Abstract
Abstract. The recent release of the International Satellite Cloud Climatology Project (ISCCP) HXG cloud products and new ERA5 reanalysis data enabled us to produce a global surface solar radiation (SSR) dataset: a 16-year (2000–2015) high-resolution (3 h, 10 km) global SSR dataset using an improved physical parameterization scheme. The main inputs were cloud optical depth from ISCCP-HXG cloud products; the water vapor, surface pressure and ozone from ERA5 reanalysis data; and albedo and aerosol from Moderate Resolution Imaging Spectroradiometer (MODIS) products. The estimated SSR data were evaluated against surface observations measured at 42 stations of the Baseline Surface Radiation Network (BSRN) and 90 radiation stations of the China Meteorological Administration (CMA). Validation against the BSRN data indicated that the mean bias error (MBE), root mean square error (RMSE) and correlation coefficient (R) for the instantaneous SSR estimates at 10 km scale were −11.5 W m−2, 113.5 W m−2 and 0.92, respectively. When the estimated instantaneous SSR data were upscaled to 90 km, its error was clearly reduced, with RMSE decreasing to 93.4 W m−2 and R increasing to 0.95. For daily SSR estimates at 90 km scale, the MBE, RMSE and R at the BSRN were −5.8 W m−2, 33.1 W m−2 and 0.95, respectively. These error metrics at the CMA radiation stations were 2.1 W m−2, 26.9 W m−2 and 0.95, respectively. Comparisons with other global satellite radiation products indicated that our SSR estimates were generally better than those of the ISCCP flux dataset (ISCCP-FD), the global energy and water cycle experiment surface radiation budget (GEWEX-SRB), and the Earth's Radiant Energy System (CERES). Our SSR dataset will contribute to the land-surface process simulations and the photovoltaic applications in the future. The dataset is available at https://doi.org/10.11888/Meteoro.tpdc.270112 (Tang, 2019).
Highlights
Surface solar radiation (SSR), which drives the energy, water and carbon cycles of Earth’s system, is the driving input for simulations of hydrology, ecology, agriculture and landsurface processes (Wild, 2009; Wang et al, 2012)
To examine the effect of different spatial resolutions on the accuracy of our SSR estimates, in addition to the 10 km spatial resolution, we evaluated our estimated SSR at spatial resolutions of 30, 50, 70, 90 and 110 km, derived by averaging the SSR values observed at the original scale of 10 km
Validation against observations collected at Baseline Surface Radiation Network (BSRN) showed that the mean bias error (MBE) and root mean square error (RMSE) were −11.5 and 113.5 W m−2 for the instantaneous SSR estimates, and −6.1 and 38.0 W m−2 for the daily SSR estimates, but their accuracies clearly improved when upscaled to more than 30 km
Summary
Surface solar radiation (SSR), which drives the energy, water and carbon cycles of Earth’s system, is the driving input for simulations of hydrology, ecology, agriculture and landsurface processes (Wild, 2009; Wang et al, 2012). SSR is an important variable that affects the speed of glacier melting (Yang et al, 2011). Information on the spatiotemporal distribution of SSR is fundamental for the selection of sites for solar power plants, decisions on energy policy, optimization of solar power systems and operations management (Mondol et al, 2008; Sengupta et al, 2018). To address issues such as these, historical SSR data have been obtained mainly through ground-based
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