Optimization and Construction of Jinan Regional Tm Model Based on LSTM and Analysis of Its Influence on the Accuracy of GNSS Inversion PWV - Shukai Wang; Qiuying Guo; Guihong Hua; Yingjun Sun; Wengang Sang; Zhengyu Wang (Atmosphere)
Water vapor constitutes a vital component of atmospheric precipitation, serving as the fundamental material basis for weather phenomena such as rainfall, and is a significant factor contributing to extreme weather events. The Weighted Mean Temperature (Tm) is a crucial factor in the calculation of Precipitable Water Vapor (PWV) in the atmosphere, directly impacting the quality of GNSS-PWV inversion. The TmN, TmL1, and TmL2 models were constructed through regression analysis and LSTM based on data from the Zhangqiu Radiosonde Station in the Jinan region from 2020 to 2022, as well as ERA5 data. The six Tm models (TmN, TmL1, TmL2, Bevis, GTm, and GPT3) were analyzed by comparing them with the Tm value from the Radiosonde station in 2023. Compared with the Bevis, GTm, and GPT3 models, the accuracy of TmN was improved by 24%, 19%, and 45%, TmL1 was improved by 20%, 16%, and 42%, and TmL2 was increased by 34%, 29%, and 52%. The influence of the above six Tm models on GNSS-PWV accuracy was analyzed using both theoretical and experimental methods. It was demonstrated that the impact of TmL1 and TmL2 on the accuracy of the PWV solution is significantly enhanced in comparison with the other Tm models. The TmL1 and TmL2 models developed in this study offer enhanced accuracy for Tm data utilized in GNSS PWV inversion within the Jinan region.