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The Qinghai–Tibet Plateau region has significant altitude fluctuations and complex climate changes. However, the current global weighted average temperature (Tm) model does not fully consider the impact of meteorological and elevation factors on it, resulting in existing models being unable to accurately predict the Tm in the region. Therefore, this study constructed a weighted average temperature refinement model (XTm) related to surface temperature, water vapor pressure, geopotential height, annual variation, and semi-annual variation based on measured data from 13 radiosonde stations in the Qinghai–Tibet Plateau region from 2008 to 2017. Using the Tm calculated via the numerical integration method of radiosonde observations in the Qinghai–Tibet Plateau region from 2018 to 2019 as a reference value, the quality of the XTm model was tested and compared with the Bevis model and GPT2w (global pressure and temperature 2 wet) model. The results show that for 13 modeling stations, the bias and root-mean-square (RMS) values of the XTm model were −0.02 K and 2.83 K, respectively; compared with the Bevis, GPT2-1, and GPT2w-5 models, the quality of XTm was increased by 47%, 38%, and 47%, respectively. For the four non-modeling stations, the average bias and RMS values of the XTm model were 0.58 K and 2.78 K, respectively; compared with the other three Tm models, the RMS values and the mean bias were both minimal. In addition, the XTm model was also used to calculate the global navigation satellite system (GNSS) precipitable water vapor (PWV), and its average values for the theoretical RMSPWV and RMSPWV/PWV generated by water vapor calculation were 0.11 mm and 1.03%, respectively. Therefore, in the Qinghai–Tibet Plateau region, the XTm model could predict more accurate Tm values, which, in turn, is important for water vapor monitoring. [ABSTRACT FROM AUTHOR]

Weighted average temperature (Tm) plays a crucial role in global navigation satellite system (GNSS) precipitable water vapor (PWV) retrieval. Aiming at the poor applicability of the existing Tm models in Europe, in the article, we used observations from 48 radiosonde stations over Europe from 2014 to 2020 to establish a weighted average temperature model in Europe (ETm) by the nonlinear least squares estimation method. The ETm model takes into account factors such as ground temperature, water vapor pressure, latitude, and their annual variation, semiannual variation and diurnal variation. Taking the Tm obtained from the radiosonde data by the integration method in 2021 as the reference value, the accuracy of the ETm model was evaluated and compared with the commonly used Bevis model, ETmPoly model, and GPT2w model. The results of the 48 modeled stations showed that the mean bias and root mean square (RMS) values of the ETm model were 0.06 and 2.85 K, respectively, which were 21.7%, 11.5%, and 31.8% higher than the Bevis, ETmPoly, and GPT2w-1 (1° × 1° resolution) models, respectively. In addition, the radiosonde data of 12 non-modeling stations over Europe in 2021 were selected to participate in the model accuracy validation. The mean bias and RMS values of the ETm model were –0.07 and 2.87 K, respectively. Compared with the Bevis, ETmPoly, and GPT2w-1 models, the accuracy (in terms of RMS values) increased by 20.5%, 10.6%, and 35.2%, respectively. Finally, to further verify the superiority of the ETm model, the ETm model, and other Tm models were applied to the GNSS PWV calculation. The ETm model had mean RMSPWV and RMSPWV/PWV values of 0.17 mm and 1.03%, respectively, which were less than other Tm models. Therefore, the ETm model has essential applications in GNSS PWV over Europe. [ABSTRACT FROM AUTHOR]