For mapping Earth surface movements at larger scale and smaller amplitudes, many new synthetic aperture radar instruments (Sentinel‐1A/B, Gaofen‐3, ALOS‐2) have been developed and launched from 2014–2017, and this trend is set to continue with Sentinel‐1C/D, Gaofen‐3B/C, RADARSAT Constellation planned for launch during 2018–2025. This poses more challenges for correcting interferograms for atmospheric effects since the spatial‐temporal variations of tropospheric delay may dominate over large scales and completely mask the actual displacements due to tectonic or volcanic deformation. To overcome this, we have developed a generic interferometric synthetic aperture radar atmospheric correction model whose notable features comprise (i) global coverage, (ii) all‐weather, all‐time useability, (iii) correction maps available in near real time, and (iv) indicators to assess the correction performance and feasibility. The model integrates operational high‐resolution European Centre for Medium‐Range Weather Forecasts (ECMWF) data (0.125° grid, 137 vertical levels, and 6‐hr interval) and continuous GPS tropospheric delay estimates (every 5 min) using an iterative tropospheric decomposition model. The model's performance was tested using eight globally distributed Sentinel‐1 interferograms, encompassing both flat and mountainous topographies, midlatitude and near polar regions, and monsoon and oceanic climate systems, achieving a phase standard deviation and displacement root‐mean‐square (RMS) of ~1 cm against GPS over wide regions (250 by 250 km). Indicators describing the model's performance including (i) GPS network and ECMWF cross RMS, (ii) phase versus estimated atmospheric delay correlations, (iii) ECMWF time differences, and (iv) topography variations were developed to provide quality control for subsequent automatic processing and provide insights of the confidence level with which the generated atmospheric correction maps may be applied.

Pointwise GPS measurements of tropospheric zenith total delay can be interpolated to provide high‐resolution water vapor maps which may be used for correcting synthetic aperture radar images, for numeral weather prediction, and for correcting Network Real‐time Kinematic GPS observations. Several previous studies have addressed the importance of the elevation dependency of water vapor, but it is often a challenge to separate elevation‐dependent tropospheric delays from turbulent components. In this paper, we present an iterative tropospheric decomposition interpolation model that decouples the elevation and turbulent tropospheric delay components. For a 150 km × 150 km California study region, we estimate real‐time mode zenith total delays at 41 GPS stations over 1 year by using the precise point positioning technique and demonstrate that the decoupled interpolation model generates improved high‐resolution tropospheric delay maps compared with previous tropospheric turbulence‐ and elevation‐dependent models. Cross validation of the GPS zenith total delays yields an RMS error of 4.6 mm with the decoupled interpolation model, compared with 8.4 mm with the previous model. On converting the GPS zenith wet delays to precipitable water vapor and interpolating to 1 km grid cells across the region, validations with the Moderate Resolution Imaging Spectroradiometer near‐IR water vapor product show 1.7 mm RMS differences by using the decoupled model, compared with 2.0 mm for the previous interpolation model. Such results are obtained without differencing the tropospheric delays or water vapor estimates in time or space, while the errors are similar over flat and mountainous terrains, as well as for both inland and coastal areas.

This study investigates the quality (in terms of elevation accuracy and systematic errors) of three recent publicly available elevation model datasets over Australia: (i) the 9 arc second national GEODATA DEM-9S ver3 from Geoscience Australia and the Australian National University; (ii) the 3 arc second SRTM ver4.1 from CGIAR-CSI; and (iii) the 1 arc second ASTER-GDEM ver1 from NASA/METI. The main features of these datasets are reported from a geodetic point of view. Comparison at about 1 billion locations identifies artefacts (e.g. residual cloud patterns and stripe effects) in ASTER. For DEM-9S, the comparisons against the space-collected SRTM and ASTER models demonstrate that signal omission (due to the ∼270 m spacing) may cause errors of the order of 100–200 m in some rugged areas of Australia. Based on a set of geodetic ground control points over Western Australia, the vertical accuracy of DEM-9S is ∼9 m, SRTM ∼6 m and ASTER ∼15 m. However, these values vary as a function of the terrain type and shape. Thus, CGIAR-CSI SRTM ver4.1 may represent a viable alternative to DEM-9S for some applications. While ASTER GDEM has an unprecedented horizontal resolution of ∼30 m, systematic errors present in this research-grade version of the ASTER GDEM ver1 will impede its immediate use for some applications.

Within the implementation of the European Geo-stationary Navigation Overlay System (EGNOS), a significant residual error in positioning is due to tropospheric delay effects. The EGNOS guidelines recommend that tropospheric delay is modelled using an empirical correction algorithm based on a receiver's height and estimates of meteorological parameters developed from average and seasonal variation data. However, such a simple average and seasonal variation model is unlikely to emulate temporal weather changes exactly. The potential errors involved in the application of the recommended algorithm and the consequent effects on the positioning errors, under typical UK weather conditions, are detailed in this paper. This was achieved by comparing tropospheric delays produced by the EGNOS model, with tropospheric delays estimated from high precision carrier phase GPS, over a one-year period for five UK stations. The RMS EGNOS model zenith tropospheric delay errors ranged from 4·0 to 4·7 cm, with maximum errors ranging from 13·2 to 17·8 cm. The errors were also shown to be spatially correlated. The subsequent effect on position error is shown to be dependent on the satellite elevation cut-off angle adopted and on whether or not the observations are weighted according to the satellite elevation angle.