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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Volume 11, issue 2 | Copyright

Special issue: SKYNET – the international network for aerosol, clouds,...

Atmos. Meas. Tech., 11, 907-924, 2018
https://doi.org/10.5194/amt-11-907-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 15 Feb 2018

Research article | 15 Feb 2018

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

Panagiotis G. Kosmopoulos1,2, Stelios Kazadzis3,1, Michael Taylor4, Panagiotis I. Raptis1,3, Iphigenia Keramitsoglou5, Chris Kiranoudis5,6, and Alkiviadis F. Bais2 Panagiotis G. Kosmopoulos et al.
  • 1Institute for Environmental Research and Sustainable Development, National Observatory of Athens, Athens, Greece
  • 2Laboratory of Atmospheric Physics, Aristotle University of Thessaloniki, Thessaloniki, Greece
  • 3Physicalisch-Meteorologisches Observatorium Davos, World Radiation Center, Davos, Switzerland
  • 4Department of Meteorology, University of Reading, Reading, UK
  • 5Institute for Astronomy, Astrophysics, Space Applications and Remote Sensing, National Observatory of Athens, Athens, Greece
  • 6School of Chemical Engineering, National Technical University of Athens, Athens, Greece

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 1min) 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 (1nm, 0.05°, 15min) 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 15min 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 20Wm−2, for the 15min 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.

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Continuous monitoring of solar energy from space is critical for its efficient exploitation and distribution. For this reason we developed neural-network- and function-based real-time models, which are capable of producing massive radiation outputs in high spectral, spatial and temporal resolution. The models' performance against ground-based measurements revealed a dependence on input quality and resolution, and an overall accuracy under cloudless and high solar energy potential conditions.
Continuous monitoring of solar energy from space is critical for its efficient exploitation and...
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