A neural network model for predicting weighted mean temperature

Water vapor is an important element of the Earth’s atmosphere, and most of it concentrates at the bottom of the troposphere. Knowledge of the water vapor measured by Global Navigation Satellite Systems (GNSS) is an important direction of GNSS research. In particular, when the zenith wet delay is converted to precipitable water vapor, the weighted mean temperature \(T_\mathrm{m}\) is a variable parameter to be determined in this conversion. The purpose of the study is getting a more accurate \(T_\mathrm{m}\) model for global users by a combination of two different characteristics of \(T_\mathrm{m}\) (i.e., the \(T_\mathrm{m}\) seasonal variations and the relationships between \(T_\mathrm{m}\) and surface meteorological elements). The modeling process was carried out by using the neural network technology. A multilayer feedforward neural network model (the NN) was established. The NN model is used with measurements of only surface temperature \(T_\mathrm{S}\). The NN was validated and compared with four other published global \(T_\mathrm{m}\) models. The results show that the NN performed better than any of the four compared models on the global scale.