Performance assessment of clear-sky solar irradiance predictions using state-of-the-art radiation models and input atmospheric data from reanalysis or ground measurements

Abstract

In this work, the performance of clear-sky direct normal irradiance (DNI) and global horizontal irradiance (GHI) predictions generated with three state-of-the-art solar radiation models with different degrees of complexity is assessed by comparison with high-quality measured irradiance data at Évora, Portugal. The libRadtran, SMARTS, and REST2 radiation models are alternatively operated using input data from three different data sources: co-located AERONET ground-based measurements, and CAMS and MERRA-2 gridded reanalysis data. For these nine combinations (three models and three data sources), the results are assessed using five statistical indicators, namely mean bias error (MBE), root mean square error (RMSE), fractional bias (FB), fractional gross error (FGE), and coefficient of determination (R2). Overall, it is found that AERONET is the data source that provides the best DNI estimates. In general, libRadtran and SMARTS produced closer estimates to the ground-based DNI observations. For GHI, however, no firm conclusion can be drawn regarding the best data source. MERRA-2 produces better estimates in combination with libRadtran and SMARTS according to all statistical indicators except R2, whereas AERONET is to be preferred according to FB, FGE, and R2 when using REST2. Curiously, the latter generates better GHI estimates despite being the simplest model. Overall, it is concluded that the best combinations of model/data source to estimate DNI are either libRadtran/MERRA-2 (according to MBE and FB) and SMARTS/AERONET (according to RMSE, FGE, and R2). In the case of GHI, the best combinations are REST2/AERONET (according to FB, FGE, and R2) and REST2/MERRA-2 (according to MBE and RMSE).