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.

Bedrock uplift in Antarctica is dominated by a combination of glacial isostatic adjustment (GIA) and elastic response to contemporary mass change. Here, we present spatially extensive GPS observations of Antarctic bedrock uplift, using 52% more stations than previous studies, giving enhanced coverage, and with improved precision. We observe rapid elastic uplift in the northern Antarctic Peninsula. After considering elastic rebound, the GPS data suggests that modeled or empirical GIA uplift signals are often over‐estimated, particularly the magnitudes of the signal maxima. Our observation that GIA uplift is misrepresented by modeling (weighted root‐mean‐squares of observation‐model differences: 4.9–5.0 mm/yr) suggests that, apart from a few regions where large ice mass loss is occurring, the spatial pattern of secular ice mass change derived from Gravity Recovery and Climate Experiment (GRACE) data and GIA models may be unreliable, and that several recent secular Antarctic ice mass loss estimates are systematically biased, mainly too high.

[1] Coordinate time series are often derived from continuous GPS data processed as discrete 24 hour solutions. Under this regimen, residual semi-diurnal and diurnal crustal tide signatures are under-sampled, resulting in aliased periodic signals in the coordinate time series. A secondary aliasing effect, well known from satellite altimetry studies but generally ignored in GPS analysis, arises from the repeat period of the satellite orbits being longer than the Nyquist period of the semi-diurnal and diurnal tidal signatures. This paper derives the theoretical periods for these two aliasing effects for the principal semidiurnal and diurnal tidal constituents. The presence of both types of aliased signals in GPS time series is then demonstrated using simulated GPS data and also considered for time series derived from real GPS data. It is shown that the beating of the two aliased signals invariably results in spurious signatures in the time series with semi-annual and annual periods.

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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.