A Physics-Guided Neural Network Model to Estimate All-Sky Diffuse Solar Radiation Using Himawari-8 Data

Abstract

Diffuse solar radiation (DSR) is essential for carbon absorption in ecosystems and clean energy. Due to the scarcity of DSR observation stations, obtaining spatially continuous and high-accuracy all-sky DSR is a significant challenge. To achieve high-accuracy DSR estimation with limited observational data, this study developed a physics-guided deep learning (DL) algorithm. The algorithm effectively combines the advantages of the radiative transfer model (RTM) and DL and utilizes Himawari-8 top-of-atmosphere (TOA) reflectance and angular data as inputs to estimate DSR. Independent Baseline Surface Radiation Network (BSRN) and Wuhan University station observation validation results show that the algorithm has a high and robust performance in estimating instantaneous (hourly and daily) DSR, with a Pearson correlation coefficient (R) of 0.88 (0.91 and 0.91), a root-mean-square error (RMSE) of 61.84 (50.66 and 17.2) W/m2, and a mean bias error (MBE) of 0.16 (0.5 and −4.43) W/m2. In addition, compared to five existing DSR products (JiEA, CHSSDR, Deep Space Climate Observatory (DSCOVR)/Earth Polychromatic Imaging Camera (EPIC), ERA5, and CERES-SYN1deg), the algorithm shows the highest consistency (hourly $R=0.84$ and daily $R=0.86$ ) and the smallest biases (hourly MBE =9.22 W/m2 and daily MBE =4.9 W/m2) at China Meteorological Administration (CMA) stations. Furthermore, comparisons with Himawari-8’s cloud cover product and related DSR products confirm the spatial rationality and continuity of the estimated DSR by this algorithm. This study demonstrates the advantages of the physics-guided neural network (PGNN) over traditional DL in enhancing the accuracy and transferability of DSR estimation, highlighting its potential for application in DSR retrievals from other similar satellites.