Articles | Volume 9, issue 10
https://doi.org/10.5194/amt-9-5249-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/amt-9-5249-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
28 Oct 2016
Research article |
| 28 Oct 2016
Assessing the performance of troposphere tomographic modeling using multi-source water vapor data during Hong Kong's rainy season from May to October 2013
Biyan Chen
Department of Land Surveying & Geo-Informatics, Hong Kong Polytechnic
University, Hong Kong, China
Zhizhao Liu
CORRESPONDING AUTHOR
Department of Land Surveying & Geo-Informatics, Hong Kong Polytechnic
University, Hong Kong, China
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Cited
20 citations as recorded by crossref.
- An improved GNSS remote sensing technique for 3D distribution of tropospheric water vapor A. Long et al. 10.1002/met.2136
- A Refined Tomographic Window for GNSS-Derived Water Vapor Tomography Y. Yao et al. 10.3390/rs12182999
- A Method to Improve the Distribution of Observations in GNSS Water Vapor Tomography F. Yang et al. 10.3390/s18082526
- An optimal tropospheric tomography approach with the support of an auxiliary area Q. Zhao et al. 10.5194/angeo-36-1037-2018
- 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
- GNSS ground-based tomography: state-of-the-art and technological challenges S. Saxena & R. Dwivedi 10.1080/01431161.2023.2247526
- Assessments of GMI-Derived Precipitable Water Vapor Products over the South and East China Seas Using Radiosonde and GNSS B. Chen et al. 10.1155/2018/7161328
- An improved GNSS tropospheric tomographic model with an extended region and combining virtual signals S. Liu et al. 10.1016/j.atmosres.2023.106703
- Rapid troposphere tomography using adaptive simultaneous iterative reconstruction technique W. Zhang et al. 10.1007/s00190-020-01386-4
- 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. 10.1016/j.atmosres.2021.105893
- Conventional and neural network-based water vapor density model for GNSS troposphere tomography C. Liu et al. 10.1007/s10291-021-01188-x
- Monitoring urban heat island intensity based on GNSS tomography technique P. Xia et al. 10.1007/s00190-023-01804-3
- An improved troposphere tomographic approach considering the signals coming from the side face of the tomographic area Q. Zhao & Y. Yao 10.5194/angeo-35-87-2017
- Reconstruction of Wet Refractivity Field Using an Improved Parameterized Tropospheric Tomographic Technique B. Chen et al. 10.3390/rs12183034
- Development of a New Vertical Water Vapor Model for GNSS Water Vapor Tomography M. Wan et al. 10.3390/rs14225656
- 3-D Water Vapor Tomography in Wuhan from GPS, BDS and GLONASS Observations Z. Dong & S. Jin 10.3390/rs10010062
- Real-Time Precise Point Positioning Using Tomographic Wet Refractivity Fields W. Yu et al. 10.3390/rs10060928
- A GPS water vapour tomography method based on a genetic algorithm F. Yang et al. 10.5194/amt-13-355-2020
- An Improved Tropospheric Tomographic Model Based on Artificial Neural Network M. Zhang et al. 10.1109/JSTARS.2023.3278302
- 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
20 citations as recorded by crossref.
- An improved GNSS remote sensing technique for 3D distribution of tropospheric water vapor A. Long et al. 10.1002/met.2136
- A Refined Tomographic Window for GNSS-Derived Water Vapor Tomography Y. Yao et al. 10.3390/rs12182999
- A Method to Improve the Distribution of Observations in GNSS Water Vapor Tomography F. Yang et al. 10.3390/s18082526
- An optimal tropospheric tomography approach with the support of an auxiliary area Q. Zhao et al. 10.5194/angeo-36-1037-2018
- 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
- GNSS ground-based tomography: state-of-the-art and technological challenges S. Saxena & R. Dwivedi 10.1080/01431161.2023.2247526
- Assessments of GMI-Derived Precipitable Water Vapor Products over the South and East China Seas Using Radiosonde and GNSS B. Chen et al. 10.1155/2018/7161328
- An improved GNSS tropospheric tomographic model with an extended region and combining virtual signals S. Liu et al. 10.1016/j.atmosres.2023.106703
- Rapid troposphere tomography using adaptive simultaneous iterative reconstruction technique W. Zhang et al. 10.1007/s00190-020-01386-4
- 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. 10.1016/j.atmosres.2021.105893
- Conventional and neural network-based water vapor density model for GNSS troposphere tomography C. Liu et al. 10.1007/s10291-021-01188-x
- Monitoring urban heat island intensity based on GNSS tomography technique P. Xia et al. 10.1007/s00190-023-01804-3
- An improved troposphere tomographic approach considering the signals coming from the side face of the tomographic area Q. Zhao & Y. Yao 10.5194/angeo-35-87-2017
- Reconstruction of Wet Refractivity Field Using an Improved Parameterized Tropospheric Tomographic Technique B. Chen et al. 10.3390/rs12183034
- Development of a New Vertical Water Vapor Model for GNSS Water Vapor Tomography M. Wan et al. 10.3390/rs14225656
- 3-D Water Vapor Tomography in Wuhan from GPS, BDS and GLONASS Observations Z. Dong & S. Jin 10.3390/rs10010062
- Real-Time Precise Point Positioning Using Tomographic Wet Refractivity Fields W. Yu et al. 10.3390/rs10060928
- A GPS water vapour tomography method based on a genetic algorithm F. Yang et al. 10.5194/amt-13-355-2020
- An Improved Tropospheric Tomographic Model Based on Artificial Neural Network M. Zhang et al. 10.1109/JSTARS.2023.3278302
- 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
Latest update: 15 Apr 2024
Short summary
A multi-source water vapor tomography model is developed using GPS (Global Positioning System), radiosonde, WVR (water vapor radiometer), NWP (numerical weather prediction), AERONET (AErosol RObotic NETwork) sunphotometer and synoptic stations' data. Results show that the assimilation of multi-source data can increase the quality of the tomographic solution. Evaluation shows that the tomography model is robust during heavy rain conditions, and it can contribute to severe weather forecasting.
A multi-source water vapor tomography model is developed using GPS (Global Positioning System),...
