellite Systems (GNSS) are not only a state-of-the-art sensor for positioning and navigation applications but also a valuable tool for remote sensing. Through the usage of L-band carrier frequencies, GNSS acts as an all-weather-operation system, offering substantial benefits compared to optical systems. Nevertheless, severe weather can still have an impact on the strength of signals received at a ground station, as we show in this study. We investigate GNSS Signal-to-Noise Ratio (SNR) observations during two severe convective storm events over the city of Zurich, Switzerland. We make use of a GNSS-SNR-based algorithm originally developed for the detection of hail particles from volcanic eruptions. Results indicate that, although GNSS observations are considered to be fairly insensitive to the presence of hydrometeors, convective storm events are visible in SNR observations. SNR levels of affected satellites show a significant drop during event periods, which are determined by weather radar observations. Over the last two decades, observations from Global Navigation Satellite Systems (GNSS) have proven to be a useful data source for meteorology. Typically, signal delays introduced by the presence of water vapor along the signal path are utilized for analyzing and predicting the atmospheric moisture field, and subsequently, precipitation. However, other GNSS observations types can also be influenced by severe thunder- and hailstorms, which are high-impact weather events. In this study, we show that is the case for the Signal-to-Noise Ratio (SNR) of GNSS signals. We investigate two large thunderstorm events which took place over the city of Zurich. These events are visible as significant degradation in SNR data of GNSS satellites. By analyzing radar images we are able to show that the time period of SNR degradation closely corresponds to the period of strongest precipitation intensity observed by radar. Although more detailed investigations have to be carried out in the future, these initial findings indicate the potential of GNSS-SNR data for observation of severe weather events and strengthen the status of GNSS as a valuable tool for meteorological applications. GNSSSevere storm detectionSignal-to-Noise ratio