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Solar irradiation regionalization in Uruguay: Understanding the interannual variability and its relation to El Niño climatic phenomena

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  • Laguarda, A.
  • Alonso-Suárez, R.
  • Terra, R.

Abstract

A regionalization of different characteristics of the solar resource is performed for Uruguay and surrounding areas (Southeastern South America). The input information consists of daily satellite estimates of Global Horizontal Irradiation (GHI) generated in a regular grid using a low uncertainty empirical satellite-based model which was specifically adapted for the region. Clusters are derived from the climatological annual cycle of monthly irradiation and clearness index and, separately, from time-series of monthly variability. The solar irradiation variability in each cluster is compared with El Niño South Oscillation (ENSO) signal. A high negative correlation is observed between ENSO and solar irradiance, most predominantly over February to May and November to December, particularly for the latter. This means that in a strong El Niño/La Niña year, solar irradiation values for the November–December period in Uruguay will be smaller/higher than the climatological average. These results are in agreement with the ones obtained for rainfall in other studies.

Suggested Citation

  • Laguarda, A. & Alonso-Suárez, R. & Terra, R., 2020. "Solar irradiation regionalization in Uruguay: Understanding the interannual variability and its relation to El Niño climatic phenomena," Renewable Energy, Elsevier, vol. 158(C), pages 444-452.
    • Handle: RePEc:eee:renene:v:158:y:2020:i:c:p:444-452
    DOI: 10.1016/j.renene.2020.05.083
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    References listed on IDEAS

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    1. 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.
    2. Zagouras, Athanassios & Kolovos, Alexander & Coimbra, Carlos F.M., 2015. "Objective framework for optimal distribution of solar irradiance monitoring networks," Renewable Energy, Elsevier, vol. 80(C), pages 153-165.
    3. Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
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    Cited by:

    1. Tapia, Mariela & Heinemann, Detlev & Ballari, Daniela & Zondervan, Edwin, 2022. "Spatio-temporal characterization of long-term solar resource using spatial functional data analysis: Understanding the variability and complementarity of global horizontal irradiance in Ecuador," Renewable Energy, Elsevier, vol. 189(C), pages 1176-1193.
    2. Gonzalez-Salazar, Miguel & Poganietz, Witold Roger, 2021. "Evaluating the complementarity of solar, wind and hydropower to mitigate the impact of El Niño Southern Oscillation in Latin America," Renewable Energy, Elsevier, vol. 174(C), pages 453-467.
    3. Wei, Yu & Zhang, Jiahao & Chen, Yongfei & Wang, Yizhi, 2022. "The impacts of El Niño-southern oscillation on renewable energy stock markets: Evidence from quantile perspective," Energy, Elsevier, vol. 260(C).

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