Establishment of PWV Fusion Model Using Multi-source Data

ZHAO Qingzhi; DU Zheng; WU Manyi; YAO Yibin; YAO Wanqiang

doi:10.13203/j.whugis20200412

Volume 42 Issue 11

Nov. 2022

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Geomatics and Information Science of Wuhan University > 2022 > 47(11): 1823-1831. > DOI: 10.13203/j.whugis20200412

ZHAO Qingzhi, DU Zheng, WU Manyi, YAO Yibin, YAO Wanqiang. Establishment of PWV Fusion Model Using Multi-source Data[J]. Geomatics and Information Science of Wuhan University, 2022, 47(11): 1823-1831. DOI: 10.13203/j.whugis20200412

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Establishment of PWV Fusion Model Using Multi-source Data

1.
College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China
2.
The First Topographic Surveying Brigade, National Administration of Surveying, Mapping and Geoinformation of China, Xi'an 710054, China
3.
School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China

Funds:

The Local Special Scientific Research Plan of Shaanxi Provincial Department of Education 22JE012

China Postdoctoral Science Foundation of Special Funded (Station) 2022T150523

the National Natural Science Foundation of China 42274039

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Author Bio:
ZHAO Qingzhi, PhD, associate professor, specializes in GNSS data processing and GNSS meteorology. E-mail: zhaoqingzhia@163.com
Received Date: November 07, 2020
Available Online: November 15, 2022
Published Date: November 04, 2022

Graphical Abstract

Abstract

Abstract

Objectives Precipitable water vapor (PWV) information with high precision and high spatial-temporal resolution plays an important role in the study of extreme weather. The PWV obtained by traditional single water vapor detection technology has the defects of poor precision and low spatial-temporal resolution due to the limitations of its system design.
Methods To solve this problem, we propose a PWV hybrid model based on multi-source data, called the GSP(GPT2w+spherical harmonical function+polynomial fitting) model.In this model, the initial value of PWV is calculated by the GPT2w model, the residual sequence of PWV is fitted by a spherical harmonic function, and after that, deviation correction is performed for the residual PWV based on the polynomial fitting, and the Bartlett test is introduced to deter mine the optimal weights of multi-source data in the GSP model.
Results The data of 26 GNSS stations and 37 ERA-Interim grid points (1°×1°) in Yunnan Province, China has been selected to validate the GSP model, and the numerical results show that the accuracy improvement rate of the GSP model is 15%— 18% compared with the traditional polynomial fitting model. Compared with the ERA5 (0.25°×0.25°) data, the mean root mean square and Bias of GSP model are 1.64 mm and -0.25 mm, respectively.
Conclusions The above results show that the proposed GSP model has high accuracy and plays an important role in extreme weather warnings.
- GNSS,
- PWV,
- multi-source data fusion,
- GSP

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References (29)

References

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