A global GNSS climate data record from 5085 stations spanning up to 22 years

Abstract. This work presents a comprehensive global GNSS climate data record derived from 5085 stations, spanning up to a 22-year period 2000–2021. The dataset was generated using the state-of-the-art processing methodologies and precise products from the International GNSS Service (IGS) Repro-3 initiative. It includes high-quality hourly estimates of Zenith Total Delay (ZTD) and Precipitable Water Vapour (PWV), offering improved accuracy and spatiotemporal coverage. A rigorous data screening and quality assessment framework was implemented, including formal error detection, offset identification, and extensive cross-validation with ERA5 reanalysis dataset, radiosonde profiles, and Very Long Baseline Interferometry measurements. Collectively, these efforts ensured the consistency, accuracy, and homogeneity of the dataset. In addition, diurnal, monthly, and annual variations in ZTD and PWV have been analysed to evaluate and demonstrate its feasibility for monitoring climate variability, atmospheric circulation, and weather extremes. The insights provided by the dataset address critical data gaps in global climate observing systems and provide a robust foundation for advancing climate research and applications. Representing a significant milestone in GNSS climatology, this dataset serves as a vital resource for the scientific community, supporting improved understanding of atmospheric processes and more effective responses to climate-related challenges. The generated dataset is now available at: https://doi.org/10.1594/PANGAEA.982476 (Wang et al., 2025a) and https://www.gnss.studio/Login (Wang et al., 2025b).

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Publication Details

Journal
Earth system science data
Published
2025-11-10
DOI
https://doi.org/10.5194/essd-17-5951-2025
Citations
1
Primary Topic
Soil Moisture and Remote Sensing
Type
article
Field-Weighted Citation Impact
0.80
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article

A global GNSS climate data record from 5085 stations spanning up to 22 years

Qiuying Huang, Luis Elneser, Anthony Rea, Yuriy Kuleshov et al.
1 citations
Earth system science data
Soil Moisture and Remote Sensing
0.80
article

A global GNSS climate data record from 5085 stations spanning up to 22 years

Qiuying Huang, Luis Elneser, Anthony Rea, Yuriy Kuleshov, Suelynn Choy, Xiaoming Wang, Haobo Li, Hongxin Zhang, Wenhui Cai
article en
1 citations

Abstract

Abstract. This work presents a comprehensive global GNSS climate data record derived from 5085 stations, spanning up to a 22-year period 2000–2021. The dataset was generated using the state-of-the-art processing methodologies and precise products from the International GNSS Service (IGS) Repro-3 initiative. It includes high-quality hourly estimates of Zenith Total Delay (ZTD) and Precipitable Water Vapour (PWV), offering improved accuracy and spatiotemporal coverage. A rigorous data screening and quality assessment framework was implemented, including formal error detection, offset identification, and extensive cross-validation with ERA5 reanalysis dataset, radiosonde profiles, and Very Long Baseline Interferometry measurements. Collectively, these efforts ensured the consistency, accuracy, and homogeneity of the dataset. In addition, diurnal, monthly, and annual variations in ZTD and PWV have been analysed to evaluate and demonstrate its feasibility for monitoring climate variability, atmospheric circulation, and weather extremes. The insights provided by the dataset address critical data gaps in global climate observing systems and provide a robust foundation for advancing climate research and applications. Representing a significant milestone in GNSS climatology, this dataset serves as a vital resource for the scientific community, supporting improved understanding of atmospheric processes and more effective responses to climate-related challenges. The generated dataset is now available at: https://doi.org/10.1594/PANGAEA.982476 (Wang et al., 2025a) and https://www.gnss.studio/Login (Wang et al., 2025b).

Earth system science dataVol. 17(11)
Bureau of Meteorology (AU), Chinese Academy of Sciences (CN), MIT University (MK), Aerospace Information Research Institute (CN), University of Chinese Academy of Sciences (CN), RMIT University (AU)
Openalex Percentile: Top 23%
Soil Moisture and Remote Sensing
0.80
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