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. This study focuses on the assessment of surface solar radiation (SSR) basedon operational neural network (NN) and multi-regression function (MRF)modelling techniques that produce instantaneous (in less than 1 min)outputs. Using real-time cloud and aerosol optical properties inputs from theSpinning Enhanced Visible and Infrared Imager (SEVIRI) on board the MeteosatSecond Generation (MSG) satellite and the Copernicus Atmosphere MonitoringService (CAMS), respectively, these models are capable of calculating SSR inhigh resolution (1 nm, 0.05 ∘ , 15 min) that can be used forspectrally integrated irradiance maps, databases and various applicationsrelated to energy exploitation. The real-time models are validated againstground-based measurements of the Baseline Surface Radiation Network (BSRN) ina temporal range varying from 15 min to monthly means, while a sensitivityanalysis of the cloud and aerosol effects on SSR is performed to ensurereliability under different sky and climatological conditions. The simulatedoutputs, compared to their common training dataset created by the radiativetransfer model (RTM) libRadtran, showed median error values in the range − 15to 15 % for the NN that produces spectral irradiances (NNS), 5–6 %underestimation for the integrated NN and close to zero errors for the MRFtechnique. The verification against BSRN revealed that the real-timecalculation uncertainty ranges from − 100 to 40 and − 20 to 20 W m −2 , for the 15 min and monthly mean global horizontal irradiance (GHI)averages, respectively, while the accuracy of the input parameters, in termsof aerosol and cloud optical thickness (AOD and COT), and their impact onGHI, was of the order of 10 % as compared to the ground-based measurements.The proposed system aims to be utilized through studies and real-timeapplications which are related to solar energy production planning anduse.

Assessment of surface solar irradiance derived from real-time modelling techniques and verification with ground-based measurements