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

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 calculated by the NGGTm model was 2.84 K, which was lower with 0.50, 0.18 and 0.06 K than those of the Bevis, GPT3-5 and GPT3-1 models, respectively. Meanwhile, taking the Tm from the radiosonde stations as the reference values, the mean bias and RMSE of the NGGTm model were 0.10 and 3.30 K, respectively, which exhibit the best accuracy and stability among the Bevis, GPT3-5 and GPT3-1 models.