Articles | Volume 16, issue 24
https://doi.org/10.5194/gmd-16-7223-2023
© 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/gmd-16-7223-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model experiment description paper
| 
14 Dec 2023
Model experiment description paper |
| 14 Dec 2023
| 14 Dec 2023
A global grid model for the estimation of zenith tropospheric delay considering the variations at different altitudes
Liangke Huang
College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, 541006, China
Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin University of Technology, Guilin, 541006, China
Shengwei Lan
College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, 541006, China
Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin University of Technology, Guilin, 541006, China
Ge Zhu
CORRESPONDING AUTHOR
College of Surveying and Geo-informatics, Tongji University, Shanghai, 200092, China
Fade Chen
College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, 541006, China
Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin University of Technology, Guilin, 541006, China
Junyu Li
College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, 541006, China
Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin University of Technology, Guilin, 541006, China
Lilong Liu
College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, 541006, China
Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin University of Technology, Guilin, 541006, China
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Cited
23 citations as recorded by crossref.
- TransXLT: A novel ZTD prediction method with SASR-based data reconstruction . Shicheng Xie et al. https://doi.org/10.1016/j.isci.2025.112328
- How to predict the zenith tropospheric delay (ZTD): using the ensemble forecast data from different NWP K. Xu et al. https://doi.org/10.1016/j.asr.2025.12.040
- A new vertical reduction model for enhancing the interpolation accuracy of VMF1/VMF3 tropospheric delay products P. Sun et al. https://doi.org/10.5194/gmd-18-6167-2025
- Spatiotemporal Adaptive Correction for WRF Surface Parameters: A Fusion Approach of GNSS ZTD and Deep Attention Networks P. Wei et al. https://doi.org/10.1109/LGRS.2025.3626740
- Enhanced PPP-RTK in mountainous regions using a 3D grid-based elevation normalization model X. Li et al. https://doi.org/10.1007/s10291-026-02106-9
- An improved method for developing the precipitable water vapor vertical correction global grid model H. Li et al. https://doi.org/10.1016/j.atmosres.2024.107664
- Spatial-temporal variation of water vapor scale height and its impact factors in different climate zones of China R. Hao et al. https://doi.org/10.1016/j.asr.2024.05.019
- Evaluation of Tropospheric Delays over China from the High-Resolution Pangu-Weather Model at Multiple Forecast Scales S. Li et al. https://doi.org/10.3390/rs17183164
- Research on GNSS precipitable water vapor prediction based on deep learning considering regional space–time and nonlinear characteristics L. Chen et al. https://doi.org/10.1016/j.asr.2026.01.056
- 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
- An empirical model for the vertical correction of precipitable water vapor (PWV) over the Chinese mainland and surrounding areas considering hourly variations in the PWV lapse rate Y. Wang et al. https://doi.org/10.1016/j.jastp.2025.106676
- Calculation and evaluation of ZTD based on ensemble forecast data and AI-based weather model from ECMWF F. Yang et al. https://doi.org/10.1016/j.geog.2026.06.005
- A hybrid-grid global model for the estimation of atmospheric weighted mean temperature considering time-varying vertical adjustment rate in GNSS precipitable water vapour retrieval S. Xie et al. https://doi.org/10.5194/gmd-18-6987-2025
- A GRNN-Based Model for ERA5 PWV Adjustment with GNSS Observations Considering Seasonal and Geographic Variations H. Pang et al. https://doi.org/10.3390/rs16132424
- Trop_XS: an intelligent architecture for real-time wide-area ZTD corrections modeling driven by heterogeneous observations X. Gao et al. https://doi.org/10.1007/s10291-026-02124-7
- Establishing high-precision regional real-time ZTD vertical models using ERA5 model-level data and GNSS observations J. Zhang et al. https://doi.org/10.1016/j.asr.2025.09.091
- Improving the accuracy of zenith tropospheric delay in TUW-VMF3 and GFZ-VMF3 using feedforward neural networks J. Li et al. https://doi.org/10.1007/s10291-025-01914-9
- A New Grid Zenith Tropospheric Delay Model Considering Time-Varying Vertical Adjustment and Diurnal Variation over China J. Zhang et al. https://doi.org/10.3390/rs16112023
- Assessment of the three representative empirical models for zenith tropospheric delay (ZTD) using the CMONOC data D. Yuan et al. https://doi.org/10.1016/j.geog.2024.01.006
- A New PatchTST-ZTD Forecasting Model Based on Different Global Climates and Its Performance in PPP Z. Chen et al. https://doi.org/10.1109/JSTARS.2025.3650203
- Fusion of numerical meteorological data to mitigate residual tropospheric error in GNSS RTK for large bridge monitoring in mountainous areas G. Liu et al. https://doi.org/10.1016/j.measurement.2024.116442
- 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 Refined Spatiotemporal ZTD Model of the Chinese Region Based on ERA and GNSS Data Y. Fan et al. https://doi.org/10.3390/rs16234515
23 citations as recorded by crossref.
- TransXLT: A novel ZTD prediction method with SASR-based data reconstruction . Shicheng Xie et al. https://doi.org/10.1016/j.isci.2025.112328
- How to predict the zenith tropospheric delay (ZTD): using the ensemble forecast data from different NWP K. Xu et al. https://doi.org/10.1016/j.asr.2025.12.040
- A new vertical reduction model for enhancing the interpolation accuracy of VMF1/VMF3 tropospheric delay products P. Sun et al. https://doi.org/10.5194/gmd-18-6167-2025
- Spatiotemporal Adaptive Correction for WRF Surface Parameters: A Fusion Approach of GNSS ZTD and Deep Attention Networks P. Wei et al. https://doi.org/10.1109/LGRS.2025.3626740
- Enhanced PPP-RTK in mountainous regions using a 3D grid-based elevation normalization model X. Li et al. https://doi.org/10.1007/s10291-026-02106-9
- An improved method for developing the precipitable water vapor vertical correction global grid model H. Li et al. https://doi.org/10.1016/j.atmosres.2024.107664
- Spatial-temporal variation of water vapor scale height and its impact factors in different climate zones of China R. Hao et al. https://doi.org/10.1016/j.asr.2024.05.019
- Evaluation of Tropospheric Delays over China from the High-Resolution Pangu-Weather Model at Multiple Forecast Scales S. Li et al. https://doi.org/10.3390/rs17183164
- Research on GNSS precipitable water vapor prediction based on deep learning considering regional space–time and nonlinear characteristics L. Chen et al. https://doi.org/10.1016/j.asr.2026.01.056
- 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
- An empirical model for the vertical correction of precipitable water vapor (PWV) over the Chinese mainland and surrounding areas considering hourly variations in the PWV lapse rate Y. Wang et al. https://doi.org/10.1016/j.jastp.2025.106676
- Calculation and evaluation of ZTD based on ensemble forecast data and AI-based weather model from ECMWF F. Yang et al. https://doi.org/10.1016/j.geog.2026.06.005
- A hybrid-grid global model for the estimation of atmospheric weighted mean temperature considering time-varying vertical adjustment rate in GNSS precipitable water vapour retrieval S. Xie et al. https://doi.org/10.5194/gmd-18-6987-2025
- A GRNN-Based Model for ERA5 PWV Adjustment with GNSS Observations Considering Seasonal and Geographic Variations H. Pang et al. https://doi.org/10.3390/rs16132424
- Trop_XS: an intelligent architecture for real-time wide-area ZTD corrections modeling driven by heterogeneous observations X. Gao et al. https://doi.org/10.1007/s10291-026-02124-7
- Establishing high-precision regional real-time ZTD vertical models using ERA5 model-level data and GNSS observations J. Zhang et al. https://doi.org/10.1016/j.asr.2025.09.091
- Improving the accuracy of zenith tropospheric delay in TUW-VMF3 and GFZ-VMF3 using feedforward neural networks J. Li et al. https://doi.org/10.1007/s10291-025-01914-9
- A New Grid Zenith Tropospheric Delay Model Considering Time-Varying Vertical Adjustment and Diurnal Variation over China J. Zhang et al. https://doi.org/10.3390/rs16112023
- Assessment of the three representative empirical models for zenith tropospheric delay (ZTD) using the CMONOC data D. Yuan et al. https://doi.org/10.1016/j.geog.2024.01.006
- A New PatchTST-ZTD Forecasting Model Based on Different Global Climates and Its Performance in PPP Z. Chen et al. https://doi.org/10.1109/JSTARS.2025.3650203
- Fusion of numerical meteorological data to mitigate residual tropospheric error in GNSS RTK for large bridge monitoring in mountainous areas G. Liu et al. https://doi.org/10.1016/j.measurement.2024.116442
- 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 Refined Spatiotemporal ZTD Model of the Chinese Region Based on ERA and GNSS Data Y. Fan et al. https://doi.org/10.3390/rs16234515
Saved (final revised paper)
Latest update: 17 Jul 2026
Short summary
The existing zenith tropospheric delay (ZTD) models have limitations such as using a single fitting function, neglecting daily cycle variations, and relying on only one resolution grid data point for modeling. This model considers the daily cycle variation and latitude factor of ZTD, using the sliding window algorithm based on ERA5 atmospheric reanalysis data. The ZTD data from 545 radiosonde stations and MERRA-2 atmospheric reanalysis data are used to validate the accuracy of the GGZTD-P model.
The existing zenith tropospheric delay (ZTD) models have limitations such as using a single...
