51 citations as recorded by crossref.

1. An assessment of method for changepoint detection applied in tropospheric parameter time series given from numerical weather model. M. Elias https://doi.org/10.13168/AGG.2020.0007
2. Evaluation of surface temperature and pressure derived from MERRA-2 and ERA5 reanalysis datasets and their applications in hourly GNSS precipitable water vapor retrieval over China L. Huang et al. https://doi.org/10.1016/j.geog.2022.08.006
3. A comparative analysis of GNSS-inferred precipitable water vapour at the potential sites for the Africa Millimetre Telescope L. Frans et al. https://doi.org/10.1093/mnras/staf103
4. Trends of atmospheric water vapour in Switzerland from ground-based radiometry, FTIR and GNSS data L. Bernet et al. https://doi.org/10.5194/acp-20-11223-2020
5. Review on the Role of GNSS Meteorology in Monitoring Water Vapor for Atmospheric Physics J. Vaquero-Martínez & M. Antón https://doi.org/10.3390/rs13122287
6. High-resolution models of tropospheric delays and refractivity based on GNSS and numerical weather prediction data for alpine regions in Switzerland K. Wilgan & A. Geiger https://doi.org/10.1007/s00190-018-1203-6
7. Integrating near-infrared, thermal infrared, and microwave satellite observations to retrieve high-resolution precipitable water vapor Z. Du et al. https://doi.org/10.1016/j.rse.2025.114611
8. The Zenith Total Delay Combination of International GNSS Service Repro3 and the Analysis of Its Precision Q. Huang et al. https://doi.org/10.3390/rs16203885
9. FY-3A/MERSI precipitable water vapor reconstruction and calibration using multi-source observation data based on a generalized regression neural network X. Ma et al. https://doi.org/10.1016/j.atmosres.2021.105893
10. Spatial and temporal changes of precipitable water vapour in Thailand S. Buntoung et al. https://doi.org/10.1080/02723646.2019.1710433
11. Analyzing correlations between GNSS retrieved precipitable water vapor and land surface temperature after earthquakes occurrence A. Guo et al. https://doi.org/10.1016/j.scitotenv.2023.162225
12. Estimation of PM2.5 concentrations with high spatiotemporal resolution in Beijing using the ERA5 dataset and machine learning models Z. Wang et al. https://doi.org/10.1016/j.asr.2022.12.016
13. New ZTD estimation model for areas with large elevation differences by integrating the GFS with GNSS L. Yang et al. https://doi.org/10.1088/1361-6501/adfcfe
14. A Novel ENSO Monitoring Method using Precipitable Water Vapor and Temperature in Southeast China Q. Zhao et al. https://doi.org/10.3390/rs12040649
15. Variability and climatology of precipitable water vapor from 12-year GPS observations in Taiwan S. Pang et al. https://doi.org/10.1016/j.asr.2021.01.021
16. Improving the accuracy of real-time precipitable water vapour using country-wide meteorological model with precise point positioning in Thailand C. Charoenphon & C. Satirapod https://doi.org/10.1080/14498596.2020.1758969
17. Reconstruction of high-resolution precipitable water vapor of FY-4A based on GNSS and remote sensing data Z. Wang et al. https://doi.org/10.1088/1361-6501/ae2b8d
18. Variability and Trend in Integrated Water Vapour from ERA-Interim and IGRA2 Observations over Peninsular Malaysia E. Makama & H. Lim https://doi.org/10.3390/atmos11091012
19. A new hybrid observation GNSS tomography method combining the real and virtual inverted signals W. Zhang et al. https://doi.org/10.1007/s00190-021-01576-8
20. Improving the accuracy and spatial resolution of precipitable water vapor dataset using a neural network-based downscaling method X. Ma et al. https://doi.org/10.1016/j.atmosenv.2021.118850
21. Assessing the Performance of Water Vapor Products from ERA5 and MERRA-2 during Heavy Rainfall in the Guangxi Region of China N. Huang et al. https://doi.org/10.3390/atmos15030306
22. Performance of ERA5 data in retrieving Precipitable Water Vapour over East African tropical region R. Ssenyunzi et al. https://doi.org/10.1016/j.asr.2020.02.003
23. Comparison of tropospheric parameters from Meteodrone measurements with GNSS estimates from ground-based stations K. Wilgan et al. https://doi.org/10.1016/j.asr.2020.04.019
24. Performance of ERA5 data in retrieving precipitable water vapor over Hong Kong Z. Wang et al. https://doi.org/10.1016/j.asr.2022.12.059
25. On the Statistical Significance of Climatic Trends Estimated From GPS Tropospheric Time Series F. Alshawaf et al. https://doi.org/10.1029/2018JD028703
26. The Novel Copernicus Global Dataset of Atmospheric Total Water Vapour Content with Related Uncertainties from GNSS Observations K. Rannat et al. https://doi.org/10.3390/rs15215150
27. Towards a zero-difference approach for homogenizing GNSS tropospheric products M. Hoseini et al. https://doi.org/10.1007/s10291-019-0915-2
28. Estimation of precipitable water vapour by GNSS meteorology in Black Sea region of Türkiye E. Kayıkçı et al. https://doi.org/10.1515/jag-2025-0102
29. Comparisons of precipitable water vapor and zenith total delay derived from GNSS measurements, radiosondes, and ERA5 reanalysis data in the contiguous United States P. Gao & J. Cui https://doi.org/10.1016/j.jastp.2026.106741
30. Estimation of integrated water vapor derived from Global Navigation Satellite System observations over Central-Western Argentina (2015–2018). Validation and usefulness for the understanding of regional precipitation events M. Camisay et al. https://doi.org/10.1016/j.jastp.2019.105143
31. An improved GNSS tropospheric tomographic model with an extended region and combining virtual signals S. Liu et al. https://doi.org/10.1016/j.atmosres.2023.106703
32. Tropospheric Delay in the Neapolitan and Vesuvius Areas (Italy) by Means of a Dense GPS Array: A Contribution for Weather Forecasting and Climate Monitoring U. Riccardi et al. https://doi.org/10.3390/atmos12091225
33. Estimating Integrated Water Vapor Trends From VLBI, GPS, and Numerical Weather Models: Sensitivity to Tropospheric Parameterization K. Balidakis et al. https://doi.org/10.1029/2017JD028049
34. A functional modelling approach for reconstructing 3 and 4 dimensional wet refractivity fields in the lower atmosphere using GNSS measurements E. Forootan et al. https://doi.org/10.1016/j.asr.2021.08.012
35. Prediction of daily leaf wetness duration using multi-step machine learning K. Alsafadi et al. https://doi.org/10.1016/j.compag.2024.109131
36. Rainfall forecasting based on the multi-source data fusion and multi-dimension interpolation method for GNSS stations lacking or sparse regions M. Su et al. https://doi.org/10.1016/j.asr.2025.12.058
37. Studies of precipitable water vapour characteristics on a global scale Q. Zhao et al. https://doi.org/10.1080/01431161.2018.1492177
38. Observation of an Extremely Dry Atmospheric Air Column above Bern K. Hocke & W. Wang https://doi.org/10.3390/cli11030063
39. Homogenization of daily precipitable water vapor time series derived from GNSS observations over China D. Zhu et al. https://doi.org/10.1016/j.asr.2023.04.052
40. Analysis of temporal and spatial variations of total precipitable water vapor in western Iran using radiosonde and MODIS measurements M. Merrikhpour & M. Rahimzadegan https://doi.org/10.1117/1.JRS.13.044508
41. Global total precipitable water variations and trends over the period 1958–2021 N. Wan et al. https://doi.org/10.5194/hess-28-2123-2024
42. Spatiotemporal analysis of atmospheric moisture and orographic precipitation dynamics in the Arsi-Bale Massif Mountains, Ethiopia: A multi-source satellite and reanalysis data integration A. Koji & J. Feto https://doi.org/10.1016/j.pce.2026.104819
43. Long-term changes in precipitable water vapour over India derived from satellite and reanalysis data for the past four decades (1980–2020) S. Sarkar et al. https://doi.org/10.1039/D2EA00139J
44. China coastal GNSS network: advancing precipitable water vapor monitoring and applications in climate analysis Z. Wu et al. https://doi.org/10.5194/essd-17-5841-2025
45. Constructing a precipitable water vapor map from regional GNSS network observations without collocated meteorological data for weather forecasting B. Chen et al. https://doi.org/10.5194/amt-11-5153-2018
46. Improving the Wet Refractivity Estimation Using the Extremely Learning Machine (ELM) Technique E. Forootan et al. https://doi.org/10.3390/atmos14010112
47. Rainfall Amount Forecast Using GNSS-PWV Based on Machine Learning Fusion Strategy and the Constraint of Rainfall Event M. Su et al. https://doi.org/10.1109/TGRS.2026.3659172
48. Evaluating the Accuracy of Satellite-Based Microwave Radiometer PWV Products Using Shipborne GNSS Observations Across the Pacific Ocean Y. Gong et al. https://doi.org/10.1109/TGRS.2021.3129001
49. A New Adaptive Absolute Method for Homogenizing GNSS‐Derived Precipitable Water Vapor Time Series D. Zhu et al. https://doi.org/10.1029/2021EA001716
50. Retrieval of high spatial resolution precipitable water vapor maps using heterogeneous earth observation data X. Ma et al. https://doi.org/10.1016/j.rse.2022.113100
51. A new approach to improve precipitable water vapour estimations of Sentinel-3A satellite data using LST, elevation and NDVI over Iran H. Dadashi & M. Rahimzadegan https://doi.org/10.1080/02626667.2023.2251468