Downward shortwave radiation (
RS) drives many processes related to atmosphere–surface interactions and has great influence on the earth’s climate system. However, ground-measured
RS is still insufficient to represent the land surface, so it is still critical to generate high
[...] Read more.
Downward shortwave radiation (
RS) drives many processes related to atmosphere–surface interactions and has great influence on the earth’s climate system. However, ground-measured
RS is still insufficient to represent the land surface, so it is still critical to generate high accuracy and spatially continuous
RS data. This study tries to apply the random forest (RF) method to estimate the
RS from the Himawari-8 Advanced Himawari Imager (AHI) data from February to May 2016 with a two-km spatial resolution and a one-day temporal resolution. The ground-measured
RS at 86 stations of the Climate Data Center of the Chinese Meteorological Administration (CDC/CMA) are collected to evaluate the estimated
RS data from the RF method. The evaluation results indicate that the RF method is capable of estimating the
RS well at both the daily and monthly time scales. For the daily time scale, the evaluation results based on validation data show an overall R value of 0.92, a root mean square error (RMSE) value of 35.38 (18.40%) Wm
−2, and a mean bias error (MBE) value of 0.01 (0.01%) Wm
−2. For the estimated monthly
RS, the overall R was 0.99, the RMSE was 7.74 (4.09%) Wm
−2, and the MBE was 0.03 (0.02%) Wm
−2 at the selected stations. The comparison between the estimated
RS data over China and the Clouds and Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF)
RS dataset was also conducted in this study. The comparison results indicate that the
RS estimates from the RF method have comparable accuracy with the CERES-EBAF
RS data over China but provide higher spatial and temporal resolution.
Full article