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Multi-scale accuracy evaluation and adaptability analysis of different atmospheric weighted mean temperature models in China

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Published/Copyright: January 7, 2026
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Journal of Applied Geodesy
From the journal Journal of Applied Geodesy

Abstract

The atmospheric weighted mean temperature (Tm) plays a critical role in deriving precipitable water vapor (PWV) from zenith wet delay (ZWD) in GNSS meteorology. Although numerous empirical Tm models have been rigorously validated at regional and global scales, their applicability and reliability across China remain insufficiently characterized. To address this gap, this study systematically evaluated four representative Tm models – Hourly Global Pressure and Temperature 2 (HGPT2), Global Pressure and Temperature 3 (GPT3), Global Tropospheric Model (GTrop), and China Tropospheric Model (CTrop) – using stratified meteorological data from 87 radiosonde stations in China (2010–2019). Model performance was assessed through bias and root mean square error (RMSE) metrics. Key findings reveal significant spatial heterogeneity in model accuracy across China’s four geographic regions. The GTrop and CTrop models demonstrated superior accuracy and nationwide stability, whereas HGPT2 and GPT3 exhibited pronounced limitations in mid-to-high latitudes. Notably, the CTrop model outperformed GTrop at select stations, achieving the lowest overall bias (0.39 K) and RMSE (3.95 K) among all models. Altitudinal analysis indicated that all models performed optimally below 1,000 m, with GTrop and CTrop showing marked accuracy improvements at higher elevations. Temporal analysis further highlighted CTrop’s robustness, exhibiting minimal errors across seasonal and diurnal variations. However, all models were constrained by limited Tm value ranges. Collectively, these results establish CTrop as the most accurate and stable Tm model for PWV retrieval in China.

Keywords: atmospheric weighted mean temperature; empirical Tm model; global navigation satellite system; accuracy analysis
Corresponding author: Bingbing Zhang, School of Geographic Sciences, Xinyang Normal University, Xinyang 464000, China, E-mail: 

Acknowledgments

We express our gratitude to the University of Wyoming (http://weather.uwyo.edu/upperair/seasia.html, accessed on 3 March 2025). Pedro Mateus, Zhangyu Sun, Daniel Landskron and Ge Zhu are also thanked for providing the corresponding Tm model.

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: The Natural Science Foundation of Henan Province (Grant No. 242300420618) and the Scientific Research Innovation Foundation for Graduate Students of Xinyang Normal University (Grant No. 2025KYJJ86).

  7. Data availability: We express our gratitude to the University of Wyoming (http://weather.uwyo.edu/upperair/seasia.html, accessed on 3 March 2025). Pedro Mateus, Zhangyu Sun, Daniel Landskron and Ge Zhu are also thanked for providing the corresponding Tm model.

Abbreviations

CTrop

China tropospheric model

DOY

day of year

ECMWF

European centre for medium-range weather forecasts

GNSS

global navigation satellite system

GPT3

global pressure and temperature 3

GTrop

global tropospheric model

HGPT2

hourly global pressure and temperature 2

IQR

interquartile range

MJD

modified Julian date

PWV

precipitable water vapor

RMSE

root mean square error

RS

radiosonde

Tm

atmospheric weighted mean temperature

Ts

surface temperature

ZWD

zenith wet delay

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Received: 2025-12-05
Accepted: 2025-12-25
Published Online: 2026-01-07

© 2026 Walter de Gruyter GmbH, Berlin/Boston

https://doi.org/10.1515/jag-2025-0127
Keywords for this article
atmospheric weighted mean temperature; empirical Tm model; global navigation satellite system; accuracy analysis
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