The Atmosphere and Ocean De‐Aliasing Level‐1B (AOD1B) product provides a priori information about temporal variations in the Earth's gravity field induced by non‐tidal circulation processes in atmosphere and ocean. It is routinely applied as a background model in the Gravity Recovery and Climate Experiment (GRACE)/GRACE Follow‐On (GRACE‐FO) satellite gravimetry data processing. We here present three new datasets in preparation for the upcoming release RL07 of AOD1B, that are based on either the global ERA5 reanalysis or the ECMWF operational data together with simulations from the Max‐Planck‐Institute for Meteorology general circulation model forced consistently with the fields of the same atmospheric data set. The oceanic simulations newly include an updated bathymetry around Antarctica including cavities under the ice shelves, the explicit implementation of the feedback effects of self‐attraction and loading to ocean dynamics as well as a refined harmonic tidal analysis. Comparison to the current release of AOD1B in terms of GRACE‐FO K‐band range‐acceleration pre‐fit residuals, LRI line‐of‐sight gravity differences and band‐pass filtered altimetry data reveals an overall improvement in the representation of the high‐frequency mass variability. Potential benefits of enhancing the temporal resolution remain inconclusive so that the upcoming release 07 will be sampled again every 3 hr.

Ground-based GNSS (Global Navigation Satellite System) has efficiently been used since the 1990s as a meteorological observing system. Recently scientists have used GNSS time series of precipitable water vapor (PWV) for climate research. In this work, we compare the temporal trends estimated from GNSS time series with those estimated from European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-Interim) data and meteorological measurements. We aim to evaluate climate evolution in Germany by monitoring different atmospheric variables such as temperature and PWV. PWV time series were obtained by three methods: (1) estimated from ground-based GNSS observations using the method of precise point positioning, (2) inferred from ERA-Interim reanalysis data, and (3) determined based on daily in situ measurements of temperature and relative humidity. The other relevant atmospheric parameters are available from surface measurements of meteorological stations or derived from ERA-Interim. The trends are estimated using two methods: the first applies least squares to deseasonalized time series and the second uses the Theil–Sen estimator. The trends estimated at 113 GNSS sites, with 10 to 19 years temporal coverage, vary between −1.5 and 2.3 mm decade⁻¹ with standard deviations below 0.25 mm decade⁻¹. These results were validated by estimating the trends from ERA-Interim data over the same time windows, which show similar values. These values of the trend depend on the length and the variations of the time series. Therefore, to give a mean value of the PWV trend over Germany, we estimated the trends using ERA-Interim spanning from 1991 to 2016 (26 years) at 227 synoptic stations over Germany. The ERA-Interim data show positive PWV trends of 0.33 ± 0.06 mm decade⁻¹ with standard errors below 0.03 mm decade⁻¹. The increment in PWV varies between 4.5 and 6.5 % per degree Celsius rise in temperature, which is comparable to the theoretical rate of the Clausius–Clapeyron equation.

Accurate tropospheric delays from Numerical Weather Models (NWM) are an important input to space geodetic techniques, especially for precise real‐time Global Navigation Satellite Systems, which are indispensable to earthquake and tsunami early warning systems as well as weather forecasting. The NWM‐based tropospheric delays are currently provided either site‐specific with a limited spatial coverage, or on two‐dimensional grids close to the Earth surface, which cannot be used for high altitudes. We introduce a new method of representing NWM‐derived tropospheric zenith hydrostatic and wet delays. A large volume of NWM‐derived data is parameterized with surface values and additional two or three coefficients for their vertical scaling to heights up to 14 km. A precision of 1–2 mm is achieved for reconstructing delays to the NWM‐determined delays at any altitudes. The method can efficiently deliver NWM‐derived tropospheric delays to a broader community of space geodetic techniques.

An analysis of processing settings impacts on estimated tropospheric gradients is presented. The study is based on the benchmark data set collected within the COST GNSS4SWEC action with observations from 430 Global Navigation Satellite Systems (GNSS) reference stations in central Europe for May and June 2013. Tropospheric gradients were estimated in eight different variants of GNSS data processing using precise point positioning (PPP) with the G-Nut/Tefnut software. The impacts of the gradient mapping function, elevation cut-off angle, GNSS constellation, observation elevation-dependent weighting and real-time versus post-processing mode were assessed by comparing the variants by each to other and by evaluating them with respect to tropospheric gradients derived from two numerical weather models (NWMs). Tropospheric gradients estimated in post-processing GNSS solutions using final products were in good agreement with NWM outputs. The quality of high-resolution gradients estimated in (near-)real-time PPP analysis still remains a challenging task due to the quality of the real-time orbit and clock corrections. Comparisons of GNSS and NWM gradients suggest the 3^∘ elevation angle cut-off and GPS+GLONASS constellation for obtaining optimal gradient estimates provided precise models for antenna-phase centre offsets and variations, and tropospheric mapping functions are applied for low-elevation observations. Finally, systematic errors can affect the gradient components solely due to the use of different gradient mapping functions, and still depending on observation elevation-dependent weighting. A latitudinal tilting of the troposphere in a global scale causes a systematic difference of up to 0.3 mm in the north-gradient component, while large local gradients, usually pointing in a direction of increasing humidity, can cause differences of up to 1.0 mm (or even more in extreme cases) in any component depending on the actual direction of the gradient. Although the Bar-Sever gradient mapping function provided slightly better results in some aspects, it is not possible to give any strong recommendation on the gradient mapping function selection.

In this study, we estimate integrated water vapor (IWV) trends from very long baseline interferometry (VLBI) and global navigation satellite systems (GNSS) data analysis, as well as from numerical weather models (NWMs). We study the impact of modeling and parameterization of the tropospheric delay from VLBI on IWV trends. We address the impact of the meteorological data source utilized to model the hydrostatic delay and the thermal deformation of antennas, as well as the mapping functions employed to project zenith delays to arbitrary directions. To do so, we derive a new mapping function, called Potsdam mapping functions based on NWM data and a new empirical model, GFZ‐PT. GFZ‐PT differs from previous realizations as it describes diurnal and subdiurnal in addition to long‐wavelength variations, it provides harmonic functions of ray tracing‐derived gradients, and it features robustly estimated rates. We find that alternating the mapping functions in VLBI data analysis yields no statistically significant differences in the IWV rates, whereas alternating the meteorological data source distorts the trends significantly. Moreover, we explore methods to extract IWV given a NWM. The rigorously estimated IWV rates from the different VLBI setups, GNSS, and ERA‐Interim are intercompared, and a good agreement is found. We find a quite good agreement comparing ERA‐Interim to VLBI and GNSS, separately, at the level of 75%.