Global pressure and temperature (GPT) series models can provide the underlying meteorological parameters for tropospheric corrections without any other meteorological observations, which allows them to be widely used for a series of geodetic as well as meteorological and climatological purposes. Due to the height difference between the empirical model height and user location, a vertical correction of meteorological parameters is inevitable, particularly for airborne users. Unfortunately, the GPT series models have limitations on the vertical correction. We explored the temperature lapse rate for the vertical adjustment using 10 years of reanalysis data provided by the National Centers for Environmental Prediction (NCEP), and extended the GPT models to improved global pressure and temperature (IGPT) series models by introducing a new temperature lapse rate model and a new formulation of pressure reduction. An Evaluation of the IGPT model expression determines that the IGPT models have better accuracy than the GPT models, particularly under large height differences, which is attributed to their ability to consider the real behavior of temperature in the atmosphere and adiabatic effects on air pressure. The performance of the IGPT models in zenith tropospheric delay (ZTD) estimations also has been evaluated by comparison with the fifth-generation European Centers for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA5) data and International GNSS Service (IGS) data. The results confirm that our new models can effectively improve the accuracy of ZTDs, particularly at larger altitude differences between the target height and the corresponding four grid points of the model, not only enhancing the performance of the model in complex terrain but also extending the feasibility of IGPT models from the Earth's surface to higher altitudes.

The tropospheric delay is a major error source for Global Navigation Satellite System (GNSS) positioning and navigation, and usually can be corrected by using an empirical model. Due to the small number of parameters and simplified algorithm, the UNB3m model has been widely hardwired in GNSS receivers for real-time applications. However, many studies have noted that the UNB3m model has significant systematic errors in the correction of tropospheric delays, mainly due to the assumption of north-south symmetry. Therefore, considering the realistic atmospheric behavior, we proposed a new tabular zenith tropospheric delay (TZTD) model using 10 years of NCEP (National Centre for Environmental Prediction) data. The performance of the TZTD model was assessed along with the GPT3 and UNB3m models by comparison with ZTDs derived from the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA5) data and GNSS observations. The results show that the TZTD model (with a RMSE of 41mm) significantly outperforms the UNB3m model (with a RMSE of 49 mm). Furthermore, the accuracy of the new model is better than that of the GPT3 model in terms of the zenith tropospheric hydrostatic delay (ZHD) prediction, particularly at high altitudes. The TZTD model characterized by simplicity and accuracy, is expected to be a substitute for the UNB3m model in real-time GNSS applications.INDEX TERMS Tropospheric delay model, real-time GNSS applications, numerical weather prediction data, UNB3m, GPT3