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As an important component of atmosphere, water vapor is highly involved in the global water cycle and energy exchange. To date, Global Navigation Satellite System (GNSS) and radiosonde techniques have been widely used to detect the water vapor content in the atmosphere. Recently, European Centre for Medium‐Range Weather Forecasts (ECMWF) has released the latest reanalysis data set, namely, ECMWF Re‐Analysis (ERA5), whose temporal and spatial resolutions have a significant improvement over its previous‐generation product of ERA‐Interim. This study aims to assess the consistency of precipitable water vapors (PWVs) derived from ERA5, ERA‐Interim, GNSS, and radiosondes during the entire year of 2017. The GNSS‐derived PWVs were obtained at 41 Crustal Movement Observation Network of China stations, while the radiosonde‐derived PWVs were acquired at the adjacent 41 radiosonde stations. The nationwide PWVs derived from the ERA5 and ERA‐Interim show root‐mean‐square (RMS) errors of 1.8 and 2.1 mm with respect to the GNSS PWVs and 2.7 and 2.8 mm with respect to the radiosonde PWVs, respectively. Besides, the RMS errors exhibit significant regional and seasonal differences. The nationwide relative RMS of the ERA5‐ and ERA‐Interim‐derived PWVs are 11.1% and 13.4% with respect to the GNSS PWVs and 16.2% and 17.8% with respect to the radiosonde PWVs, respectively. The relative RMS values show significant difference between the east and west sides of Hu line across China. Furthermore, the nationwide PWVs are obtained using GNSS data sets at over 200 Crustal Movement Observation Network of China stations to compare with the ERA5‐derived PWVs in China. Results indicate that the spatial distribution of the PWVs derived from the two data sources is quite consistent, suggesting a great application prospect of the ERA5 products in China.

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GPS has been widely used in the field of geodesy and geodynamics thanks to its technology development and the improvement of positioning accuracy. A time series observed by GPS in vertical direction usually contains tectonic signals, non-tectonic signals, residual atmospheric delay, measurement noise, etc. Analyzing these information is the basis of crustal deformation research. Furthermore, analyzing the GPS time series and extracting the non-tectonic information are helpful to study the effect of various geophysical events. Principal component analysis (PCA) is an effective tool for spatiotemporal filtering and GPS time series analysis. But as it is unable to extract statistically independent components, PCA is unfavorable for achieving the implicit information in time series. Independent component analysis (ICA) is a statistical method of blind source separation (BSS) and can separate original signals from mixed observations. In this paper, ICA is used as a spatiotemporal filtering method to analyze the spatial and temporal features of vertical GPS coordinate time series in the UK and Sichuan-Yunnan region in China. Meanwhile, the contributions from atmospheric and soil moisture mass loading are evaluated. The analysis of the relevance between the independent components and mass loading with their spatial distribution shows that the signals extracted by ICA have a strong correlation with the non-tectonic deformation, indicating that ICA has a better performance in spatiotemporal analysis.