Articles | Volume 14, issue 10
https://doi.org/10.5194/amt-14-6379-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-14-6379-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Oct 2021
Research article |
| 01 Oct 2021
| 01 Oct 2021
A new zenith hydrostatic delay model for real-time retrievals of GNSS-PWV
Longjiang Li
Jiangsu Key Laboratory of Resources and Environmental Information Engineering, China University of Mining and Technology, Xuzhou, 221116, China
School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, 221116, China
Suqin Wu
CORRESPONDING AUTHOR
Jiangsu Key Laboratory of Resources and Environmental Information Engineering, China University of Mining and Technology, Xuzhou, 221116, China
School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, 221116, China
Kefei Zhang
Jiangsu Key Laboratory of Resources and Environmental Information Engineering, China University of Mining and Technology, Xuzhou, 221116, China
School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, 221116, China
Satellite Positioning for Atmosphere, Climate and Environment (SPACE) Research Centre, RMIT University, Melbourne, Victoria, 3001, Australia
Xiaoming Wang
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 610209, China
Wang Li
Jiangsu Key Laboratory of Resources and Environmental Information Engineering, China University of Mining and Technology, Xuzhou, 221116, China
School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, 221116, China
Zhen Shen
Jiangsu Key Laboratory of Resources and Environmental Information Engineering, China University of Mining and Technology, Xuzhou, 221116, China
School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, 221116, China
Dantong Zhu
Jiangsu Key Laboratory of Resources and Environmental Information Engineering, China University of Mining and Technology, Xuzhou, 221116, China
School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, 221116, China
Qimin He
Jiangsu Key Laboratory of Resources and Environmental Information Engineering, China University of Mining and Technology, Xuzhou, 221116, China
School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, 221116, China
Moufeng Wan
Jiangsu Key Laboratory of Resources and Environmental Information Engineering, China University of Mining and Technology, Xuzhou, 221116, China
School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, 221116, China
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Cited
26 citations as recorded by crossref.
- A data-driven troposphere ZTD modeling method considering the distance of GNSS CORS to the coast X. Li et al. https://doi.org/10.1007/s10291-024-01735-2
- From GNSS Zenith Tropospheric Delay to Precipitable Water Vapor: Accuracy Assessment Using In-Situ and Reanalysis Meteorological Data Over China H. Wang et al. https://doi.org/10.1109/JSTARS.2025.3569930
- Evaluation of the Zenith Tropospheric Delay (ZTD) Derived from VMF3_FC and VMF3_OP Products Based on the CMONOC Data H. Zhang et al. https://doi.org/10.3390/atmos15070766
- Construction and evaluation of GNSS-derived two-dimensional PWV fields for precipitation analysis J. Zhang et al. https://doi.org/10.1016/j.asr.2026.05.101
- The New PWV Conversion Models Based on GNSS and Meteorological Elements in the China Region L. Li et al. https://doi.org/10.3390/atmos13111810
- Assessment of Global Forecast System–Derived Zenith Hydrostatic and Wet Delays and Systematic Bias Correction for Zenith Wet Delay over mainland China J. Li et al. https://doi.org/10.1016/j.asr.2026.05.063
- Developing a novel model to estimate zenith hydrostatic delay using surface meteorological data for GNSS-PWV retrieval Q. Zhang et al. https://doi.org/10.1016/j.asr.2026.03.057
- A Calibrated GPT3 (CGPT3) Model for the Site-Specific Zenith Hydrostatic Delay Estimation in the Chinese Mainland and Its Surrounding Areas J. Li et al. https://doi.org/10.3390/rs14246357
- Multi-Source Retrieval of Thermodynamic Profiles from an Integrated Ground-Based Remote Sensing System Using an EnKF1D-Var Framework Q. Zhang et al. https://doi.org/10.3390/rs17183133
- The New Improved ZHD and Weighted Mean Temperature Models Based on GNSS and Radiosonde Data Using GPT3 and Fourier Function L. Li et al. https://doi.org/10.3390/atmos13101648
- Refinement of GNSS-PWV conversion coefficients based on the Emardson model in the Tibetan Plateau alpine region J. Cheng et al. https://doi.org/10.1016/j.asr.2026.04.079
- Correlation of precipitable water vapor and heavy rainfall over Cyprus using GNSS sensors network D. Giannadaki et al. https://doi.org/10.3389/frsip.2025.1622256
- PWVFnet: A Short-Time Troposphere PWV Forecast Model Combined Empirical Models and Spatiotemporal ConvLSTM Network X. Ma et al. https://doi.org/10.1109/JSTARS.2025.3648114
- Case Study on Zenith Hydrostatic Delay Calibration Models for Forecast Vienna Mapping Function 3 and Their Impact on GNSS Precipitable Water Vapor Retrieval J. Li et al. https://doi.org/10.1109/TGRS.2026.3655097
- An Improved Method for Rainfall Forecast Based on GNSS-PWV L. Li et al. https://doi.org/10.3390/rs14174280
- A GNSS ZTD enhancement framework integrating physical constraints and hybrid neural networks H. Li et al. https://doi.org/10.1016/j.asr.2026.05.078
- A Deep Learning-Based Approach for Directly Retrieving GNSS Precipitable Water Vapor and Its Application in Typhoon Monitoring L. Huang et al. https://doi.org/10.1109/TGRS.2024.3479693
- Practical Performance Assessment of Water Vapor Monitoring Using BDS PPP-B2b Service L. Zhou et al. https://doi.org/10.3390/app15148033
- A new method for tropospheric tomography using GNSS and Fengyun-4A data M. Zhang et al. https://doi.org/10.1016/j.atmosres.2022.106460
- A GRU-Based Model Using GNSS-PWV and Meteorological Data for Forecasting Rainfalls L. Li et al. https://doi.org/10.1109/JSTARS.2025.3588524
- An Improved Tropospheric Tomographic Model Based on Artificial Neural Network M. Zhang et al. https://doi.org/10.1109/JSTARS.2023.3278302
- New mean tropospheric temperature models based on machine learning algorithms for Brazil D. Brum et al. https://doi.org/10.1080/01431161.2024.2334197
- A GNSS PWV filling and short-term forecasting framework fused hybrid neural network H. Li et al. https://doi.org/10.1016/j.atmosres.2025.108508
- A regional area-precise ZTD forecasting model combining GPT3 and GNSS based on an ensemble learning algorithm S. Chen et al. https://doi.org/10.1007/s10291-025-01936-3
- Diverse Techniques in Estimating Integrated Water Vapor for Calibration and Validation of Satellite Altimetry S. Mertikas et al. https://doi.org/10.3390/rs17162779
- An optimized BP neural network for modeling zenith tropospheric delay in the Chinese mainland using coupled particle swarm and genetic algorithm L. Huang et al. https://doi.org/10.1080/10095020.2024.2392701
26 citations as recorded by crossref.
- A data-driven troposphere ZTD modeling method considering the distance of GNSS CORS to the coast X. Li et al. https://doi.org/10.1007/s10291-024-01735-2
- From GNSS Zenith Tropospheric Delay to Precipitable Water Vapor: Accuracy Assessment Using In-Situ and Reanalysis Meteorological Data Over China H. Wang et al. https://doi.org/10.1109/JSTARS.2025.3569930
- Evaluation of the Zenith Tropospheric Delay (ZTD) Derived from VMF3_FC and VMF3_OP Products Based on the CMONOC Data H. Zhang et al. https://doi.org/10.3390/atmos15070766
- Construction and evaluation of GNSS-derived two-dimensional PWV fields for precipitation analysis J. Zhang et al. https://doi.org/10.1016/j.asr.2026.05.101
- The New PWV Conversion Models Based on GNSS and Meteorological Elements in the China Region L. Li et al. https://doi.org/10.3390/atmos13111810
- Assessment of Global Forecast System–Derived Zenith Hydrostatic and Wet Delays and Systematic Bias Correction for Zenith Wet Delay over mainland China J. Li et al. https://doi.org/10.1016/j.asr.2026.05.063
- Developing a novel model to estimate zenith hydrostatic delay using surface meteorological data for GNSS-PWV retrieval Q. Zhang et al. https://doi.org/10.1016/j.asr.2026.03.057
- A Calibrated GPT3 (CGPT3) Model for the Site-Specific Zenith Hydrostatic Delay Estimation in the Chinese Mainland and Its Surrounding Areas J. Li et al. https://doi.org/10.3390/rs14246357
- Multi-Source Retrieval of Thermodynamic Profiles from an Integrated Ground-Based Remote Sensing System Using an EnKF1D-Var Framework Q. Zhang et al. https://doi.org/10.3390/rs17183133
- The New Improved ZHD and Weighted Mean Temperature Models Based on GNSS and Radiosonde Data Using GPT3 and Fourier Function L. Li et al. https://doi.org/10.3390/atmos13101648
- Refinement of GNSS-PWV conversion coefficients based on the Emardson model in the Tibetan Plateau alpine region J. Cheng et al. https://doi.org/10.1016/j.asr.2026.04.079
- Correlation of precipitable water vapor and heavy rainfall over Cyprus using GNSS sensors network D. Giannadaki et al. https://doi.org/10.3389/frsip.2025.1622256
- PWVFnet: A Short-Time Troposphere PWV Forecast Model Combined Empirical Models and Spatiotemporal ConvLSTM Network X. Ma et al. https://doi.org/10.1109/JSTARS.2025.3648114
- Case Study on Zenith Hydrostatic Delay Calibration Models for Forecast Vienna Mapping Function 3 and Their Impact on GNSS Precipitable Water Vapor Retrieval J. Li et al. https://doi.org/10.1109/TGRS.2026.3655097
- An Improved Method for Rainfall Forecast Based on GNSS-PWV L. Li et al. https://doi.org/10.3390/rs14174280
- A GNSS ZTD enhancement framework integrating physical constraints and hybrid neural networks H. Li et al. https://doi.org/10.1016/j.asr.2026.05.078
- A Deep Learning-Based Approach for Directly Retrieving GNSS Precipitable Water Vapor and Its Application in Typhoon Monitoring L. Huang et al. https://doi.org/10.1109/TGRS.2024.3479693
- Practical Performance Assessment of Water Vapor Monitoring Using BDS PPP-B2b Service L. Zhou et al. https://doi.org/10.3390/app15148033
- A new method for tropospheric tomography using GNSS and Fengyun-4A data M. Zhang et al. https://doi.org/10.1016/j.atmosres.2022.106460
- A GRU-Based Model Using GNSS-PWV and Meteorological Data for Forecasting Rainfalls L. Li et al. https://doi.org/10.1109/JSTARS.2025.3588524
- An Improved Tropospheric Tomographic Model Based on Artificial Neural Network M. Zhang et al. https://doi.org/10.1109/JSTARS.2023.3278302
- New mean tropospheric temperature models based on machine learning algorithms for Brazil D. Brum et al. https://doi.org/10.1080/01431161.2024.2334197
- A GNSS PWV filling and short-term forecasting framework fused hybrid neural network H. Li et al. https://doi.org/10.1016/j.atmosres.2025.108508
- A regional area-precise ZTD forecasting model combining GPT3 and GNSS based on an ensemble learning algorithm S. Chen et al. https://doi.org/10.1007/s10291-025-01936-3
- Diverse Techniques in Estimating Integrated Water Vapor for Calibration and Validation of Satellite Altimetry S. Mertikas et al. https://doi.org/10.3390/rs17162779
- An optimized BP neural network for modeling zenith tropospheric delay in the Chinese mainland using coupled particle swarm and genetic algorithm L. Huang et al. https://doi.org/10.1080/10095020.2024.2392701
Saved (final revised paper)
Latest update: 14 Jul 2026
Short summary
The zenith hydrostatic delay (ZHD) derived from blind models are of low accuracy, especially in mid- and high-latitude regions. To address this issue, the ratio of the ZHD to zenith total delay (ZTD) is firstly investigated; then, based on the relationship between the ZHD and ZTD, a new ZHD model was developed using the back propagation artificial neural network (BP-ANN) method which took the ZTD as an input variable. The model outperforms blind models.
The zenith hydrostatic delay (ZHD) derived from blind models are of low accuracy, especially in...
