Decadal All-sky Terrestrial Precipitable Water Vapor Dataset from FengYun Microwave Imagers

Abstract Precipitable water vapor (PWV) is a key component of the Earth’s climate system, playing a vital role in weather, climate, and hydrological cycling. Passive microwave remote sensing offers a promising approach to measure all-sky PWV, though it remains challenging over land. Building on our previous development of a machine learning algorithm, we have created a global terrestrial PWV dataset using measurements from the MicroWave Radiation Imager (MWRI) aboard three FY-3 satellite series (FY-3B, FY-3C and FY-3D). The dataset spans from 2012 to 2020 at a spatial resolution of 0.25° × 0.25°. It was validated against SuomiNet GPS and IGRA2 (Integrated Global Radiosonde Archive Version 2) PWV products, achieving root-mean-square errors (RMSEs) of 4.47 and 3.89 mm, respectively, with RMSE values ranging from 2.90 to 5.49 mm across diverse surface conditions. As an all-weather PWV product with high-precision, the MWRI PWV dataset addresses gaps in global passive microwave-based terrestrial PWV observations, offering significant value for atmospheric research, climate modeling, hydrological studies, and beyond.

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Publication Details

Journal
Advances in Atmospheric Sciences
Published
2025-12-13
DOI
https://doi.org/10.1007/s00376-025-5101-6
Primary Topic
Precipitation Measurement and Analysis
Type
article
Field-Weighted Citation Impact
0.00

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article

Decadal All-sky Terrestrial Precipitable Water Vapor Dataset from FengYun Microwave Imagers

Rubin Jiang, Xiangao Xia, Min Min, Peng Zhang et al.
Advances in Atmospheric Sciences
Precipitation Measurement and Analysis
article

Decadal All-sky Terrestrial Precipitable Water Vapor Dataset from FengYun Microwave Imagers

Rubin Jiang, Xiangao Xia, Min Min, Peng Zhang, Shengli Wu, Xinran Xia
article en

Abstract

Abstract Precipitable water vapor (PWV) is a key component of the Earth’s climate system, playing a vital role in weather, climate, and hydrological cycling. Passive microwave remote sensing offers a promising approach to measure all-sky PWV, though it remains challenging over land. Building on our previous development of a machine learning algorithm, we have created a global terrestrial PWV dataset using measurements from the MicroWave Radiation Imager (MWRI) aboard three FY-3 satellite series (FY-3B, FY-3C and FY-3D). The dataset spans from 2012 to 2020 at a spatial resolution of 0.25° × 0.25°. It was validated against SuomiNet GPS and IGRA2 (Integrated Global Radiosonde Archive Version 2) PWV products, achieving root-mean-square errors (RMSEs) of 4.47 and 3.89 mm, respectively, with RMSE values ranging from 2.90 to 5.49 mm across diverse surface conditions. As an all-weather PWV product with high-precision, the MWRI PWV dataset addresses gaps in global passive microwave-based terrestrial PWV observations, offering significant value for atmospheric research, climate modeling, hydrological studies, and beyond.

Advances in Atmospheric SciencesVol. 43(3)
China Meteorological Administration (CN), Sun Yat-sen University (CN), Chinese Academy of Sciences (CN), Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) (CN), Space Engineering University (CN), Institute of Atmospheric Physics (CN), University of Chinese Academy of Sciences (CN), Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (CN)
Magee-Womens Research Institute, National Natural Science Foundation of China, National Centers for Environmental Information
Openalex Percentile: Top 56%
Precipitation Measurement and Analysis
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