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With the improvement of Global Navigation Satellite System (GNSS) observation accuracy and the establishment of large continuously operating networks, long GNSS time series are now widely used to understand a range of Earth deformation processes. The continuously operating stations of the Crustal Movement Observation Network of China capture deformation signals due to time‐dependent tectonic, nontectonic mass loading, and potentially unknown geophysical processes. In order to separate and recover these underlying sources accurately and effectively, we apply the independent component analysis (ICA) to decompose the observed time series of vertical displacements. Then, we compare these signals with those predicted from independently developed geophysical process models of atmospheric, nontidal ocean, snow, soil moisture mass loading, and the Land Surface Discharge Model, as well as with Gravity Recovery and Climate Experiment observations. For comparison, we also perform the principal component analysis decomposition of time series and find that the ICA achieves a more consistent representation of multiple geophysical contributors to annual vertical GNSS displacements. ICA can decompose the long‐term trend and different seasonal and multiannual signals that closely correspond to the independently derived mass loading models. We find that independent contributions from atmospheric, soil moisture, and snow mass loading can be resolved from the GNSS data. Discrepancies are likely due to the correlated nature of some of the loading processes and unmodeled contributions from groundwater and surface water changes in South Central China and the Ganges Basin. We extract the seasonal sources of vertical GNSS displacements in China using independent component analysisComparison of the independent components with mass loadings and GRACE shows that atmospheric and soil moisture seasonal loads are dominantWe estimate of mass loading cycles due to unmodeled surface and groundwater changes

Atmospheric rivers (ARs) are long, narrow, and transient corridors of strong horizontal water vapor transport that can result in heavy precipitation. Measuring the movement of these concentrated water vapor bands is important in gaining better insight into AR characteristics and forecasts of AR‐caused precipitation. We describe a method to dynamically map the movement of landfalling ARs. The method utilizes high‐rate GPS observations from a dense network to derive isochrones that represent the AR arrival time over specific locations. The generated isochrones show that the three ARs, during landfall over Southern California in January 2017, moved southeastward and took about 10 hr to pass over the study area. Overlaying the topography with isochrones reveals that the Peninsular Ranges slow the movement of the landfalling ARs. The large spacing between two adjacent isochrones, reflecting fast AR movement, is closely related to the increased hourly rain rate. Atmospheric rivers (ARs), “rivers in the sky,” are “rivers” of water vapor rather than liquid water. The landfall of ARs can cause extreme rainfall that in turn induces disasters. We present a method with a dense high‐rate GPS network to capture the movement of the landfalling ARs over Southern California. For the three landfalling AR cases in January 2017, results show that the ARs moved southeastward and the durations of AR passing over the study area were about 10 hr. The results also reveal that the landfalling AR movement is affected by local terrain and the fast AR movement is closely related to the large hourly rain rate. The use of the method provides a way to study ARs with high spatial‐temporal resolution, which is important in gaining better insight into the forecasts of AR‐caused rainfall. Isochrones derived by GPS observations from a dense network are capable of mapping the movement of landfalling atmospheric riversResults show that the three landfalling atmospheric rivers moved southeastward with a duration of about 10 hr over the study areaThe isochrone‐reflected fast atmospheric river movement is closely related to the large hourly rain rate

We use 2.5 to 14 years long position time series from >800 continuous Global Positioning System (GPS) stations to study vertical deformation rates in the Euro‐Mediterranean region. We estimate and remove common mode errors in position time series using a principal component analysis, obtaining a significant gain in the signal‐to‐noise ratio of the displacements data. Following the results of a maximum likelihood estimation analysis, which gives a mean spectral index ~ −0.7, we adopt a power law + white noise stochastic model in estimating the final vertical rates and find 95% of the velocities within ±2 mm/yr, with uncertainties from filtered time series ~40% smaller than from the unfiltered ones. We highlight the presence of statistically significant velocity gradients where the stations density is higher. We find undulations of the vertical velocity field at different spatial scales both in tectonically active regions, like eastern Alps, Apennines, and eastern Mediterranean, and in regions characterized by a low or negligible tectonic activity, like central Iberia and western Alps. A correlation between smooth vertical velocities and topographic features is apparent in many sectors of the study area. Glacial isostatic adjustment and weathering processes do not completely explain the measured rates, and a combination of active tectonics and deep‐seated geodynamic processes must be invoked. Excluding areas where localized processes are likely, or where subduction processes may be active, mantle dynamics is the most likely process, but regional mantle modeling is required for a better understanding. We show a first synoptic view of vertical GPS rates for the Euro‐MediterraneanVertical velocity gradients occur at different spatial scalesCorrelation between vertical velocities and topography is apparent in many areas