融合GPT2w模型与GNSS参数获取大气可降水量

针对GPT2w模型误差累积所导致的天顶对流层延迟(zenith tropospheric delay, ZTD)和大气可降水量(precipitable water vapor, PWV)精度不高的问题，利用2017年长三角地区7个探空站和2个GNSS站的实测数据检验GPT2w模型获取的气压、温度、水汽压、加权平均温度(Tm)和ZTD等参数的精度，并融合GNSS解算得到的ZTD(GNSS-ZTD)与GPT2w模型获取的气象参数，提高PWV反演精度。结果表明：1)近地面处的气压、温度和水汽压的bias分布在－3~4 mbar、－7~7 K和－9~2 mbar之间，精度较高；2)GPT2w模型获取的Tm在长三角地区适用性较好，年均bias和RMS分别为－1.21 K和6.89 K；3)基于GPT2w模型解算的ZTD的bias和RMS均值分别为1.4 cm和9.4 cm，精度明显低于基于实测气象数据获得的GNSS-ZTD；4)参数融合法计算的PWV与GNSS-PWV精度相当，该方法可用于无实测气象参数时实时获取PWV。

Parameter Fusion from GPT2w Model and GNSS to Obtain Precipitable Water Vapor

In view of the problem that the zenith tropospheric delay(ZTD) and precipitable water vapor(PWV) caused by the accumulated errors of the GPT2w model, we obtain actual data from seven radiosonde stations and two GNSS stations in Yangtze river delta region in 2017 to test the precision of parameters such as air pressure, temperature, water vapor pressure, weighted mean temperature (Tm) and ZTD of the GPT2w model, and combine the ZTD calculated by GNSS (GNSS-ZTD) and the Tm obtained from the GPT2w model to improve the PWV precision. The results show that the biases of air pressure, temperature and water vapor pressure near the ground are distributed at -3~4 mbar, -7~7 K and -9~2 mbar respectively, which is with high accuracy. The Tm obtained from GPT2w has good applicability in Yangtze river delta, with an annual average of -1.21 K bias and 6.89 K RMS. The mean bias and RMS of ZTD obtained from GPT2w model are 1.4 cm and 9.4 cm respectively, with precision that are significantly lower than that of GNSS-ZTD derived from actual meteorological data. The accuracy of PWV calculated by parameter fusion method is equivalent to GNSS-PWV. This method can be used to obtain PWV quickly when there is without actual meteorological parameters.