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Sensitivity of satellite-based methods for deriving solar radiation to different choice of aerosol input and models

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  • Polo, J.
  • Antonanzas-Torres, F.
  • Vindel, J.M.
  • Ramirez, L.

Abstract

This paper presents a sensitivity analysis of satellite-based methods for deriving solar irradiance components for analyzing the impact of the external inputs that are normally associated to the satellite model. Different sensitivity calculations have been performed using as reference site the PSA (Solar Platform of Almeria) station placed at the south-east of Spain. Thus, the sensitivity to the aerosol information input has been addressed by comparing the estimations using aerosol input from AERONET data with those using aerosol dataset such as MODIS or MISR (based on satellite) and MACC (based on reanalysis). Sensitivity to the clear sky model choice has been also studied by using three different models, from the simpler ESRA model (in terms of input parameters) to the most sophisticated REST2. Finally, three global to direct conversion models (Louche, DirInt and DirIndex) have been included to explore the sensitivity of the direct normal irradiance estimations. The sensitivity analysis has shown the interrelations between the different cases according to the uncertainty of the input information used. The results have been analyzed for clear and non-clear sky conditions separately and for the DNI irradiance range of 400–900 W m−2 as a case of special interest for the concentrating solar power applications. The work presented here has as novelty the analysis of the propagation of uncertainty of individual models and atmospheric datasets in the framework of a satellite-based model for solar irradiance computation and their relative weights to the final performance of the model. An underestimation of AOD by 50% causes an error in the global horizontal irradiance calculated by a clear sky model of 3–5% depending on the model used, and slightly less for an overestimation of AOD. For DNI the error ranges are 12–15% and 9–12% for 50% underestimation and overestimation of AOD respectively.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:68:y:2014:i:c:p:785-792
    DOI: 10.1016/j.renene.2014.03.022
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    1. 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.
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    1. Cornejo, Lorena & Martín-Pomares, Luis & Alarcon, Diego & Blanco, Julián & Polo, Jesús, 2017. "A through analysis of solar irradiation measurements in the region of Arica Parinacota, Chile," Renewable Energy, Elsevier, vol. 112(C), pages 197-208.
    2. Diane Palmer & Richard Blanchard, 2021. "Evaluation of High-Resolution Satellite-Derived Solar Radiation Data for PV Performance Simulation in East Africa," Sustainability, MDPI, vol. 13(21), pages 1-24, October.
    3. Polo, J. & Gastón, M. & Vindel, J.M. & Pagola, I., 2015. "Spatial variability and clustering of global solar irradiation in Vietnam from sunshine duration measurements," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 1326-1334.
    4. Gueymard, Christian A. & Bright, Jamie M. & Lingfors, David & Habte, Aron & Sengupta, Manajit, 2019. "A posteriori clear-sky identification methods in solar irradiance time series: Review and preliminary validation using sky imagers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 412-427.
    5. 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.
    6. 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.
    7. Alonso-Montesinos, J. & Batlles, F.J., 2015. "The use of a sky camera for solar radiation estimation based on digital image processing," Energy, Elsevier, vol. 90(P1), pages 377-386.
    8. Psiloglou, B.E. & Kambezidis, H.D. & Kaskaoutis, D.G. & Karagiannis, D. & Polo, J.M., 2020. "Comparison between MRM simulations, CAMS and PVGIS databases with measured solar radiation components at the Methoni station, Greece," Renewable Energy, Elsevier, vol. 146(C), pages 1372-1391.
    9. Linares-Rodriguez, Alvaro & Quesada-Ruiz, Samuel & Pozo-Vazquez, David & Tovar-Pescador, Joaquin, 2015. "An evolutionary artificial neural network ensemble model for estimating hourly direct normal irradiances from meteosat imagery," Energy, Elsevier, vol. 91(C), pages 264-273.
    10. 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.
    11. Armando Castillejo-Cuberos & John Boland & Rodrigo Escobar, 2021. "Short-Term Deterministic Solar Irradiance Forecasting Considering a Heuristics-Based, Operational Approach," Energies, MDPI, vol. 14(18), pages 1-24, September.
    12. Antonanzas-Torres, F. & Sanz-Garcia, A. & Martínez-de-Pisón, F.J. & Antonanzas, J. & Perpiñán-Lamigueiro, O. & Polo, J., 2014. "Towards downscaling of aerosol gridded dataset for improving solar resource assessment, an application to Spain," Renewable Energy, Elsevier, vol. 71(C), pages 534-544.
    13. Antonanzas-Torres, F. & Urraca, R. & Polo, J. & Perpiñán-Lamigueiro, O. & Escobar, R., 2019. "Clear sky solar irradiance models: A review of seventy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 107(C), pages 374-387.
    14. Alonso-Suárez, R. & David, M. & Branco, V. & Lauret, P., 2020. "Intra-day solar probabilistic forecasts including local short-term variability and satellite information," Renewable Energy, Elsevier, vol. 158(C), pages 554-573.
    15. Martín-Pomares, Luis & Martínez, Diego & Polo, Jesús & Perez-Astudillo, Daniel & Bachour, Dunia & Sanfilippo, Antonio, 2017. "Analysis of the long-term solar potential for electricity generation in Qatar," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1231-1246.
    16. Mazorra Aguiar, L. & Polo, J. & Vindel, J.M. & Oliver, A., 2019. "Analysis of satellite derived solar irradiance in islands with site adaptation techniques for improving the uncertainty," Renewable Energy, Elsevier, vol. 135(C), pages 98-107.
    17. Cheng, Xinghong & Ye, Dong & Shen, Yanbo & Li, Deping & Feng, Jinming, 2022. "Studies on the improvement of modelled solar radiation and the attenuation effect of aerosol using the WRF-Solar model with satellite-based AOD data over north China," Renewable Energy, Elsevier, vol. 196(C), pages 358-365.
    18. Polo, J. & Téllez, F.M. & Tapia, C., 2016. "Comparative analysis of long-term solar resource and CSP production for bankability," Renewable Energy, Elsevier, vol. 90(C), pages 38-45.

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