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Parallax and cloud shadow correction in satellite-based solar irradiance estimation: A study in tropical environments

Author

Listed:
  • Roy, Arindam
  • Hammer, Annette
  • Heinemann, Detlev
  • Schroedter-Homscheidt, Marion
  • Lünsdorf, Ontje
  • Lezaca, Jorge

Abstract

Accurate estimation of Global horizontal solar irradiance (GHI) from geostationary satellite imagery is essential for intraday solar PV power forecasting. Tropical regions show an even more challenging situation: A typically much higher tropopause results in higher cloud tops and correspondingly larger parallax errors in satellite imagery with significantly larger cloud shadow displacements compared to mid-latitudes. This study improves GHI estimates from Meteosat-8 by correcting cloud parallax and shadow displacement using gridded cloud top height (CTH) data. Fractional or sub-pixel displacement of individual cloudy pixels is enabled by bilinear interpolation in contrast to prior methods that allowed only integer shifts or assigned a single CTH value to a grouping of adjacent cloud pixels. Validation against one year of 15-min resolution ground-based measurements at five sites in South and Southeast Asia shows a reduction in relative root mean square error (rel. RMSE) from 23.8 % to 22.1 %. Improvements are more pronounced at higher satellite viewing zenith angles (θsza) and in the presence of high-altitude clouds. The corrected satellite-based GHI exhibits 4–7 percentage points lower rel. RMSE than National Solar Radiation Database (NSRDB) and 2.5 points lower than CAMS solar radiation service for similar θsza. Greatest error reductions occur during partly cloudy conditions for sites within 61° θsza, and under overcast skies for sites close to the edge of Meteosat-8's field of view. Improvements also depend on the co-scattering angle between sun and satellite with respect to the site, and the availability of sufficient upstream cloud information along the path of solar irradiance falling on the site. Ramp detection accuracy improves, particularly at lower detection thresholds, as measured using the Swinging Door Algorithm.

Suggested Citation

  • Roy, Arindam & Hammer, Annette & Heinemann, Detlev & Schroedter-Homscheidt, Marion & Lünsdorf, Ontje & Lezaca, Jorge, 2025. "Parallax and cloud shadow correction in satellite-based solar irradiance estimation: A study in tropical environments," Applied Energy, Elsevier, vol. 399(C).
    • Handle: RePEc:eee:appene:v:399:y:2025:i:c:s0306261925011870
    DOI: 10.1016/j.apenergy.2025.126457
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    References listed on IDEAS

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    1. Nespoli, Alfredo & Niccolai, Alessandro & Ogliari, Emanuele & Perego, Giovanni & Collino, Elena & Ronzio, Dario, 2022. "Machine Learning techniques for solar irradiation nowcasting: Cloud type classification forecast through satellite data and imagery," Applied Energy, Elsevier, vol. 305(C).
    2. Sengupta, Manajit & Xie, Yu & Lopez, Anthony & Habte, Aron & Maclaurin, Galen & Shelby, James, 2018. "The National Solar Radiation Data Base (NSRDB)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 89(C), pages 51-60.
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