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The shadow of the wind: the impact of Saharan dust on photovoltaic power generation in the Mediterranean

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Listed:
  • Varga, György
  • Gresina, Fruzsina
  • Gelencsér, András
  • Csávics, Adrienn
  • Rostási, Ágnes

Abstract

The increasing frequency and intensity of Saharan Dust Events (SDEs) in Europe poses a significant challenge to the reliability of photovoltaic (PV) energy systems. This study examines SDE effects on solar power in five Mediterranean countries – Portugal, Spain, France, Italy, and Greece – from 2019 to 2023, using dust data, PV statistics, and meteorological analysis. The findings of this study demonstrate that SDEs reduce PV output by an average of 25–40 %, with losses exceeding 50 % during extreme events. In Portugal this decline was 10.1–29.3 %, 16.3–19.8 % in Spain; 4.4–40.5 % in France; 13.9–36.8 % in Italy and 20.1–40.9 % in Greece, during the highest dust levels. Solar irradiance drops are due to both dust-induced attenuation and increased cirrus cloud formation from enhanced ice nucleation. Analysis of recent dust storms shows consistent day-ahead PV forecast errors, with underestimations up to −15 % in Portugal and Spain, and overestimations up to +10 % in Italy and Greece, highlighting the need for improved models that incorporate aerosol-cloud interactions. The results highlight the need for improved forecasting that includes real-time dust monitoring and cloud processes. As SDEs increase with climate change, accounting for dust-related uncertainties is crucial for reliable grid operation and solar power planning in southern Europe.

Suggested Citation

  • Varga, György & Gresina, Fruzsina & Gelencsér, András & Csávics, Adrienn & Rostási, Ágnes, 2026. "The shadow of the wind: the impact of Saharan dust on photovoltaic power generation in the Mediterranean," Renewable Energy, Elsevier, vol. 256(PF).
  • Handle: RePEc:eee:renene:v:256:y:2026:i:pf:s0960148125020014
    DOI: 10.1016/j.renene.2025.124337
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    References listed on IDEAS

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    1. James D. Atkinson & Benjamin J. Murray & Matthew T. Woodhouse & Thomas F. Whale & Kelly J. Baustian & Kenneth S. Carslaw & Steven Dobbie & Daniel O’Sullivan & Tamsin L. Malkin, 2013. "The importance of feldspar for ice nucleation by mineral dust in mixed-phase clouds," Nature, Nature, vol. 498(7454), pages 355-358, June.
    2. Mayer, Martin János & Gróf, Gyula, 2021. "Extensive comparison of physical models for photovoltaic power forecasting," Applied Energy, Elsevier, vol. 283(C).
    3. James D. Atkinson & Benjamin J. Murray & Matthew T. Woodhouse & Thomas F. Whale & Kelly J. Baustian & Kenneth S. Carslaw & Steven Dobbie & Daniel O’Sullivan & Tamsin L. Malkin, 2013. "Erratum: The importance of feldspar for ice nucleation by mineral dust in mixed-phase clouds," Nature, Nature, vol. 500(7463), pages 490-490, August.
    4. Varga, György & Gresina, Fruzsina & Szeberényi, József & Gelencsér, András & Rostási, Ágnes, 2024. "Effect of Saharan dust episodes on the accuracy of photovoltaic energy production forecast in Hungary (Central Europe)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).
    5. Varga, György & Gresina, Fruzsina & Gelencsér, András & Csávics, Adrienn & Rostási, Ágnes, 2025. "Desert dust and photovoltaic energy forecasts: Lessons from 46 Saharan dust events in Hungary (Central Europe)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 212(C).
    6. Femke J. M. M. Nijsse & Jean-Francois Mercure & Nadia Ameli & Francesca Larosa & Sumit Kothari & Jamie Rickman & Pim Vercoulen & Hector Pollitt, 2023. "The momentum of the solar energy transition," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    7. Markovics, Dávid & Mayer, Martin János, 2022. "Comparison of machine learning methods for photovoltaic power forecasting based on numerical weather prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
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