GPT, GPT2, GPT3 경험모델을 이용한 PPP ZTD의 비교와 분석
Analysis and Comparison of PPP ZTD using Empirical Models GPT, GPT2, and GPT3
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초록
The accuracy of tropospheric delay corrections for the global navigation satellite system (GNSS) depends on the quality of the tropospheric model. The empirical tropospheric models used in GNSS processing include the Saastamoinen, global pressure and temperature (GPT), and global mapping function (GMF). In the present study, we estimate precise point positioning (PPP) zenith total delay (ZTD) using GPT, GPT2, and GPT3 empirical models, and then compare the results. To verify the PPP ZTD obtained from the GPT model, we compared it with the international GNSS service (IGS) ZTD products. The root mean square (RMS) value at the DAEJ station was estimated to be 4.97 mm. This is close to the accuracy of the IGS ZTD, which is about 4 mm. As a result, it is suggested that the PPP ZTD estimated in this study is reliable. In addition, we confirmed that there is a bias of 0.33 mm, 0.26 mm, and 0.49 mm between the GPT and other models at the DAEJ, MIZU, and DARW stations, respectively. On the other hand, no bias was observed between the GPT2 and GPT3 models, except for at DARW, and their ZTD values were in good agreement.
Abstract
The accuracy of tropospheric delay corrections for the global navigation satellite system (GNSS) depends on the quality of the tropospheric model. The empirical tropospheric models used in GNSS processing include the Saastamoinen, global pressure and temperature (GPT), and global mapping function (GMF). In the present study, we estimate precise point positioning (PPP) zenith total delay (ZTD) using GPT, GPT2, and GPT3 empirical models, and then compare the results. To verify the PPP ZTD obtained from the GPT model, we compared it with the international GNSS service (IGS) ZTD products. The root mean square (RMS) value at the DAEJ station was estimated to be 4.97 mm. This is close to the accuracy of the IGS ZTD, which is about 4 mm. As a result, it is suggested that the PPP ZTD estimated in this study is reliable. In addition, we confirmed that there is a bias of 0.33 mm, 0.26 mm, and 0.49 mm between the GPT and other models at the DAEJ, MIZU, and DARW stations, respectively. On the other hand, no bias was observed between the GPT2 and GPT3 models, except for at DARW, and their ZTD values were in good agreement.