Neural Network-Based Estimation of Near-Surface Air Temperature in All-Weather Conditions Using FY-4A AGRI Data over China

Abstract

Near-surface air temperature (Ta) estimation by geostationary meteorological satellites is mainly carried out under clear-sky conditions. In this study, we propose an all-weather Ta 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 Ta estimation models for clear and cloudy skies, respectively. For clear skies, AGRI LST was utilized to estimate the Ta (Ta,clear), whereas cloud top temperature and cloud top height were employed to estimate the Ta for cloudy skies (Ta,cloudy). The estimated Ta was validated using the 2020 data from 1211 stations in China, and the RMSE values of the Ta,clear and Ta,cloudy 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 Ta 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 Ta 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 Ta estimation. The AGRI-estimated Ta showed higher accuracy compared to the ERA5-Land data. The proposed models demonstrated potential for Ta estimation under all-weather conditions and are adaptable to other geostationary satellites.

Keywords

• all-weather
• air temperature
• AGRI
• GFS
• neural network