6. Real-Time Data Assimilation for Space Weather Effects Mitigation on GNSS/SBAS

Ionospheric variability falls into various scales. It has diurnal, seasonal, annual and slower trends. Ionospheric models account for the variability faster than diurnal variation of plasma concentration. Empirical models a built to capture trends and variations that are persistent over time. They reproduce climatology: the patterns that we see in average if we consider big dataset. We understand difference between climate and weather. Measurements could be fed into the model in order to make it better fit current state on the environment. Different applications require different specification of the environment. This chapter discusses structures in the propagation environment that could affect GNSS signals, the structures that could be captured using GNSS signals, and how the latter could be used to mitigate the first. We consider global instruments which data are used to feed global models and which are to be simulated locally. We also consider how measurements from different instruments can contribute to the environment specification in order to improve GNSS positioning accuracy.