Meteorological observations from ground and space-based Global Navigation Satellite System (GNSS)
Convener: Guergana Guerova  | Co-Convener: Jonathan Jones 
 / Tue, 08 Sep, 09:00–10:30  / Room Sofia III
 / Attendance Tue, 08 Sep, 16:15–17:00  / Display Mon, 07 Sep, 09:00–Wed, 09 Sep, 18:00  / Sofia I

Global Navigation Satellite Systems (GNSS) have not only revolutionised positioning, navigation and timing, but also provided an accurate sensor of the most abundant greenhouse gas, water vapour. In Europe, the application of GNSS in meteorology started roughly two decades ago and today it has evolved into a well established research field with operational assimilation in a number of European National meteorological services Numerical Weather Prediction (NWP) models. Over time, however, advances in GNSS data processing have made it possible to derive new, more advanced tropospheric products for use in severe weather monitoring and for climate science.

As well as tropospheric observations, GNSS signals can also be used to provide a number of other atmospheric observations such as estimates of the ionospheric Total Electron Content (TEC) and reflected signals can be used for terrestrial observations of soil moisture and snow depth (GNSS-reflectometry). Traditionally GNSS-Reflectometry has been achieved using reflected GNSS signals received by LEO satellites observing sea-state, or using inverted antennae to observe soil moisture and snow depth local to the GNSS antenna. More recently however, new techniques have evolved to observe soil moisture and snow depth without the need for inverted antennae, which opens up the possibility of utilising existing, large-scale, commercial GNSS networks as low-cost, snow and soil moisture observing networks.

This session will aim at presenting the state of the art of new products and observing techniques in the fields of tropospheric, ionospheric and GNSS-reflectometry, with a particular focus on severe weather and climate, illustrating the benefits of synergy between the ground and space based approaches.