A new model for estimating atmospheric weighted mean temperature from radiosonde and multi-mission GNSS radio occultation data

In Global Navigation Satellite System (GNSS) meteorology, the atmospheric weighted mean temperature (T_m) is a critical intermediate parameter for converting zenith wet delay (ZWD) to precipitable water vapor (PWV), essential for accurate atmospheric water content estimation. However, global models often overlook regional climatic variability, leading to reduced accuracy in localized applications. This study introduces an improved T_m model developed using radiosonde observations across Iran and GNSS radio occultation (RO) profiles from CHAMP, GRACE, MetOp-A/B/C, COSMIC, TerraSAR-X, and TanDEM-X missions collected between 2007 and 2022. A novel integral formulation was proposed to estimate T_m more accurately by incorporating vertical water vapor distribution and temperature linearity. Based on this formulation, three regional T_m models were constructed using annual, semiannual, and diurnal periodicities, along with surface temperature (T_s), each varying in structure and complexity. Validation against independent radiosonde observations from 2022 showed that Models Two and Three outperformed the Bevis model, reducing RMSE by 30.7%. When evaluated against GNSS RO profiles, Model One—excluding T_s due to its inaccessibility in RO data—yielded the highest accuracy, with a 42.6% improvement in RMSE over the Bevis model. To evaluate the practical effectiveness of the proposed T_m model, PWV was derived from GNSS data at the tehn and tabz stations during the second half of 2022 and compared with PWV values obtained from co-located radiosonde observations in Tehran and Tabriz. Using T_m from Model One improved PWV estimation compared to the Bevis model, reducing RMSE and MAE by up to 54% and 53.8% in Tabriz and 50.6% and 52.9% in Tehran, respectively. These results demonstrate that regionalized T_m modeling, particularly approaches that avoid dependence on T_s, can significantly enhance GNSS-based PWV estimation in areas with limited surface data.