Research on precipitable water vapor prediction method based on lightGBM algorithm

The precipitable water vapor (PWV) represents the content of liquid water vapor in a unit cross-sectional area vertically from the Earth's surface to the top of the troposphere, reflecting the concentration of water vapor in the atmosphere. In this study, data from seven radiosondes in the Yangtze River Delta region from 2014 to 2019 were utilized to analyze the correlations between PWV and zenith tropospheric delay (ZTD), zenith hydrostatic delay (ZHD), zenith wet delay (ZWD), water vapor pressure (E_s), atmospheric pressure (P_s), surface temperature (T_s), and weighted mean temperature (T_m). A new Light Gradient Boosting Machine (LightGBM)-based PWV prediction model for the Yangtze River Delta region was established, and then the prediction accuracy of the LightGBM-PWV model was analyzed. The results show that the correlation coefficients (R) between PWV and T_m, T_s, P_s, E_s, ZHD, ZWD, and ZTD were 0.74, 0.76, –0.59, 0.76, –0.43, 1.00, and 0.94 respectively. The average biases of the yearly, seasonal, and monthly LightGBM-PWV model were 0.10 mm, 0.11 mm, and 0.12 mm respectively, and their RMSE are 0.25 mm, 0.26 mm, and 0.31 mm. The accuracy of the yearly, seasonal, and monthly LightGBM-PWV model decreased sequentially, different from the traditional linear fitting PWV models. The yearly LightGBM-PWV forecasting models demonstrate the highest accuracy. It can be applied for the GNSS-PWV forecasting, analysis, and research in the Yangtze River Delta region.