Effects of clouds and aerosols on downwelling surface solar irradiance nowcasting and sort-term forecasting

Solar irradiance nowcasting and short-term forecasting are important tools for the integration of solar plants in the grid. Understanding the role of clouds and aerosols in those techniques is essential for improving their accuracy. In this study, we introduce the improvements in the existing nowcasting/short-term forecasting operational systems SENSE/NextSENSE, based on a new configuration and upgrading of cloud and aerosol inputs methods and also, we investigate the limitations of such model evaluation with surface-based sensors due to cloud effects. We assess the real-time estimates of surface global horizontal irradiance (GHI) produced by the improved SENSE2 operational system at high spatial and temporal resolution (~5 km, 15 min) for a domain including Europe and Middle East-North Africa (MENA) region and the short-term forecasts of GHI up to 3 h ahead by the NextSENSE2 system, against ground-based measurements from 10 stations across the model domain, for a whole year (2017). Results show that the GHI estimates are within +/-50 W/m2 (or +/-10 %) of the measured GHI for 61 % of the cases, after the new model configuration and a proposed bias correction. The bias ranges between -12 W/m2 to 23 W/m2 (or 2 % to 29 %) with mean value 11.3 W/m2 (2.3 %). The correlation coefficient is between 0.83 to 0.96 with mean value 0.93. Statistics are improved a lot when integrating in daily and monthly scales (mean bias 6.6 W/m2 and 5.7 W/m2, respectively). We demonstrate that the main overestimation of the SENSE2 GHI is linked with the underestimation of cloud optical thickness from the Meteosat Second Generation (MSG) satellites, while the relatively low overestimation linked with aerosol optical depth (AOD) forecasts (derived from Copernicus Atmospheric Monitoring Service – CAMS) results in low overestimation of clear sky GHI. The highest deviations are associated with cloudy atmospheric conditions with clouds obscuring the sun over the ground-based station. Thus, they are much more linked with satellite/ground-based comparison limitations than the actual model performance. The NextSENSE2 GHI forecasts based on the cloud motion vector (CMV) model, outperform the smart persistence forecasting method, which assumes the same cloud conditions for the future time steps. The forecasting skill (FS) of the CMV based model compared to the persistence approach increases with cloudiness (FS up to ~20 %), linked mostly to periods with changes in cloudiness, that persistence by definition fails to predict. Our results can be useful for further studies on satellite-based solar model evaluations and, in general, for the operational implementation of solar energy nowcasting and short-term forecasting, supporting solar energy production and management.