GNSS Meteorology: AI-Enabled Assimilation of GNSS-derived Tropospheric Parameters with Two Demonstrated Case Studies

GNSS Meteorology is an increasingly important source of atmospheric observations, providing near-real-time information on tropospheric water vapor derived from GNSS signal delays. These observations become especially valuable when available at high density, enabling improved characterization of mesoscale moisture gradients and rapidly evolving atmospheric structures. Skyfora’s Telecom GNSS Meteorology enables such dense coverage by repurposing existing telecom infrastructure as a distributed atmospheric sensing network and extracting GNSS-derived delay information at scale. Such observation streams are particularly valuable in regions where conventional radiosonde, radar, or dense surface networks are limited.

In this contribution, we present an AI-enabled data assimilation method for integrating GNSS-derived tropospheric parameters into modern weather modelling systems. Rather than relying on classical variational methods alone (3D-Var, 4D-Var) and their associated linearized observation operators and background-error assumptions, the approach leverages generative, physics-informed machine learning models to produce dynamically consistent atmospheric state estimates while accounting for observational uncertainty and irregular sampling.

We further highlight the practical deployment of GNSS Meteorology through two demonstration case studies: (i) a national-scale network trial in Latvia and (ii) a live demonstration in the Barcelona region. Together, these cases illustrate how GNSS-derived atmospheric observations can be operationalized into scalable atmospheric monitoring capabilities. The results emphasize the potential of combining novel observation networks with AI-based assimilation to enhance atmospheric situational awareness and support future improvements in forecast skill.