Up to now, state‐of‐the‐art empirical slant delay modeling for processing observations from radio space geodetic techniques has been provided by a combination of two empirical models. These are GPT (Global Pressure and Temperature) and GMF (Global Mapping Function), both operating on the basis of long‐term averages of surface values from numerical weather models. Weaknesses in GPT/GMF, specifically their limited spatial and temporal variability, are largely eradicated by a new, combined model GPT2, which provides pressure, temperature, lapse rate, water vapor pressure, and mapping function coefficients at any site, resting upon a global 5° grid of mean values, annual, and semi‐annual variations in all parameters. Built on ERA‐Interim data, GPT2 brings forth improved empirical slant delays for geophysical studies. Compared to GPT/GMF, GPT2 yields a 40% reduction of annual and semi‐annual amplitude differences in station heights with respect to a solution based on instantaneous local pressure values and the Vienna mapping functions 1, as shown with a series of global VLBI (Very Long Baseline Interferometry) solutions.

Global positioning system (GPS) tropospheric tomography normally requires that the slant wet delays between the GPS satellites and the ground receivers are estimated with high accuracy, which may be difficult given the presence of a number of error sources. This paper presents an alternative approach, namely to estimate the three-dimensional structure of the atmospheric water vapor directly from raw GPS phase observations. The method is tested in a number of simulations, where the impact of network size, the possible horizontal and vertical resolutions, the observation noise, and the inclusion of additional global navigation satellite systems were studied. The simulation results indicate that the refractivity field can be obtained with an accuracy of ~20% or better up to around 4 km with a height resolution of 1 km provided that a sufficient number of receivers and satellites is available