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<p>Atmospheric weighted mean temperature (Tm) is pivotal for converting zenith wet delay (ZWD) derived from global navigation satellite system (GNSS) signal to precipitable water vapor (PWV). Currently, most Tm models are developed based on radiosonde (RS) data or reanalysis data. These models are limited by the availability of measured surface temperature and the accuracy of input data used during modeling. Additionally, they face challenges in accounting for the diurnal effect on Tm. In this study, we innovatively use the latest AI weather model, Pangu-Weather, to estimate surface air temperature (Pangu-Ts) in 2016–2019. Compared with the measured surface temperature (RS-Ts) at RS stations, bias and root mean square error (RMSE) are − 0.75&#xa0;K and 2.54&#xa0;K, respectively. Subsequently, a Tm forecast model (RF-Tm) in China is developed based on this data using random forest (RF). The model only requires the input of time, 3D coordinates of stations, and predicted Pangu-Ts data to yield the forecasted Tm estimates. The validation results based on RS data show that bias of the RF-Tm is −&#xa0;0.38&#xa0;K and the RMSE is 2.47&#xa0;K. Through comparison and validation with the Bevis model, GPT3, and the Tm forecast model (BP-Tm) developed using back propagation neural network (BPNN), RF-Tm demonstrates reductions in RMSE of 41.33%, 39.46%, and 2.76%, respectively. The mean values with theoretical RMSE and relative error of PWV derived from the RF-Tm are 0.171&#xa0;mm and 0.90%. The RF-Tm proposed in this study can provide reliable and high-precision Tm estimation for real-time GNSS PWV retrieval.</p>

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Initial results of atmospheric weighted mean temperature estimation with Pangu-Weather in real-time GNSS PWV retrieval for China

  • 期刊论文

  • 发表日期：

    2025年1月6日

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  • Liangke Huang,
  • Yicai Wang,
  • Haohang Bi,
  • Ge Zhu,
  • Lilong Liu,
  • Weiping Jiang

摘要

Atmospheric weighted mean temperature (Tm) is pivotal for converting zenith wet delay (ZWD) derived from global navigation satellite system (GNSS) signal to precipitable water vapor (PWV). Currently, most Tm models are developed based on radiosonde (RS) data or reanalysis data. These models are limited by the availability of measured surface temperature and the accuracy of input data used during modeling. Additionally, they face challenges in accounting for the diurnal effect on Tm. In this study, we innovatively use the latest AI weather model, Pangu-Weather, to estimate surface air temperature (Pangu-Ts) in 2016–2019. Compared with the measured surface temperature (RS-Ts) at RS stations, bias and root mean square error (RMSE) are − 0.75 K and 2.54 K, respectively. Subsequently, a Tm forecast model (RF-Tm) in China is developed based on this data using random forest (RF). The model only requires the input of time, 3D coordinates of stations, and predicted Pangu-Ts data to yield the forecasted Tm estimates. The validation results based on RS data show that bias of the RF-Tm is − 0.38 K and the RMSE is 2.47 K. Through comparison and validation with the Bevis model, GPT3, and the Tm forecast model (BP-Tm) developed using back propagation neural network (BPNN), RF-Tm demonstrates reductions in RMSE of 41.33%, 39.46%, and 2.76%, respectively. The mean values with theoretical RMSE and relative error of PWV derived from the RF-Tm are 0.171 mm and 0.90%. The RF-Tm proposed in this study can provide reliable and high-precision Tm estimation for real-time GNSS PWV retrieval.