Estimation of All-Sky Gridded Diurnal Near-Surface Air Temperatures at Regional Scale From FY-4B Measurements

The near-surface air temperature (${{T}_{air}}$) is a principal variable describing energy exchange and water circulation between the land surface and the atmospheric environment. The estimation of ${{T}_{air}}$ by satellite land surface temperature (LST) is challenging due to the variable magnitude of the difference between ${{T}_{air}}$ and LST in both space and time, as well as the restriction of estimated ${{T}_{air}}$ to clear-sky conditions because of the penetration of infrared wavelengths. Moreover, the estimation suffers from low temporal resolution and primarily focuses on daily minimum, maximum, and two instantaneous ${{T}_{air}}$ per day. This study proposes a method for estimating all-sky gridded diurnal ${{T}_{air}}$ at regional scale from FY-4B/AGRI measurements. The multiscale geographically weighted regression model was investigated to establish the dynamic relationships between ground station observed ${{T}_{air}}$ and satellite LST under clear-sky conditions by employing different spatial values for each explanatory variable in localized regressions. A moving window loop based multiple linear regression was employed to establish the relationship between satellite-derived clear-sky ${{T}_{air}}$ and other variables to extrapolate ${{T}_{air}}$ in cloudy-sky pixels. The results showed that the proposed method captures the trend of ${{T}_{air}}$ variations well in hourly profiles with R values greater than 0.95. RMSE was 1.75 °C, 1.38 °C, 1.95 °C, and 2.19 °C in April, July, October, and January, respectively. The demonstration of heatwave monitoring showed that satellite-estimated ${{T}_{air}}$ provide an excellent representation of the spatial and temporal evolution of the heatwave.