Find in Library

Near-surface air temperature (<i>T_a</i>) estimation by geostationary meteorological satellites is mainly carried out under clear-sky conditions. In this study, we propose an all-weather <i>T_a</i> estimation method utilizing FY-4A Advanced Geostationary Radiation Imager (AGRI) and the Global Forecast System (GFS), along with additional auxiliary data. The method includes two neural-network-based <i>T_a</i> estimation models for clear and cloudy skies, respectively. For clear skies, AGRI LST was utilized to estimate the <i>T_a</i> (<i>T_a,clear</i>), whereas cloud top temperature and cloud top height were employed to estimate the <i>T_a</i> for cloudy skies (<i>T_a,cloudy</i>). The estimated <i>T_a</i> was validated using the 2020 data from 1211 stations in China, and the RMSE values of the <i>T_a,clear</i> and <i>T_a,cloudy</i> were 1.80 °C and 1.72 °C, while the correlation coefficients were 0.99 and 0.986, respectively. The performance of the all-weather <i>T_a</i> estimation model showed clear temporal and spatial variation characteristics, with higher accuracy in summer (RMSE = 1.53 °C) and lower accuracy in winter (RMSE = 1.88 °C). The accuracy in southeastern China was substantially better than in western and northern China. In addition, the dependence of the accuracy of the <i>T_a</i> estimation model for LST, CTT, CTH, elevation, and air temperature were analyzed. The global sensitivity analysis shows that AGRI and GFS data are the most important factors for accurate <i>T_a</i> estimation. The AGRI-estimated <i>T_a</i> showed higher accuracy compared to the ERA5-Land data. The proposed models demonstrated potential for <i>T_a</i> estimation under all-weather conditions and are adaptable to other geostationary satellites.