EGU23-17204
https://doi.org/10.5194/egusphere-egu23-17204
EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Modeling of the weighted mean temperature based on the random forest machine learning approach

Qinzheng Li1,2, Johannes Böhm1, Linguo Yuan2, and Robert Weber1
Qinzheng Li et al.
  • 1Department of Geodesy and Geoinformation, Vienna University of Technology (TU Wien), Wien 1040, Austria
  • 2Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China

Atmospheric weighted mean temperature, Tm, is an important parameter in the Earth’s atmospheric water vapor sounding with the Global Navigation Satellite System (GNSS) technique. In this study, considering spatial distribution, time-varying characteristics, and the correlation with surface meteorological variables, Tm modeling is realized based on the random forest (RF) machine learning and global atmospheric profiles from radiosonde (RS) data and GPS radio occultations (RO) measurements. Comparisons of modeled results and numerical integrations of atmospheric profiles in 2020 show that the RF-based Tm model with surface meteorological parameters generally obtains a good accuracy with overall RMS errors of 2.8 K in comparison with RS data and 2.6 K in contrast to GPS RO data.

How to cite: Li, Q., Böhm, J., Yuan, L., and Weber, R.: Modeling of the weighted mean temperature based on the random forest machine learning approach, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-17204, https://doi.org/10.5194/egusphere-egu23-17204, 2023.