Articles | Volume 15, issue 2
https://doi.org/10.5194/essd-15-723-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-15-723-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
10 Feb 2023
Data description paper |
| 10 Feb 2023
| 10 Feb 2023
An enhanced integrated water vapour dataset from more than 10 000 global ground-based GPS stations in 2020
Karlsruhe Institute of Technology, Geodetic Institute, Karlsruhe, BW, Germany
Geoffrey Blewitt
Nevada Bureau of Mines and Geology, University of Nevada, Reno, NV, USA
Corné Kreemer
Nevada Bureau of Mines and Geology, University of Nevada, Reno, NV, USA
William C. Hammond
Nevada Bureau of Mines and Geology, University of Nevada, Reno, NV, USA
Donald Argus
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Xungang Yin
NOAA National Centers for Environmental Information, Asheville, NC, USA
Roeland Van Malderen
Royal Meteorological Institute of Belgium, Brussels, Belgium
Michael Mayer
Karlsruhe Institute of Technology, Geodetic Institute, Karlsruhe, BW, Germany
Weiping Jiang
GNSS Research Center, Wuhan University, Wuhan, China
Joseph Awange
School of Earth and Planetary Sciences, Curtin University, Perth,
Australia
Hansjörg Kutterer
Karlsruhe Institute of Technology, Geodetic Institute, Karlsruhe, BW, Germany
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15 citations as recorded by crossref.
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- Global, spatially explicit modelling of zenith wet delay with XGBoost L. Crocetti et al. 10.1007/s00190-024-01829-2
- Real-time tropospheric delay map retrieval using sparse GNSS stations Z. Du et al. 10.1007/s10291-023-01554-x
- Precipitable water vapor estimation from Himawari-8/AHI observations using a stacking machine learning model Z. Du et al. 10.1016/j.atmosres.2024.107281
- An Improved Model for the Retrieval of Precipitable Water Vapor in All-Weather Conditions (RCMNT) Based on NIR and TIR Recordings of MODIS Y. Wang et al. 10.1109/TGRS.2024.3381750
- Troposphere Sensing Using Grazing‐Angle GNSS‐R Measurement From LEO Satellites Y. Wang 10.1029/2023GL106249
- A rapid ray tracing method to evaluate the performances of ERA5 and MERRA2 in retrieving global tropospheric delay M. Zhang et al. 10.1088/1361-6501/ad1707
- Retrieving Precipitable Water Vapor Over Land From Satellite Passive Microwave Radiometer Measurements Using Automated Machine Learning X. Xia et al. 10.1029/2023GL105197
- Monitoring urban heat island intensity based on GNSS tomography technique P. Xia et al. 10.1007/s00190-023-01804-3
- Real-Time Retrieval of All-Weather Weighted Mean Temperature From FengYun-4A Observations Z. Du et al. 10.1109/TGRS.2024.3382036
- The Novel Copernicus Global Dataset of Atmospheric Total Water Vapour Content with Related Uncertainties from GNSS Observations K. Rannat et al. 10.3390/rs15215150
- Assessment of the atmosphere profiles and derived parameters from fresh FengYun-3E and other common occultation systems S. Li et al. 10.1016/j.measurement.2024.114367
- An enhanced integrated water vapour dataset from more than 10 000 global ground-based GPS stations in 2020 P. Yuan et al. 10.5194/essd-15-723-2023
- The Novel Copernicus Global Dataset of Atmospheric Total Water Vapour Content with Related Uncertainties from GNSS Observations K. Rannat et al. 10.3390/rs15215150
13 citations as recorded by crossref.
- Unraveling the Accuracy Enigma: Investigating ZTD Data Precision in TUW-VMF3 and GFZ-VMF3 Products Using a Comprehensive Global GPS Dataset J. Li et al. 10.1109/TGRS.2024.3385228
- Novel Validation and Calibration Strategy for Total Precipitable Water Products of Fengyun-2 Geostationary Satellites Z. Du et al. 10.1109/TGRS.2023.3295091
- Global, spatially explicit modelling of zenith wet delay with XGBoost L. Crocetti et al. 10.1007/s00190-024-01829-2
- Real-time tropospheric delay map retrieval using sparse GNSS stations Z. Du et al. 10.1007/s10291-023-01554-x
- Precipitable water vapor estimation from Himawari-8/AHI observations using a stacking machine learning model Z. Du et al. 10.1016/j.atmosres.2024.107281
- An Improved Model for the Retrieval of Precipitable Water Vapor in All-Weather Conditions (RCMNT) Based on NIR and TIR Recordings of MODIS Y. Wang et al. 10.1109/TGRS.2024.3381750
- Troposphere Sensing Using Grazing‐Angle GNSS‐R Measurement From LEO Satellites Y. Wang 10.1029/2023GL106249
- A rapid ray tracing method to evaluate the performances of ERA5 and MERRA2 in retrieving global tropospheric delay M. Zhang et al. 10.1088/1361-6501/ad1707
- Retrieving Precipitable Water Vapor Over Land From Satellite Passive Microwave Radiometer Measurements Using Automated Machine Learning X. Xia et al. 10.1029/2023GL105197
- Monitoring urban heat island intensity based on GNSS tomography technique P. Xia et al. 10.1007/s00190-023-01804-3
- Real-Time Retrieval of All-Weather Weighted Mean Temperature From FengYun-4A Observations Z. Du et al. 10.1109/TGRS.2024.3382036
- The Novel Copernicus Global Dataset of Atmospheric Total Water Vapour Content with Related Uncertainties from GNSS Observations K. Rannat et al. 10.3390/rs15215150
- Assessment of the atmosphere profiles and derived parameters from fresh FengYun-3E and other common occultation systems S. Li et al. 10.1016/j.measurement.2024.114367
2 citations as recorded by crossref.
- An enhanced integrated water vapour dataset from more than 10 000 global ground-based GPS stations in 2020 P. Yuan et al. 10.5194/essd-15-723-2023
- The Novel Copernicus Global Dataset of Atmospheric Total Water Vapour Content with Related Uncertainties from GNSS Observations K. Rannat et al. 10.3390/rs15215150
Latest update: 23 Apr 2024
Short summary
We developed a 5 min global integrated water vapour (IWV) product from 12 552 ground-based GPS stations in 2020. It contains more than 1 billion IWV estimates. The dataset is an enhanced version of the existing operational GPS IWV dataset from the Nevada Geodetic Laboratory. The enhancement is reached by using accurate meteorological information from ERA5 for the GPS IWV retrieval with a significantly higher spatiotemporal resolution. The dataset is recommended for high-accuracy applications.
We developed a 5 min global integrated water vapour (IWV) product from 12 552 ground-based GPS...
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