GPT3模型反演GNSS大气可降水量精度评定

采用中国区域2017~2018年与GNSS站并址的49个探空站资料对GPT3模型估算的气象参数的精度进行评估，再利用49个GNSS站结合GPT3模型估算的气象参数反演日均大气可降水量PWV，并采用与GNSS站并址的探空站数据对其精度进行评定。实验得出：1）在中国地区，1°分辨率的GPT3模型的精度和稳定性优于5°分辨率，其气压、气温和大气加权平均温度T_m的偏差均值分别为0.73 hPa、1.34 K和－1.67 K，均方根误差均值分别为4.21 hPa、3.75 K和4.15 K；2）利用GPT3模型提供的气温结合Bevis经验公式反演的PWV与GPT3模型提供的Tm反演的PWV精度相当，且2种方法反演的PWV和探空资料实测地表温度反演的PWV呈现很好的一致性，在我国青藏高原和西北地区反演PWV的精度优于我国南方和北方地区。

Evaluation of GNSS Precipitable Water Vapor Derived from GPT3 Model

Firstly, we evaluate the accuracy of the meteorological parameters estimated by the GPT3 model using the data of 49 radiosonde stations adjacent to GNSS stations in China from 2017 to 2018. Secondly, combining the meteorological parameters estimated by the GPT3 model and 49 GNSS stations, we calculate the daily mean PWV, and evaluate the accuracy by radiosonde stations adjacent to GNSS stations. Finally, the results are obtained through the experiment: 1) In China, the accuracy and stability of the GPT3 model with 1° resolution are better than those with 5° resolution. The air pressure, temperature and T_m annual bias are 0.73 hPa, 1.34 K and -1.67 K, and the annual RMSE are 4.21 hPa, 3.75 K and 4.15 K. 2) The accuracy of PWV based on temperature inversion by GPT3 model combined with Bevis empirical formula is similar to that by GPT3 model Tm, and the PWV obtained by the two methods and PWV obtained by the sounding data show good consistency; furthermore, the accuracy of PWV in Tibet Plateau and northwest China is better than that in the southern and northern regions.