Articles | Volume 11, issue 9
https://doi.org/10.5194/amt-11-5153-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/amt-11-5153-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
11 Sep 2018
Research article |
| 11 Sep 2018
| 11 Sep 2018
Constructing a precipitable water vapor map from regional GNSS network observations without collocated meteorological data for weather forecasting
School of Geosciences and Info-Physics, Central South University,
Changsha, Hunan, China
Key Laboratory of Precise Engineering
Surveying and Deformation Disaster Monitoring of Hunan Province,
Changsha, Hunan, China
Key Laboratory of Metallogenic
Prediction of Nonferrous Metals and Geological Environment Monitoring
Ministry of Education, School of Geosciences and Info-Physics, Central South
University, Changsha, Hunan, China
Wujiao Dai
School of Geosciences and Info-Physics, Central South University,
Changsha, Hunan, China
Key Laboratory of Precise Engineering
Surveying and Deformation Disaster Monitoring of Hunan Province,
Changsha, Hunan, China
Key Laboratory of Metallogenic
Prediction of Nonferrous Metals and Geological Environment Monitoring
Ministry of Education, School of Geosciences and Info-Physics, Central South
University, Changsha, Hunan, China
Zhizhao Liu
Department of Land Surveying
and Geo-Informatics, Hong Kong Polytechnic University, Hong Kong, China
School of Geosciences and Info-Physics, Central South University,
Changsha, Hunan, China
Key Laboratory of Metallogenic
Prediction of Nonferrous Metals and Geological Environment Monitoring
Ministry of Education, School of Geosciences and Info-Physics, Central South
University, Changsha, Hunan, China
Cuilin Kuang
School of Geosciences and Info-Physics, Central South University,
Changsha, Hunan, China
Key Laboratory of Precise Engineering
Surveying and Deformation Disaster Monitoring of Hunan Province,
Changsha, Hunan, China
Key Laboratory of Metallogenic
Prediction of Nonferrous Metals and Geological Environment Monitoring
Ministry of Education, School of Geosciences and Info-Physics, Central South
University, Changsha, Hunan, China
Minsi Ao
Hunan Province Mapping and Science and Technology Investigation
Institute, Changsha, Hunan, China
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37 citations as recorded by crossref.
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- Development and Assessment of an ALLSSA‐Based Atmospheric Weighted Mean Temperature Model With High Time Resolution for GNSS Precipitable Water Retrieval Y. Ma et al. 10.1029/2021EA002089
- Determination of Weighted Mean Temperature (Tm) Lapse Rate and Assessment of Its Impact on Tm Calculation F. Yang et al. 10.1109/ACCESS.2019.2946916
- Tomographic Reconstruction of Water Vapor Density Fields From the Integration of GNSS Observations and Fengyun-4A Products B. Chen et al. 10.1109/TGRS.2023.3239392
- A Deep Learning-Based Approach for Directly Retrieving GNSS Precipitable Water Vapor and Its Application in Typhoon Monitoring L. Huang et al. 10.1109/TGRS.2024.3479693
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- A Model for the Relationship between Rainfall, GNSS-Derived Integrated Water Vapour, and CAPE in the Eastern Central Andes M. Ramezani Ziarani et al. 10.3390/rs13183788
- An optimal calibration method for MODIS precipitable water vapor using GNSS observations Y. Wang et al. 10.1016/j.atmosres.2024.107591
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- A novel ENSO monitoring index and its potential for drought application Q. Zhao et al. 10.1016/j.jastp.2021.105762
- Improving the capability of water vapor retrieval from Landsat 8 using ensemble machine learning Y. Wu et al. 10.1016/j.jag.2023.103407
- Estimation of GPS-based atmospheric precipitable water vapour over Dehradun A. Srivastava 10.1007/s12040-021-01692-1
- Application of GPS PWV for rainfall detection using ERA5 datasets over the Indian IGS locations A. Srivastava 10.1007/s12040-024-02286-3
- Improving the accuracy and spatial resolution of precipitable water vapor dataset using a neural network-based downscaling method X. Ma et al. 10.1016/j.atmosenv.2021.118850
- Precipitable water vapor fusion method based on artificial neural network Q. Zhao et al. 10.1016/j.asr.2022.04.031
- Atmospheric precipitable water vapor and its correlation with clear-sky infrared temperature observations V. Kelsey et al. 10.5194/amt-15-1563-2022
- Evaluating Aerosol Optical Depth From Himawari‐8 With Sun Photometer Network W. Wang et al. 10.1029/2018JD028599
- Precipitable Water Vapor Converted from GNSS-ZTD and ERA5 Datasets for the Monitoring of Tropical Cyclones Q. He et al. 10.1109/ACCESS.2020.2991094
- A new Egyptian Grid Weighted Mean Temperature (EGWMT) model using hourly ERA5 reanalysis data in GNSS PWV retrieval R. Sleem et al. 10.1038/s41598-024-64132-2
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- High temporal resolution global PWV dataset of 2005–2016 by using a neural network approach to determine the mean temperature of the atmosphere P. Yang et al. 10.1016/j.asr.2021.01.054
- Machine Learning‐Based Model for Real‐Time GNSS Precipitable Water Vapor Sensing Y. Zheng et al. 10.1029/2021GL096408
- An improved method for developing the precipitable water vapor vertical correction global grid model H. Li et al. 10.1016/j.atmosres.2024.107664
- Preliminary Results on Tropospheric ZTD Estimation by Smartphone L. Benvenuto et al. 10.3390/rs13224567
- Modified atmospheric pressure extrapolation model using ERA5 for geodetic applications H. Su et al. 10.1007/s10291-021-01153-8
- Reconstruction of Wet Refractivity Field Using an Improved Parameterized Tropospheric Tomographic Technique B. Chen et al. 10.3390/rs12183034
- The GNSS PWV retrieval using non-observation meteorological parameters based on ERA5 and its relation with precipitation W. Yang et al. 10.1016/j.geog.2023.09.002
- Spatiotemporal characteristics of GNSS-derived precipitable water vapor during heavy rainfall events in Guilin, China L. Huang et al. 10.1186/s43020-021-00046-y
- Assessing the performance of GPS tomography at retrieving water vapour fields during landfalling atmospheric rivers over southern California B. Chen et al. 10.1002/met.1943
- Stratified rainfall forecast method using GNSS observations Y. Liu et al. 10.1016/j.atmosres.2022.106421
- Revealing the water vapor transport during the Henan “7.20” heavy rainstorm based on ERA5 and Real-Time GNSS Y. Wu et al. 10.1016/j.ejrs.2024.02.004
37 citations as recorded by crossref.
- Investigating the Influence of Water Vapor on Heavy Rainfall Events in the Southern Korean Peninsula Y. Kim et al. 10.3390/rs15020340
- Copula-based modeling of dependence structure in geodesy and GNSS applications: case study for zenith tropospheric delay in complex terrain R. Mousavian et al. 10.1007/s10291-020-01044-4
- Development and Assessment of an ALLSSA‐Based Atmospheric Weighted Mean Temperature Model With High Time Resolution for GNSS Precipitable Water Retrieval Y. Ma et al. 10.1029/2021EA002089
- Determination of Weighted Mean Temperature (Tm) Lapse Rate and Assessment of Its Impact on Tm Calculation F. Yang et al. 10.1109/ACCESS.2019.2946916
- Tomographic Reconstruction of Water Vapor Density Fields From the Integration of GNSS Observations and Fengyun-4A Products B. Chen et al. 10.1109/TGRS.2023.3239392
- A Deep Learning-Based Approach for Directly Retrieving GNSS Precipitable Water Vapor and Its Application in Typhoon Monitoring L. Huang et al. 10.1109/TGRS.2024.3479693
- GEODESY, CARTOGRAPHY AND AERIAL PHOTOGRAPHY M. Paziak 10.23939/istcgcap2019.01.020
- PWV Retrieval Performance Evaluation for the Fresh BDS‐3 With Multisource Data N. Jiang et al. 10.1029/2023EA002923
- A New Method for Refining the GNSS-Derived Precipitable Water Vapor Map C. Liu et al. 10.3390/s19030698
- A Model for the Relationship between Rainfall, GNSS-Derived Integrated Water Vapour, and CAPE in the Eastern Central Andes M. Ramezani Ziarani et al. 10.3390/rs13183788
- An optimal calibration method for MODIS precipitable water vapor using GNSS observations Y. Wang et al. 10.1016/j.atmosres.2024.107591
- A Global Assessment of Precipitable Water Vapor Derived From GNSS Zenith Tropospheric Delays With ERA5, NCEP FNL, and NCEP GFS Products B. Chen et al. 10.1029/2021EA001796
- An improved weighted mean temperature (Tm) model based on GPT2w with Tm lapse rate F. Yang et al. 10.1007/s10291-020-0953-9
- An ERA5 based local modelling of weighted mean temperature over hilly region in India for improved spatiotemporal analysis of extreme weather event using GNSS PWV S. Saxena & R. Dwivedi 10.1016/j.asr.2023.06.017
- Assimilating Sentinel‐3 All‐Sky PWV Retrievals to Improve the WRF Forecasting Performance Over the South China Y. Gong et al. 10.1029/2022JD037979
- A novel ENSO monitoring index and its potential for drought application Q. Zhao et al. 10.1016/j.jastp.2021.105762
- Improving the capability of water vapor retrieval from Landsat 8 using ensemble machine learning Y. Wu et al. 10.1016/j.jag.2023.103407
- Estimation of GPS-based atmospheric precipitable water vapour over Dehradun A. Srivastava 10.1007/s12040-021-01692-1
- Application of GPS PWV for rainfall detection using ERA5 datasets over the Indian IGS locations A. Srivastava 10.1007/s12040-024-02286-3
- Improving the accuracy and spatial resolution of precipitable water vapor dataset using a neural network-based downscaling method X. Ma et al. 10.1016/j.atmosenv.2021.118850
- Precipitable water vapor fusion method based on artificial neural network Q. Zhao et al. 10.1016/j.asr.2022.04.031
- Atmospheric precipitable water vapor and its correlation with clear-sky infrared temperature observations V. Kelsey et al. 10.5194/amt-15-1563-2022
- Evaluating Aerosol Optical Depth From Himawari‐8 With Sun Photometer Network W. Wang et al. 10.1029/2018JD028599
- Precipitable Water Vapor Converted from GNSS-ZTD and ERA5 Datasets for the Monitoring of Tropical Cyclones Q. He et al. 10.1109/ACCESS.2020.2991094
- A new Egyptian Grid Weighted Mean Temperature (EGWMT) model using hourly ERA5 reanalysis data in GNSS PWV retrieval R. Sleem et al. 10.1038/s41598-024-64132-2
- A new global grid-based weighted mean temperature model considering vertical nonlinear variation P. Sun et al. 10.5194/amt-14-2529-2021
- High temporal resolution global PWV dataset of 2005–2016 by using a neural network approach to determine the mean temperature of the atmosphere P. Yang et al. 10.1016/j.asr.2021.01.054
- Machine Learning‐Based Model for Real‐Time GNSS Precipitable Water Vapor Sensing Y. Zheng et al. 10.1029/2021GL096408
- An improved method for developing the precipitable water vapor vertical correction global grid model H. Li et al. 10.1016/j.atmosres.2024.107664
- Preliminary Results on Tropospheric ZTD Estimation by Smartphone L. Benvenuto et al. 10.3390/rs13224567
- Modified atmospheric pressure extrapolation model using ERA5 for geodetic applications H. Su et al. 10.1007/s10291-021-01153-8
- Reconstruction of Wet Refractivity Field Using an Improved Parameterized Tropospheric Tomographic Technique B. Chen et al. 10.3390/rs12183034
- The GNSS PWV retrieval using non-observation meteorological parameters based on ERA5 and its relation with precipitation W. Yang et al. 10.1016/j.geog.2023.09.002
- Spatiotemporal characteristics of GNSS-derived precipitable water vapor during heavy rainfall events in Guilin, China L. Huang et al. 10.1186/s43020-021-00046-y
- Assessing the performance of GPS tomography at retrieving water vapour fields during landfalling atmospheric rivers over southern California B. Chen et al. 10.1002/met.1943
- Stratified rainfall forecast method using GNSS observations Y. Liu et al. 10.1016/j.atmosres.2022.106421
- Revealing the water vapor transport during the Henan “7.20” heavy rainstorm based on ERA5 and Real-Time GNSS Y. Wu et al. 10.1016/j.ejrs.2024.02.004
Latest update: 14 Dec 2024
Short summary
The lack of collocated meteorological data at GNSS stations makes it difficult to take full advantage of GNSS observations for weather studies. This research demonstrates the potentials of retrieving accurate PWV from GNSS using adjacent synoptic data and generating high-quality PWV maps from the GNSS network for weather prediction in near-real time. Results also demonstrate that it's possible to reveal the moisture advection, transportation and convergence during heavy rainfalls using PWV maps.
The lack of collocated meteorological data at GNSS stations makes it difficult to take full...
