Articles | Volume 14, issue 12
https://doi.org/10.5194/amt-14-7821-2021
© 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-14-7821-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Dec 2021
Research article |
| 16 Dec 2021
| 16 Dec 2021
Global evaluation of the precipitable-water-vapor product from MERSI-II (Medium Resolution Spectral Imager) on board the Fengyun-3D satellite
Wengang Zhang
Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy Rain, China Meteorological Administration, Wuhan 430205, China
Three Gorges National Climatic Station, Yichang 443099, China
Ling Wang
CORRESPONDING AUTHOR
National Satellite Meteorological Center, China Meteorological
Administration, Beijing 100081, China
Yang Yu
Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy Rain, China Meteorological Administration, Wuhan 430205, China
Guirong Xu
Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy Rain, China Meteorological Administration, Wuhan 430205, China
Xiuqing Hu
National Satellite Meteorological Center, China Meteorological
Administration, Beijing 100081, China
Zhikang Fu
Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy Rain, China Meteorological Administration, Wuhan 430205, China
Chunguang Cui
Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy Rain, China Meteorological Administration, Wuhan 430205, China
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Cited
16 citations as recorded by crossref.
- 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 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
- 基于机器学习的风云三号中分辨率光谱成像仪-II陆地气溶胶产品偏差校正方法（特邀） 李. Li Mengnan et al. https://doi.org/10.3788/AOS252008
- Accuracy evolution of precipitable water vapor products from Medium Resolution Spectral Imager (MERSI) on Fengyun-3 series satellites H. Sun et al. https://doi.org/10.1016/j.asr.2026.06.028
- Evaluation of oceanic precipitable water vapor products from Microwave Radiation Imager (MWRI) onboard the Fengyun-3D satellite Y. Zhou et al. https://doi.org/10.1016/j.asr.2023.02.033
- Novel Validation and Calibration Strategy for Total Precipitable Water Products of Fengyun-2 Geostationary Satellites Z. Du et al. https://doi.org/10.1109/TGRS.2023.3295091
- Evolution of Atmospheric Water Vapor and Cloud Liquid Water During Non- and Pre-Precipitation Conditions over the Middle Yangtze River Basin in the Warm Season W. Zhang et al. https://doi.org/10.3390/rs18101642
- Improving Fengyun-3D satellite atmospheric precipitable water vapor products through machine learning-based post-processing correction M. Li et al. https://doi.org/10.1016/j.atmosres.2025.108133
- Constructing High-Precision and Spatial Resolution Precipitable Water Vapor Product Using Multiple Fusion Models Y. Zhou et al. https://doi.org/10.1109/JSTARS.2024.3459051
- Comprehensive evaluation of the precipitable water vapor products of Fengyun satellites via GNSS data over mainland China Y. Zhou et al. https://doi.org/10.1016/j.atmosres.2024.107235
- Precipitable water vapor estimation from Himawari-8/AHI observations using a stacking machine learning model Z. Du et al. https://doi.org/10.1016/j.atmosres.2024.107281
- An Empirical Grid Model for Precipitable Water Vapor X. Wang et al. https://doi.org/10.3390/rs14236174
- Global total precipitable water variations and trends over the period 1958–2021 N. Wan et al. https://doi.org/10.5194/hess-28-2123-2024
- Independent Validation of Total Precipitable Water From Morphed Integrated Microwave Imagery at CIMSS: A Global Study Using GNSS and Radiosonde Measurements J. Xu et al. https://doi.org/10.1109/TGRS.2026.3656168
- Machine Learning-Based Calibration Study of MERSI-II Atmospheric Precipitable Water Vapor Product M. Li https://doi.org/10.54097/xnwft166
- Research on generating high-precision and high-resolution atmospheric PWV products over mainland China based on multi-source data fusion H. Li et al. https://doi.org/10.1016/j.asr.2026.09.052
16 citations as recorded by crossref.
- 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 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
- 基于机器学习的风云三号中分辨率光谱成像仪-II陆地气溶胶产品偏差校正方法（特邀） 李. Li Mengnan et al. https://doi.org/10.3788/AOS252008
- Accuracy evolution of precipitable water vapor products from Medium Resolution Spectral Imager (MERSI) on Fengyun-3 series satellites H. Sun et al. https://doi.org/10.1016/j.asr.2026.06.028
- Evaluation of oceanic precipitable water vapor products from Microwave Radiation Imager (MWRI) onboard the Fengyun-3D satellite Y. Zhou et al. https://doi.org/10.1016/j.asr.2023.02.033
- Novel Validation and Calibration Strategy for Total Precipitable Water Products of Fengyun-2 Geostationary Satellites Z. Du et al. https://doi.org/10.1109/TGRS.2023.3295091
- Evolution of Atmospheric Water Vapor and Cloud Liquid Water During Non- and Pre-Precipitation Conditions over the Middle Yangtze River Basin in the Warm Season W. Zhang et al. https://doi.org/10.3390/rs18101642
- Improving Fengyun-3D satellite atmospheric precipitable water vapor products through machine learning-based post-processing correction M. Li et al. https://doi.org/10.1016/j.atmosres.2025.108133
- Constructing High-Precision and Spatial Resolution Precipitable Water Vapor Product Using Multiple Fusion Models Y. Zhou et al. https://doi.org/10.1109/JSTARS.2024.3459051
- Comprehensive evaluation of the precipitable water vapor products of Fengyun satellites via GNSS data over mainland China Y. Zhou et al. https://doi.org/10.1016/j.atmosres.2024.107235
- Precipitable water vapor estimation from Himawari-8/AHI observations using a stacking machine learning model Z. Du et al. https://doi.org/10.1016/j.atmosres.2024.107281
- An Empirical Grid Model for Precipitable Water Vapor X. Wang et al. https://doi.org/10.3390/rs14236174
- Global total precipitable water variations and trends over the period 1958–2021 N. Wan et al. https://doi.org/10.5194/hess-28-2123-2024
- Independent Validation of Total Precipitable Water From Morphed Integrated Microwave Imagery at CIMSS: A Global Study Using GNSS and Radiosonde Measurements J. Xu et al. https://doi.org/10.1109/TGRS.2026.3656168
- Machine Learning-Based Calibration Study of MERSI-II Atmospheric Precipitable Water Vapor Product M. Li https://doi.org/10.54097/xnwft166
- Research on generating high-precision and high-resolution atmospheric PWV products over mainland China based on multi-source data fusion H. Li et al. https://doi.org/10.1016/j.asr.2026.09.052
Saved (final revised paper)
Latest update: 05 Nov 2026
Short summary
Global precipitable water vapor (PWV) derived from MERSI-II (Medium Resolution Spectral Imager) is compared with PWV from the Integrated Global Radiosonde Archive (IGRA). Our results show a good agreement between PWV from MERSI-II and IGRA and that MERSI-II PWV is slightly underestimated on the whole, especially in summer. The bias between MERSI-II and IGRA grows with a larger spatial distance between the footprint of the satellite and the IGRA station, as well as increasing PWV.
Global precipitable water vapor (PWV) derived from MERSI-II (Medium Resolution Spectral Imager)...
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