Precipitable Water Vapor (PWV), as an important indicator of atmospheric water vapor, can be derived from Global Navigation Satellite System (GNSS) observations with the advantages of high precision and all-weather capacity. GNSS-derived PWV with a high spatiotemporal resolution has become an important source of observations in meteorology, particularly for severe weather conditions, for water vapor is not well sampled in the current meteorological observing systems. In this study, an empirical atmospheric weighted mean temperature (T_m) model for Guilin is established using the radiosonde data from 2012 to 2017. Then, the observations at 11 GNSS stations in Guilin are used to investigate the spatiotemporal features of GNSS-derived PWV under the heavy rainfalls from June to July 2017. The results show that the new T_m model in Guilin has better performance with the mean bias and Root Mean Square (RMS) of − 0.51 and 2.12 K, respectively, compared with other widely used models. Moreover, the GNSS PWV estimates are validated with the data at Guilin radiosonde station. Good agreements are found between GNSS-derived PWV and radiosonde-derived PWV with the mean bias and RMS of − 0.9 and 3.53 mm, respectively. Finally, an investigation on the spatiotemporal characteristics of GNSS PWV during heavy rainfalls in Guilin is performed. It is shown that variations of PWV retrieved from GNSS have a direct relationship with the in situ rainfall measurements, and the PWV increases sharply before the arrival of a heavy rainfall and decreases to a stable state after the cease of the rainfall. It also reveals the moisture variation in several regions of Guilin during a heavy rainfall, which is significant for the monitoring of rainfalls and weather forecast.

Precipitable water vapor (PWV), despite being the key factor for assessing climate change over the Tibetan Plateau (TP), is undersampled in this region due to the complex terrain and climate conditions therein. The fifth‐generation European Center for Medium‐Range Weather Forecasts Reanalysis (ERA5) and Second Modern‐Era Retrospective Analysis for Research and Applications (MERRA‐2) data sets are the two newly released global reanalyzes that provide hourly PWV products with a high spatiotemporal resolution for water vapor research. The accuracies of these two PWV products on the TP require a comprehensive and cautious assessment. In this study, the hourly PWV values derived from ERA5 and MERRA‐2 are systematically assessed by a comparison with 33 Global Navigation Satellite System (GNSS) stations from 2017 to 2018 over the TP. To improve the reliability of this evaluation, an enhanced T_m model and a PWV vertical correction model, namely, TP‐T_m and TP‐PWVC, respectively, are developed, and both models show better performance when compared against other widely used models. The accuracies of the ERA5‐ and MERRA‐2‐derived PWV at the 33 GNSS stations are very high; ERA5 has the smallest mean bias and root mean square (RMS) error of −0.08 and 1.77 mm, respectively, compared to those of 0.53 and 2.12 mm for MERRA‐2. In addition, the biases and RMS errors show significant regional and seasonal differences. The performance of ERA5 is slightly better than MERRA‐2, mainly due to its higher spatial resolution. The spatial distributions of the PWV derived from the two reanalyzes are consistent with the GNSS‐derived PWV distribution. The diurnal PWV variations derived from ERA5 are in good agreement with the diurnal variation of the GNSS‐derived PWV, whereas significant differences are observed for MERRA‐2 at several stations. These results indicate that ERA5 can provide more reliable hourly PWV estimates over the TP, which can help users further understand the strengths and weaknesses of these two PWV products and promote climate change research.

Precipitable water vapor (PWV) plays a vital role in climate research, especially for Antarctica in which meteorological observations are insufficient due to the adverse climate and topography therein. Reanalysis data sets provide a great opportunity for Antarctic water vapor research. This study investigates the climatological PWV means, variability and trends over Antarctica from four reanalyses, including the fifth generation of European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA5), the Second Modern-Era Retrospective analysis for Research and Applications (MERRA-2), Japanese 55-year Reanalysis (JRA-55) and National Centers for Environmental Prediction/Department of Energy (NCEP/DOE), in the period of 2001–2018 based on radiosonde and GNSS observations. PWV data from the ERA5, MERRA-2, JRA-55 and NCEP/DOE have been evaluated by radiosonde and GNSS observations, showing that ERA5 and MERRA-2 perform better than JRA-55 and NCEP/DOE with mean root mean square (RMS) errors below 1.2 mm. The climatological PWV mean distribution over Antarctica roughly shows a decreasing trend from west to east, with the highest content in summer and the lowest content in winter. The PWV variability is generally small over Antarctica, showing a seasonal dependence that is larger in the cold season and smaller in the warm season. PWV trends for all reanalyses at most Antarctic regions are insignificant and most reanalyses present overall drying trends from 2001 to 2018, except for ERA5 exhibiting a moistening trend. PWV trends also show seasonal and regional dependence. All reanalyses are generally consistent with radiosonde and GNSS observations in reproducing the PWV means (mean differences within 1.1 mm), variability (mean differences within 3%) and trends (mean differences within 6.4% decade⁻¹) over Antarctica, except for NCEP/DOE showing spurious variability and trends in East Antarctica. Results can help us further understand these four reanalysis PWV products and promote climate research in Antarctica.

Precipitable water vapor (PWV) is an important indicator for quantifying atmospheric water vapor, and its evolution is intrinsically linked to the formation and development of extreme weather. As a type of heavy rainfall occurring during the Dragon Boat Festival in southern China, the phenomenon known as “Dragon Boat Water” (DBW) has caused a series of disasters in Guangxi Province. Therefore, it is crucial to investigate PWV evolution and contributing factors. The PWV retrieved by Global Navigation Satellite System (GNSS) technology offers a reliable method due to its advantages of high-temporal resolution, high-precision, and weather-independent. In this study, the GNSS-derived PWV at the stations was obtained based on the GNSS data from 2020 to 2022 at 121 GNSS stations as well as the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) atmospheric reanalysis data. To assess the performance of the GNSS-derived PWV, it was compared with Radiosondes, demonstrating its high accuracy. Additionally, combined with ERA5 reanalysis data, the evolution of GNSS-retrieval PWV during the DBW event in Guangxi was analyzed, and the results showed that the direction of the water vapor transport pathway was from the southeast to the northwest. The overall trend of PWV decreased from southeast to northwest, with higher values observed in coastal areas compared to inland areas, and greater concentrations in plains than in mountains. Further investigation revealed that the evolution of GNSS-derived PWV was governed by the synergistic effects of mean sea level pressure (MSLP), horizontal wind speed (u-wind), vertical wind speed (v-wind), and 2m temperature (T2M). The direction of the wind field generally aligned with the direction of PWV movement, and the magnitude of PWV corresponded with wind field intensity. The distribution of PWV was found to be negatively correlated with MSLP and positively correlated with T2M. These findings could deepen the understanding of PWV dynamics and improve the prediction of extreme precipitation events.

Zenith wet delay (ZWD) is one of the major sources of errors in the Global Navigation Satellite System (GNSS) positioning. Existing machine learning (ML)-based ZWD models have limitations in their generalization capabilities and in modeling the high spatiotemporal fluctuations in the atmospheric water vapor. To address these limitations, we introduce an advanced ensemble technique that integrates several base ML models through a Self-Supervised Uncertainty-Based Stacking Ensemble (SUSE). The proposed model enhances the accuracy of modeling and prediction of ZWD for China and its neighboring areas. The results demonstrate that our models, SUSE, outperform other ML, (DL), empirical (GPT3), and traditional models (Askne & Nordius (A&N) and Saastamoinen). In terms of RMSE, our blind model SUSE_A achieves internal and external improvements of 24.36% and 24.24% compared to GPT3. Additionally, SUSE_A outperforms conventional models that use meteorological data (A&N and Saastamoinen) and has comparable performance with other ML and DL models that use meteorological parameters. When surface meteorological data were available, the SUSE_B model reduced the RMSE by 34.48% and 47.89% with respect to A&N and Saastamoinen, respectively. Temporal and spatial analysis reveal the superior accuracy and stability of the SUSE model over other models examined in this research. These results indicate the promising prospects of the SUSE model for GNSS applications.

Global Navigation Satellite System (GNSS) radio occultation (RO) is one of the most crucial observations in atmospheric and climate science. GNSS RO globally produces accurate and long-term stable vertical profiles for essential climate variables such as refractivity and temperature at high vertical resolution in all weather conditions. Currently, various RO satellite constellation programs have been developed by nations and companies, and the growing quantity of RO observations can contribute not only globally but also has the potential to benefit specific regions, such as China. To investigate the potential of RO observation in China, the performance of five operational RO measurements from COSMIC-2, MetOp-B/C, FY-3D/E, Spire and PlanetiQ on data coverage capabilities and quality are assessed by comparing with ERA5 and radiosonde over China. The results of data coverage showed that all RO missions can acquire extensive coverage over China with effective low-altitude penetration capability, whereas MetOp-B/C exhibits some gaps in local time coverage. The results of data quality confirmed that commercial Spire and PlanetiQ are comparable to those of national-led COSMIC-2, MetOp-B/C and FY3D/E, even though Spire exhibited a lower signal-to-noise ratio (SNR). The mean bending angle and refractivity relative differences of all RO measurements are within ±2.9% and ±1.5/0.9% (with respect to ERA5/radiosonde) in the altitude range of 5 to 35 km, respectively, and the corresponding relative standard deviations (SD) are less than 6% and 1.8/2.2%, respectively. Mean temperature and specific humidity differences of all RO measurements are within ±0.8/1.0 K and ± 0.7/1.0 g/kg, respectively, from the near surface to 15 km, with SD of less than 2.1/2.0 K and 1.8/1.7 g/kg. These results can help users further understand the strengths and weaknesses of these RO observations and indicate the significant application potential of numerous high-quality RO profiles from various RO measurements, which is anticipated to enhance numerical weather predictions for China.