Since measurements of meteorological parameters like air pressure, water vapor pressure, and temperature are typically not conducted at the receiving antenna’s height, accurate vertical adjustments are indispensable for the tropospheric delay calculation in GNSS applications. We developed an enhanced GPT3 model named as GPT3a, incorporating a new temperature lapse rate grid model and the adiabatic method. The capabilities of GPT3a in predicting atmospheric parameter profiles are assessed by comparison with radiosonde data, NCEP reanalysis data, and GNSS data. Compared with GPT3, IGPT, UNB3, and the constant model with a value of 6.5 K/km, the accuracy of the GPT3a is improved by 50 %, 26 %, 21 %, and 31 % respectively in predicting lapse rate. The RMSEs of GPT3a, GPT3 and IGPT, across temperature profiles (4.2 K, 10.7 K and 6.6 K, respectively), pressure profiles (5.7 hPa, 18.7 hPa and 6.8 hPa, respectively), and ZHD profiles (16.4 mm, 36.4 mm and 17.5 mm, respectively), demonstrate that GPT3a performs superiorly to the GPT3 and IGPT models. Moreover, in the GNSS-based water vapor retrieval, when there is a large height difference between GNSS sites and the model, the GPT3a obviously outperforms GPT3. In short, GPT3a can be used as an enhancement of GPT3 to support GNSS positioning, GNSS meteorology and atmospheric research, which extends the applicability of GPT3 beyond the Earth’s surface to airspace.
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