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Meteorological Radiation Model (MRM v6.1): Improvements in diffuse radiation estimates and a new approach for implementation of cloud products

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  • Kambezidis, H.D.
  • Psiloglou, B.E.
  • Karagiannis, D.
  • Dumka, U.C.
  • Kaskaoutis, D.G.

Abstract

This work reviews techniques and models for solar radiation simulations and communicates further improvements performed in the Meteorological Radiation Model (MRM) developed at the National Observatory of Athens. The new version (MRM v6.1) considers a forward to-the-ground scattering as a function of solar altitude for the diffuse radiation estimates, while concurrently uses two running codes i.e., with sunshine duration as input (MRM v6.1a) and with cloud products (MRM v6.1b). The new scattering function leads to increase in diffuse radiation, especially for low zenith angles, and to better simulations with the measured diffuse (RMSE=36.3% and MBE=6.5%, against RMSE=42.0% and MBE=22.3% for the latest MRM v6). These changes lead also to better simulations of global radiation (RMSE=8.7% against RMSE=9.5% for MRM v6), while the direct radiation is not affected. The accuracy in the simulations increases significantly for clear-sky conditions, while it shows a small dependence on aerosol amount and solar altitude. Furthermore, in the sub-version MRM v6.1b the calculations for the cloud transmittance have been modified to allow for the inclusion of cloud products as inputs in case of non-availability of sunshine duration data. In this study, MRM v6.1b uses MERRA retrievals of cloud optical depth and cloud fraction for calculations of the cloud transmittance; these parameters usually lead to significant uncertainties in the simulations of the hourly direct and global radiations, especially for large cloud fractions. This indicates the need for high spatial and temporal resolution satellite data of cloudiness for accurate estimations of solar radiation under cloudy conditions and highlights the incapability of radiative transfer models on such simulations. However, on monthly basis both MRM v6.1a and v6.1b provide high accuracy in solar radiation estimates, thus rendering MRM v6.1 as a powerful tool for solar energy applications.

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  • Kambezidis, H.D. & Psiloglou, B.E. & Karagiannis, D. & Dumka, U.C. & Kaskaoutis, D.G., 2017. "Meteorological Radiation Model (MRM v6.1): Improvements in diffuse radiation estimates and a new approach for implementation of cloud products," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 616-637.
    • Handle: RePEc:eee:rensus:v:74:y:2017:i:c:p:616-637
    DOI: 10.1016/j.rser.2017.02.058
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    as
    1. Khatib, Tamer & Mohamed, Azah & Sopian, K., 2012. "A review of solar energy modeling techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(5), pages 2864-2869.
    2. Eltbaakh, Yousef A. & Ruslan, M.H. & Alghoul, M.A. & Othman, M.Y. & Sopian, K., 2012. "Issues concerning atmospheric turbidity indices," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(8), pages 6285-6294.
    3. Kambezidis, H.D. & Psiloglou, B.E. & Karagiannis, D. & Dumka, U.C. & Kaskaoutis, D.G., 2016. "Recent improvements of the Meteorological Radiation Model for solar irradiance estimates under all-sky conditions," Renewable Energy, Elsevier, vol. 93(C), pages 142-158.
    4. Hay, John E., 1993. "Satellite based estimates of solar irradiance at the earth's surface—II. Mapping of solar radiation," Renewable Energy, Elsevier, vol. 3(4), pages 395-398.
    5. Hay, John E., 1993. "Satellite based estimates of solar irradiance at the earth's surface—I. Modelling approaches," Renewable Energy, Elsevier, vol. 3(4), pages 381-393.
    6. Bahel, V. & Srinivasan, R. & Bakhsh, H., 1987. "Statistical comparison of correlations for estimation of global horizontal solar radiation," Energy, Elsevier, vol. 12(12), pages 1309-1316.
    7. Linares-Rodriguez, Alvaro & Ruiz-Arias, José Antonio & Pozo-Vazquez, David & Tovar-Pescador, Joaquin, 2013. "An artificial neural network ensemble model for estimating global solar radiation from Meteosat satellite images," Energy, Elsevier, vol. 61(C), pages 636-645.
    8. Muneer, T. & Younes, S. & Munawwar, S., 2007. "Discourses on solar radiation modeling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 11(4), pages 551-602, May.
    9. Yadav, Amit Kumar & Malik, Hasmat & Chandel, S.S., 2015. "Application of rapid miner in ANN based prediction of solar radiation for assessment of solar energy resource potential of 76 sites in Northwestern India," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1093-1106.
    10. Olatomiwa, Lanre & Mekhilef, Saad & Shamshirband, Shahaboddin & Petković, Dalibor, 2015. "Adaptive neuro-fuzzy approach for solar radiation prediction in Nigeria," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1784-1791.
    11. Besharat, Fariba & Dehghan, Ali A. & Faghih, Ahmad R., 2013. "Empirical models for estimating global solar radiation: A review and case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 798-821.
    12. Li, Huashan & Bu, Xianbiao & Lian, Yongwang & Zhao, Liang & Ma, Weibin, 2012. "Further investigation of empirically derived models with multiple predictors in estimating monthly average daily diffuse solar radiation over China," Renewable Energy, Elsevier, vol. 44(C), pages 469-473.
    13. Almorox, J. & Hontoria, C. & Benito, M., 2011. "Models for obtaining daily global solar radiation with measured air temperature data in Madrid (Spain)," Applied Energy, Elsevier, vol. 88(5), pages 1703-1709, May.
    14. Vernay, Christophe & Blanc, Philippe & Pitaval, Sébastien, 2013. "Characterizing measurements campaigns for an innovative calibration approach of the global horizontal irradiation estimated by HelioClim-3," Renewable Energy, Elsevier, vol. 57(C), pages 339-347.
    15. Jacovides, C.P. & Kaskaoutis, D.G. & Tymvios, F.S. & Asimakopoulos, D.N., 2004. "Application of SPCTRAL2 parametric model in estimating spectral solar irradiances over polluted Athens atmosphere," Renewable Energy, Elsevier, vol. 29(7), pages 1109-1119.
    16. Kambezidis, H.D. & Psiloglou, B.E. & Synodinou, B.M., 1997. "Comparison between measurements and models for daily solar irradiation on tilted surfaces in Athens, Greece," Renewable Energy, Elsevier, vol. 10(4), pages 505-518.
    17. Tsoutsos, Theocharis & Frantzeskaki, Niki & Gekas, Vassilis, 2005. "Environmental impacts from the solar energy technologies," Energy Policy, Elsevier, vol. 33(3), pages 289-296, February.
    18. Janjai, S. & Praditwong, P. & Moonin, C., 1996. "A new model for computing monthly average daily diffuse radiation for Bangkok," Renewable Energy, Elsevier, vol. 9(1), pages 1283-1286.
    19. Xu, Xiaojun & Du, Huaqiang & Zhou, Guomo & Mao, Fangjie & Li, Pingheng & Fan, Weiliang & Zhu, Dien, 2016. "A method for daily global solar radiation estimation from two instantaneous values using MODIS atmospheric products," Energy, Elsevier, vol. 111(C), pages 117-125.
    20. Yacef, R. & Benghanem, M. & Mellit, A., 2012. "Prediction of daily global solar irradiation data using Bayesian neural network: A comparative study," Renewable Energy, Elsevier, vol. 48(C), pages 146-154.
    21. Kaskaoutis, D.G. & Kambezidis, H.D., 2008. "The role of aerosol models of the SMARTS code in predicting the spectral direct-beam irradiance in an urban area," Renewable Energy, Elsevier, vol. 33(7), pages 1532-1543.
    22. Wang, Lunche & Kisi, Ozgur & Zounemat-Kermani, Mohammad & Salazar, Germán Ariel & Zhu, Zhongmin & Gong, Wei, 2016. "Solar radiation prediction using different techniques: model evaluation and comparison," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 384-397.
    23. Pérez-Higueras, P.J. & Rodrigo, P. & Fernández, E.F. & Almonacid, F. & Hontoria, L., 2012. "A simplified method for estimating direct normal solar irradiation from global horizontal irradiation useful for CPV applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(8), pages 5529-5534.
    24. Polo, J. & Antonanzas-Torres, F. & Vindel, J.M. & Ramirez, L., 2014. "Sensitivity of satellite-based methods for deriving solar radiation to different choice of aerosol input and models," Renewable Energy, Elsevier, vol. 68(C), pages 785-792.
    25. Teke, Ahmet & Yıldırım, H. Başak & Çelik, Özgür, 2015. "Evaluation and performance comparison of different models for the estimation of solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1097-1107.
    26. Zervas, P.L. & Sarimveis, H. & Palyvos, J.A. & Markatos, N.C.G., 2008. "Prediction of daily global solar irradiance on horizontal surfaces based on neural-network techniques," Renewable Energy, Elsevier, vol. 33(8), pages 1796-1803.
    27. Li, Huashan & Ma, Weibin & Lian, Yongwang & Wang, Xianlong & Zhao, Liang, 2011. "Global solar radiation estimation with sunshine duration in Tibet, China," Renewable Energy, Elsevier, vol. 36(11), pages 3141-3145.
    28. Polo, J. & Martín, L. & Vindel, J.M., 2015. "Correcting satellite derived DNI with systematic and seasonal deviations: Application to India," Renewable Energy, Elsevier, vol. 80(C), pages 238-243.
    29. Boilley, Alexandre & Wald, Lucien, 2015. "Comparison between meteorological re-analyses from ERA-Interim and MERRA and measurements of daily solar irradiation at surface," Renewable Energy, Elsevier, vol. 75(C), pages 135-143.
    30. Torres, J.L. & De Blas, M. & García, A. & de Francisco, A., 2010. "Comparative study of various models in estimating hourly diffuse solar irradiance," Renewable Energy, Elsevier, vol. 35(6), pages 1325-1332.
    31. Batlles, F.J. & Rubio, M.A. & Tovar, J. & Olmo, F.J. & Alados-Arboledas, L., 2000. "Empirical modeling of hourly direct irradiance by means of hourly global irradiance," Energy, Elsevier, vol. 25(7), pages 675-688.
    32. Gueymard, Christian A., 2005. "Interdisciplinary applications of a versatile spectral solar irradiance model: A review," Energy, Elsevier, vol. 30(9), pages 1551-1576.
    33. Bahel, V. & Bakhsh, H. & Srinivasan, R., 1987. "A correlation for estimation of global solar radiation," Energy, Elsevier, vol. 12(2), pages 131-135.
    34. Kosmopoulos, P.G. & Kazadzis, S. & Lagouvardos, K. & Kotroni, V. & Bais, A., 2015. "Solar energy prediction and verification using operational model forecasts and ground-based solar measurements," Energy, Elsevier, vol. 93(P2), pages 1918-1930.
    35. Akarslan, Emre & Hocaoglu, Fatih Onur, 2016. "A novel adaptive approach for hourly solar radiation forecasting," Renewable Energy, Elsevier, vol. 87(P1), pages 628-633.
    36. Kaplanis, S. & Kaplani, E., 2007. "A model to predict expected mean and stochastic hourly global solar radiation I(h;nj) values," Renewable Energy, Elsevier, vol. 32(8), pages 1414-1425.
    37. Hu, Eric & Yang, YongPing & Nishimura, Akira & Yilmaz, Ferdi & Kouzani, Abbas, 2010. "Solar thermal aided power generation," Applied Energy, Elsevier, vol. 87(9), pages 2881-2885, September.
    38. Lina M. Mercado & Nicolas Bellouin & Stephen Sitch & Olivier Boucher & Chris Huntingford & Martin Wild & Peter M. Cox, 2009. "Impact of changes in diffuse radiation on the global land carbon sink," Nature, Nature, vol. 458(7241), pages 1014-1017, April.
    39. López, Gabriel & Rubio, Miguel Angel & Batlles, Francisco J., 2000. "Estimation of hourly direct normal from measured global solar irradiance in Spain," Renewable Energy, Elsevier, vol. 21(2), pages 175-186.
    40. Chen, Rensheng & Kang, Ersi & Ji, Xibin & Yang, Jianping & Wang, Junhai, 2007. "An hourly solar radiation model under actual weather and terrain conditions: A case study in Heihe river basin," Energy, Elsevier, vol. 32(7), pages 1148-1157.
    41. Li, Huashan & Ma, Weibin & Lian, Yongwang & Wang, Xianlong, 2010. "Estimating daily global solar radiation by day of year in China," Applied Energy, Elsevier, vol. 87(10), pages 3011-3017, October.
    42. Benghanem, Mohamed & Mellit, Adel, 2010. "Radial Basis Function Network-based prediction of global solar radiation data: Application for sizing of a stand-alone photovoltaic system at Al-Madinah, Saudi Arabia," Energy, Elsevier, vol. 35(9), pages 3751-3762.
    Full references (including those not matched with items on IDEAS)

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