Accuracy of satellite-derived solar direct irradiance in Southern Spain and Switzerland

We present a validation study of direct normal irradiance (DNI) estimates from HelioMont with ground-based measurements from two European sites for the year 2015. The HelioMont algorithm infers irradiance with data from the Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument as the primary source of information on clouds, and data from models or reanalysis for other influential input parameters. The validation sites are the Plataforma Solar de Almería (PSA), a solar power research facility in Southern Spain characterized by arid conditions and the Swiss Baseline Surface Radiation Network (BSRN) site of Payerne, characterized by a much more frequent cloud coverage. Our analysis shows the importance of separately evaluating the quality of (1) the clear-sky irradiance computation and (2) the determination of the cloud effect. We also specifically investigate the cloud modification factor (CMF) using a validation CMF derived from ground-based data, giving us more insight into event-by-event agreement between HelioMont estimates and measured irradiances. The clear-sky HelioMont DNI uncertainty is mainly influenced by the aerosol optical depth (AOD) input data. Using the original AOD input (a 2008 climatology based on data from the Aerosol Comparisons between Observations and Models project) leads to large negative biases of 115 W m−2 to 145 W m−2. Using AOD from the Copernicus Atmosphere Monitoring Service (CAMS) allows reducing these biases to 15 W m−2 to 25 W m−2 (2% to 3%) with a dispersion of ±12% to ±15%, which is the HelioMont clear-sky DNI expanded uncertainty when using CAMS AOD. Using ground-measured AOD reduces this uncertainty to ±5% to ±6.5%, which is probably the limit of what is achievable with HelioMont. For all-sky comparisons, mean biases were between about −5 W m−2 and 55 W m−2 (depending on AOD input and station), while the root-mean-square deviation (RMSD) was between about 175 W m−2 and 195 W m−2. Our validation method yielded correlation between HelioMont and validation CMF between 0.79 and 0.92 (Pearson’s correlation coefficient r), while RMSD was between 0.18 and 0.24. The computation of the cloud effect is the part of HelioMont that is the main source of uncertainty. Systematic errors were identified (underestimation of the number of near-zero DNI and overestimation of the number of clear-sky cases) and solving them may lead to substantial improvement