An algorithm for global troposphere delay determination by the combination of GPT/UNB3m and classic models

Liu, Guoyan; Yu, Guangzheng

doi:10.1109/cpgps.2017.8075100

Cited by 1 publication

(1 citation statement)

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“…Based on the empirical grid coefficients, the empirical tropospheric delay model, namely GPT3 model, provides ZTD using the trigonometric functions (Boehm et al, 2007). So far, the ZTD values derived both from the two types of the tropospheric delay models have been applied in various practice by many scholars such as the development of ZTD models, different GNSS applications and accuracy assessment: (a) For ZTD model development, Liu and Yu (2017) proposed an algorithm for global troposphere delay determination by the combination of GPT/ UNB3m. F. 2019) applied the discrete model in real-time GNSS applications, evaluating its performance using forecast data from the European Center for Medium-Range Weather Forecasts (ECMWF).…”

Section: Introductionmentioning

confidence: 99%

Comparing Discrete and Empirical Troposphere Delay Models: A Global IGS‐Based Evaluation

Yao,

Yang,

et al. 2024

Radio Science

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Zenith tropospheric delay (ZTD) is an important atmospheric parameter in radio‐space‐geodetic techniques such as Global Navigation Satellite System (GNSS), which is pivotal for GNSS positioning, navigation and meteorology. The Vienna Mapping Function (VMF) data server is a widely utilized source for implementing ZTD, offering two types of models, that is, the empirical one and the discrete one with Grid‐wise and Site‐wise models. Therefore, to evaluate the accuracy of these models becomes the focus of this article. Specifically, this study investigates their performances in terms of calculation of ZTD, using the hourly values derived from the International GNSS Service data as references. The results show that the root mean square err (RMSE) of the Site‐wise, Grid‐wise and global pressure and temperature 3 model are 11.71/13.03/38.56 mm, respectively, indicating the discrete model performs generally better than the empirical model, and the Site‐wise model is the better of the two discrete models. From the perspective of spatial resolution, the performance of these three models in ZTD calculation shows obvious influences of latitude changes and elevation differences. From the temporal analysis, the accuracy of the discrete model shows differences over different UTC epochs, while the empirical model can only express the seasonal ZTD characteristics with the average RMSE at different epochs being similar, the specifically values are 39.67, 39.26, 39.38 and 39.18 mm at UTC 0:00, 6:00, 12:00 and 18:00, respectively. The histogram and boxplot well indicate the accuracy differences of the three models in different seasons. Additionally, the time series of three models at different latitudes were also explored in this research. These explorations are conducive to the selection of appropriate models for calculating ZTD based on specific requirements.

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Section: Introductionmentioning

confidence: 99%