MAGIC-CH: Machine-learning based Advancement and usability assessment of GNSS Interferometric Reflectometry for Climatological studies in Switzerland

This project assesses the capabilities of the Swiss Global Navigation Satellite Systems (GNSS) infrastructure for the retrieval of snow and soil moisture products using GNSS Interferometric Reflectometry (GNSS-IR).

Snow, soil moisture and atmospheric water vapor are all key parameters of the global climate system and the hydrological cycle. GNSS-IR is an innovative technique, which allows sensing these parameters using ground-reflected signals from GNSS. The project examines the potential of the current Swiss GNSS infrastructure and provides several advancements to the current GNSS-IR algorithm such as its adaptation to stations situated in complex topography or improved modelling of vegetation effects, using machine learning techniques.

It is funded by the Swiss Federal Office of Meteorology and Climatology (MeteoSwiss) within the framework of the Global Climate Observing System (GCOS) Switzerland.

Contacts:
Matthias Aichinger-Rosenberger, ETH Zurich,
Laura Crocetti, ETH Zurich,
Benedikt Soja, ETH Zurich,

Partners:
Federal Office of Meteorology and Climatology MeteoSwiss