A hybrid-grid global model for the estimation of atmospheric weighted mean temperature considering time-varying vertical adjustment rate in GNSS precipitable water vapour retrieval

作者：Shaofeng Xie, Jihong Zhang, Liangke Huang, Fade Chen, Yongfeng Wu, Yijie Wang, Lilong Liu · 发表于：Geoscientific model development · 年份：2025 · DOI：10.5194/gmd-18-6987-2025 · 被引用次数：4 · 研究领域：GNSS positioning and interference、Geophysics and Gravity Measurements、Ionosphere and magnetosphere dynamics

Abstract. The atmospheric weighted mean temperature (Tm) is a key parameter in global navigation satellite system (GNSS) water vapour retrieval and can convert the zenith wet delay (ZWD) into precipitable water vapour (PWV). However, there are some shortcomings in the existing Tm models, such as the detailed time-varying vertical adjustment rate not being considered. In addition, the spatiotemporal characteristics of Tm need to be further refined. Therefore, we developed a new global high-precision and high-spatiotemporal-resolution Tm model considering time-varying vertical adjustment rate using the latest European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) atmospheric reanalysis data. Firstly, a new global grid Tm vertical adjustment rate model (NGGTm-H) was developed using the sliding-window algorithm. Secondly, the daily variation characteristics of Tm and its relationships with geographical situations were investigated. Finally, a new global hybrid-grid Tm model (NGGTm) considering time-varying vertical adjustment rate was developed. To verify the effectiveness of the proposed model, the NGGTm model was compared with the Bevis and global pressure and temperature 3 (GPT3) models using the Tm data recorded at 378 radiosonde stations in 2017 and the surface gridded Tm data calculated from the ERA5 reanalysis data. The results show that taking the surface gridded Tm data of ERA5 as reference values, the average root-mean-square error (RMSE) value calculate...