# Review on the Role of GNSS Meteorology in Monitoring Water Vapor for Atmospheric Physics
## 1 Introduction
This work also included a more detailed explanation of the method for ground-based GNSS water vapor monitoring, providing specific figures for all the constants involved, and discussing their uncertainty in depth. The refractivity constants were obtained by statistical treatment of values from other studies, while the computation of \(T_{m}\) values was also carried out using numerical weather prediction models (NWP), yielding a root mean square error (RMSE) of 2.4 K (as compared to the RMSE of 4.7 K from using the so called \"Bevis formula\"). After these studies, the importance of water vapor and the need of improving its spatial and temporal resolution and coverage was acknowledged in some studies, such as [9]. Moreover, GNSS meteorology was found to be suitable for this task, as most of the infrastructure was already there (for navigation and geodetic applications, mainly). Therefore, it was a unique opportunity to obtain more meteorological information at a very low cost. However, these efforts would be useless without the information provided by services like the International GNSS Service IGS [10], operating since 1994 and providing necessary information for the processing of GNSS data, such as precise and real-time orbits, clock corrections, tropospheric products, and so on. The IGS counts with a global network of ground-based receivers and a set of Analysis Centers to process their data. This global network is depicted in Figure 1.  
It must be noted that the processing of tropospheric delays must be finely tuned for meteorological purposes. For navigation techniques, the estimation of the different delays do not need to be very precise, as long as their combination adds up to the total delay suffered by the satellite-receiver signals. However, for meteorological purposes, the other delays need to be obtained with high precision to avoid that part of the error can be transferred to the tropospheric delay estimate. For instance, the ocean tide loading can induce errors of up to 1 cm in ZTD if it is not properly modeled [11]. GFZ-Potsdam started the development of one of the first systematic near real-time water vapor products based on a German network and in association with other research centers. This was the so-called GNSS Atmosphere Sounding Project (GASP). It was partly dedicated to space-based GNSS observations but also to ground-based measurements [12]. The STD started to be assimilated in analysis experiments for dense GNSS networks, developing ways of estimating the 3D structure of water vapor [13]. The use of ground-based GNSS networks for tomography of the troposphere, obtaining 4D maps of water vapor, was first discussed by [PERSON] et al. [14]. They proposed a methodology (software LOTTOS) to retrieve these maps from STD measurements and concluded that the technique could be used for monitoring the troposphere. Later, [PERSON] et al. [15] presented the STARTWAVE (STudies in Atmospheric Radiative Transfer and Water Vapour Effects) project, whose objective was to obtain a database of different water vapor products (including ground-based GNSS ones) that allowed the study of the role of water vapor in the climate. At the same time, the idea of using GNSS networks for water vapor tomography was still in the air, and [PERSON] and [PERSON] [16] considered the idea of different network geometries. They concluded that the spatial resolution would be given by the mean inter-station distance. It was also reported that the quality and quantity of measurements depend on the distribution of the satellite constellation and, therefore, on time, they would improve as more satellites (including other constellations such as GLONASS or Galileo) became available. Multi-GNSS processing became operational for near real-time applications a few years later, when [PERSON] et al. [17] first compared this kind of measurement with single-GNSS ones. The processing was possible thanks to the efforts of the IGS Multi-GNSS Experiment MGEX [18].