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Assessment of Empirical Troposphere Model GPT3 Based on NGL’s Global Troposphere Products

by Junsheng Ding 1,2 and Junping Chen 1,2,3,*
Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China
School of Astronomy and Space Science, University of Chinese Academy of Sciences, Beijing 100049, China
Shanghai Key Laboratory of Space Navigation and Positioning Techniques, Shanghai 200030, China
Author to whom correspondence should be addressed.
Sensors 2020, 20(13), 3631;
Received: 6 May 2020 / Revised: 21 June 2020 / Accepted: 22 June 2020 / Published: 28 June 2020
(This article belongs to the Section Remote Sensors)
Tropospheric delay is one of the major error sources in GNSS (Global Navigation Satellite Systems) positioning. Over the years, many approaches have been devised which aim at accurately modeling tropospheric delays, so-called troposphere models. Using the troposphere data of over 16,000 global stations in the last 10 years, as calculated by the Nevada Geodetic Laboratory (NGL), this paper evaluates the performance of the empirical troposphere model GPT3, which is the latest version of the GPT (Global Pressure and Temperature) series model. Owing to the large station number, long time-span and diverse station distribution, the spatiotemporal properties of the empirical model were analyzed using the average deviation (BIAS) and root mean square (RMS) error as indicators. The experimental results demonstrate that: (1) the troposphere products of NGL have the same accuracy as the IGS (International GNSS Service) products and can be used as a reference for evaluating general troposphere models. (2) The global average BIAS of the ZTD (zenith total delay) estimated by GPT3 is −0.99 cm and the global average RMS is 4.41 cm. The accuracy of the model is strongly correlated with latitude and ellipsoidal height, showing obviously seasonal variations. (3) The global average RMS of the north gradient and east gradient estimated by GPT3 is 0.77 mm and 0.73 mm, respectively, which are strongly correlated with each other, with values increasing from the equator to lower latitudes and decreasing from lower to higher latitudes. View Full-Text
Keywords: GPT3 troposphere model; NGL products; ZTD; gradient; GNSS GPT3 troposphere model; NGL products; ZTD; gradient; GNSS
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Ding, J.; Chen, J. Assessment of Empirical Troposphere Model GPT3 Based on NGL’s Global Troposphere Products. Sensors 2020, 20, 3631.

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