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Establishment and Assessment of a New GNSS Precipitable Water Vapor Interpolation Scheme Based on the GPT2w Model

1,2,3, 1,3,*, 2, 1 and 4
1
School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
2
Nottingham Geospatial Institute, University of Nottingham, Nottingham NG7 2TU, UK
3
Key Laboratory of Precise Engineering and Industry Surveying of National Administration of Surveying, Mapping and Geoinformation, Wuhan University, Wuhan 430079, China
4
College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(9), 1127; https://doi.org/10.3390/rs11091127
Received: 7 March 2019 / Revised: 15 April 2019 / Accepted: 30 April 2019 / Published: 10 May 2019
(This article belongs to the Special Issue GPS/GNSS for Earth Science and Applications)
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Abstract

With the development of Global Navigation Satellite System (GNSS) reference station networks that provide rich data sources containing atmospheric information, the precipitable water vapor (PWV) retrieved from GNSS remote sensing has become one of the most important bodies of data in many meteorological departments. GNSS stations are distributed in the form of scatters, generally, these separations range from a few kilometers to tens of kilometers. Therefore, the spatial resolution of GNSS-PWV can restrict some applications such as interferometric synthetic aperture radar (InSAR) atmospheric calibration and regional atmospheric water vapor analysis, which inevitably require the spatial interpolation of GNSS-PWV. This paper explored a PWV interpolation scheme based on the GPT2w model, which requires no meteorological data at an interpolation station and no regression analysis of the observation data. The PWV interpolation experiment was conducted in Hong Kong by different interpolation schemes, which differed in whether the impact of elevation was considered and whether the GPT2w model was added. In this paper, we adopted three skill scores, i.e., compound relative error (CRE), mean absolute error (MAE), and root mean square error (RMSE), and two approaches, i.e., station cross-validation and grid data validation, for our comparison. Numerical results showed that the interpolation schemes adding the GPT2w model could greatly improve the PWV interpolation accuracy when compared to the traditional schemes, especially at interpolation points away from the elevation range of reference stations. Moreover, this paper analyzed the PWV interpolation results under different weather conditions, at different locations, and on different days. View Full-Text
Keywords: GNSS remote sensing; precipitable water vapor; interpolation; GPT2w model GNSS remote sensing; precipitable water vapor; interpolation; GPT2w model
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Yang, F.; Guo, J.; Meng, X.; Shi, J.; Zhou, L. Establishment and Assessment of a New GNSS Precipitable Water Vapor Interpolation Scheme Based on the GPT2w Model. Remote Sens. 2019, 11, 1127.

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