Assessment of surface solar irradiance derived from real-time modelling techniques and verification with ground-based measurements
Abstract. This study focuses on the assessment of surface solar radiation (SSR) based
on 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 the
Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the Meteosat
Second Generation (MSG) satellite and the Copernicus Atmosphere Monitoring
Service (CAMS), respectively, these models are capable of calculating SSR in
high resolution (1 nm, 0.05 ∘ , 15 min) that can be used for
spectrally integrated irradiance maps, databases and various applications
related to energy exploitation. The real-time models are validated against
ground-based measurements of the Baseline Surface Radiation Network (BSRN) in
a temporal range varying from 15 min to monthly means, while a sensitivity
analysis of the cloud and aerosol effects on SSR is performed to ensure
reliability under different sky and climatological conditions. The simulated
outputs, compared to their common training dataset created by the radiative
transfer model (RTM) libRadtran, showed median error values in the range − 15
to 15 % for the NN that produces spectral irradiances (NNS), 5–6 %
underestimation for the integrated NN and close to zero errors for the MRF
technique. The verification against BSRN revealed that the real-time
calculation 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 terms
of aerosol and cloud optical thickness (AOD and COT), and their impact on
GHI, was of the order of 10 % as compared to the ground-based measurements.
The proposed system aims to be utilized through studies and real-time
applications which are related to solar energy production planning and
use.