基于GPT3模型的ZTD及PWV反演精度分析

使用亚洲区域18个IGS测站和中国区域内16个探空站2016~2018年的数据，研究GPT3模型反演天顶对流层延迟（ZTD）和大气可降水量（PWV）的精度，并与其他GPT系列模型进行对比。结果表明，GPT3-1模型估计的ZTD的bias均值和最大值均最小，分别为1.34 mm和14.06 mm；GPT3模型整体精度略优于GPT2w模型，优于GPT2模型。探空站处GPT3模型反演的PWV的bias和RMSE均表现出较强的季节性特征；由GPT3模型反演的PWV的月均值可知，GPT3-1模型比GPT3-5模型具有更高的精度和稳定性。

Accuracy Analysis of ZTD and Precipitable Water Vapor Inversion Based on GPT3 Model

Using the data of 18 IGS stations in Asian region and 16 radiosonde stations in China region from 2016 to 2018, the accuracy of zenith tropospheric delay (ZTD) and precipitable water vapor (PWV) inversed by GPT3 model are studied, and compared with other GPT series models. The results show that the ZTD of the GPT3-1 model has the smallest mean and maximum deviation bias values of 1.34 mm and 14.06 mm, respectively; the accuracy of GPT3 model is slightly better than GPT2w model and better than GPT2 model. The bias and root mean square error(RMSE) of the GPT3 model-derived PWV at the radiosonde stations show strong seasonal characteristics, and the GPT3-1 model has higher accuracy and stability than the GPT3-5 model, as shown by the monthly mean values of the GPT3 model-derived PWV.