Estimation of Surface Downward Shortwave Radiation over China from Himawari-8 AHI Data Based on Random Forest

Downward shortwave radiation (R-S) drives many processes related to atmosphere-surface interactions and has great influence on the earth's climate system. However, ground-measured R-S is still insufficient to represent the land surface, so it is still critical to generate high accuracy and spatially continuous R-S data. This study tries to apply the random forest (RF) method to estimate the R-S 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 R-S at 86 stations of the Climate Data Center of the Chinese Meteorological Administration (CDC/CMA) are collected to evaluate the estimated R-S data from the RF method. The evaluation results indicate that the RF method is capable of estimating the R-S 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 R-S, 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 R-S data over China and the Clouds and Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) R-S dataset was also conducted in this study. The comparison results indicate that the R-S estimates from the RF method have comparable accuracy with the CERES-EBAF R-S data over China but provide higher spatial and temporal resolution.