Abstract
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.
Highlights
Downward shortwave radiation (RS) incident at the earth’s surface plays a vital role in the energy exchange between the land surface and atmosphere; RS drives the significant ecological and biophysical processes on the earth [1,2,3,4]
The random forest (RF) machine learning method is applied in this study to estimate RS at the daily and monthly time scales and 2-km spatial resolution using the Himawari-8 Advanced Himawari Imager (AHI) data and ancillary datasets
The results show that the overall R is 0.99, the root mean square error (RMSE) is 7.74 (4.09%) Wm−2, and the mean bias error (MBE) is 0.03 (0.02%) Wm−2
Summary
Downward shortwave radiation (RS) incident at the earth’s surface plays a vital role in the energy exchange between the land surface and atmosphere; RS drives the significant ecological and biophysical processes on the earth [1,2,3,4]. Ghimire et al [43] selected the support vector regression (SVR) method to estimate the RS using MODIS data; the result showed that it was a feasible way to apply the hybrid SVR model for obtaining RS using satellite observations. Shi et al [52] evaluated the RS product from the Himawari-8 using the Chinese Ecosystem Research Network (CERN) RS data; the results indicated that the officially released daily RS product had a mean bias error (MBE) value of 13.8 Wm−2 when compared to the CERN RS measurements. A short summary is presented in the last section of this paper
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