Matthias Aichinger-Rosenberger
Abstract/Description
Besides being an indispensable sensor for positioning, navigation and timing purposes, observations from Global Navigation Satellite Systems (GNSS) are also contributing to atmospheric and climate sciences. Products such as signal path delays or vertical profiles of temperature and humidity can be utilized for numerical weather prediction and operational weather forecasting. Since GNSS broadcast signals on L-band carrier frequencies, they act as an all-weather monitoring system, providing important advantages over other remote sensing techniques. However, severe weather events can still impact the strength of signals received at a ground station. This has been demonstrated recently by analyzing signal-to-noise ratio (SNR) observations for two severe thunderstorms over the city of Zurich during the summer of 2021 (Aichinger-Rosenberger et al. 2023).
This study presents an extension of these investigations by combining SNR and troposphere products from GNSS for the detection and tracking of thunderstorm systems. Specific case studies of severe events over the Alpine region will be presented. Furthermore, the study explores the performance of alternative detection methods such as data-driven algorithms in comparison to the simple statistical method presented in Aichinger-Rosenberger et al. (2023).
References
Aichinger-Rosenberger, M., Aregger, M., Kopp, J., & Soja, B. (2023). Detecting signatures of convective storm events in GNSS-SNR: Two case studies from summer 2021 in Switzerland. Geophysical Research Letters, 50, e2023GL104916.
