The atmospheric excess path delay is a major contributor to the error budget of space geodetic positioning applications and should therefore be reduced to the maximum possible extent. Numerical weather models are undergoing improvements with regard to their spatial resolution, which enables the compensation of troposphere propagation errors by applying corrections obtained from ray‐tracing through three‐dimensional meteorologic fields. Since in the selection of the locations of the grid points priority is given to the requirements of meteorologists rather than the facilitation of efficient ray‐tracing algorithms, we propose a method that can resample and refine the large data cubes onto regular grids using a sophisticated and fast method developed at the National Institute of Information and Communications Technology (NICT). Once these data sets are generated, ray‐tracing algorithms can be applied in order to compute atmospheric excess path delays in real time for several users using off‐the‐shelf PCs. We present three different ray‐tracing strategies and discuss their advantages and bottlenecks with regard to accuracy and data throughput.

This paper presents a new method for retrieving sea surface heights from Global Navigation Satellite Systems reflectometry (GNSS‐R) data by inverse modeling of SNR observations from a single geodetic receiver. The method relies on a B‐spline representation of the temporal sea level variations in order to account for its continuity. The corresponding B‐spline coefficients are determined through a nonlinear least squares fit to the SNR data, and a consistent choice of model parameters enables the combination of multiple GNSS in a single inversion process. This leads to a clear increase in precision of the sea level retrievals which can be attributed to a better spatial and temporal sampling of the reflecting surface. Tests with data from two different coastal GNSS sites and comparison with colocated tide gauges show a significant increase in precision when compared to previously used methods, reaching standard deviations of 1.4 cm at Onsala, Sweden, and 3.1 cm at Spring Bay, Tasmania.

Global navigation satellite system (GNSS) remote sensing of the troposphere, called GNSS meteorology, is already a well-established tool in post-processing applications. Real-time GNSS meteorology has been possible since 2013, when the International GNSS Service (IGS) established its real-time service. The reported accuracy of the real-time zenith total delay (ZTD) has not improved significantly over time and usually remains at the level of 5–18 mm, depending on the station and test period studied. Millimeter-level improvements are noticed due to GPS ambiguity resolution, gradient estimation, or multi-GNSS processing. However, neither are these achievements combined in a single processing strategy, nor is the impact of other processing parameters on ZTD accuracy analyzed. Therefore, we discuss these shortcomings in detail and present a comprehensive analysis of the sensitivity of real-time ZTD on processing parameters. First, we identify a so-called common strategy, which combines processing parameters that are identified to be the most popular among published papers on the topic. We question the popular elevation-dependent weighting function and introduce an alternative one. We investigate the impact of selected processing parameters, i.e., PPP functional model, GNSS selection and combination, inter-system weighting, elevation-dependent weighting function, and gradient estimation. We define an advanced strategy dedicated to real-time GNSS meteorology, which is superior to the common one. The a posteriori error of estimated ZTD is reduced by 41%. The accuracy of ZTD estimates with the proposed strategy is improved by 17% with respect to the IGS final products and varies over stations from 5.4 to 10.1 mm. Finally, we confirm the latitude dependency of ZTD accuracy, but also detect its seasonality.