Estimation of ground GNSS precipitable water based on atmospheric water vapor region model

Xiangrong Yan; Weifang Yang; Yuhao Hou; Yibo Peng; Xiaoshuang Cao; Haojie Dong

doi:10.1117/12.3029751

21 June 2024 Estimation of ground GNSS precipitable water based on atmospheric water vapor region model

Xiangrong Yan, Weifang Yang, Yuhao Hou, Yibo Peng, Xiaoshuang Cao, Haojie Dong

Proceedings Volume 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024); 131671B (2024) https://doi.org/10.1117/12.3029751
Event: International Conference on Remote Sensing, Mapping and Image Processing (RSMIP 2024), 2024, Xiamen, China

Abstract

Precipitable Water Vapor (PWV) with high spatial and temporal resolution can be obtained by ground-based GNSS technology, but the detection accuracy is affected by the atmospheric weighted mean temperature model and the calculation accuracy of the wet delay. In this paper, an interpolation method taking into account the prior water vapor information of GNSS is proposed, and the radiosonde data is used as the prior water vapor information to establish the atmospheric weighted mean temperature model in Xuzhou. Then, the high precision PWV value is obtained by the weighted average of PWV obtained from the atmospheric weighted mean temperature model and PWV obtained by GAMIT. Based on the GNSS data of the Xuzhou area for 92 days from June to August 2019, this paper uses the new scheme, inverse distance weighting, local polynomial, radial basis function, and Kriging respectively to interpolate to PWV at the radiosonde station. The results show that the interpolation accuracy of the new scheme is the highest, RMSE is 1.9mm, and the average improvement rate of RMSE of the new scheme is 3.675% compared with the four interpolation methods.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xiangrong Yan, Weifang Yang, Yuhao Hou, Yibo Peng, Xiaoshuang Cao, and Haojie Dong "Estimation of ground GNSS precipitable water based on atmospheric water vapor region model", Proc. SPIE 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024), 131671B (21 June 2024); https://doi.org/10.1117/12.3029751

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