Unconstrained GNSS Water Vapor Tomography With Real Data and LEO Augmentation

Abstract The availability of four complete GNSS constellations (GPS, GLONASS, BeiDou and Galileo) offers, for the first time, the possibility of performing water vapor tomographic inversions that do not rely on external data. A tomographic model that includes no external constraints and requires no first guess and no virtual observations is described. Two sets of 1‐year of inversions near Lisbon and Hong Kong, with rather different climates and network geometries, show that network density is a key factor for the quality of these inversions. Furthermore, a feasibility study combining the same GNSS slant observations near Lisbon with synthetic slant data from a hypothetical constellation of GNSS‐like satellites in Low Earth Orbit (LEO)—both computed by raytracing through a Weather Research and Forecasting simulation forced by ERA5 reanalysis—suggests that higher resolution, better than 500 m, is attainable with tomography, as required for the observation of low tropospheric features in the water vapor profile.