the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 27 May 2026
Dust Radiative Effects and Impact on Energy Production over the Mediterranean Basin
Abstract. Atmospheric aerosols are among the key factors affecting the Earth’s radiation budget, playing a fundamental role in understanding climate forcing, feedback mechanisms, and their impact on future climate projections and on solar energy systems. More specifically, dust aerosol particles, which are characterized by high complexity of their optical and microphysical properties, remain one of the most uncertain components. In this study, we focus on four severe dust events across multiple sites in the broader Mediterranean Basin between 2021 and 2022. We employ a combination of ground-based
remote sensing observations along with Radiative Transfer (RT) modelling, with the libRadtran package and METAL-WRF scheme, as well as photovoltaic (PV) power generation simulations using the Global Solar Energy Estimator (GSEE) to investigate the impact of the different optical and geometrical aspects of these events on solar radiation and solar energy. The results revealed that the strongest dust-induced attenuation was systematically observed in the direct component of solar radiation (DNI), with maximum losses frequently exceeding 60–80%, while Global Horizontal Irradiance (GHI) typically ranged between 5% and 25%. These findings were reflected directly into substantial PV power output losses, for both fixed tilt and two-axis tracking systems, reaching ~45% and 80%, respectively, with the impact on the latter being significantly higher due to their strong dependence on DNI. A sensitivity analysis based on how aerosol optical properties and solar geometry jointly influence PV energy production revealed that Solar Zenith Angle (SZA) plays the most dominant role, followed by Aerosol Optical Depth (AOD), which leads to strong attenuation independently of SZA under altered aerosol load conditions. Finally, the comparison of the modelled PV output estimated from the modelled irradiances based on the two different RT models with the PV output considering ground-based GHI measurements revealed a similar agreement under clear sky conditions, while under cloudy conditions, the analysis revealed the critical role of the diffuse horizontal irradiance (DHI) component in the simulations.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Atmospheric Measurement Techniques.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.- Preprint
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Status: open (until 03 Jul 2026)
- RC1: 'Comment on egusphere-2026-2775', Anonymous Referee #1, 11 Jun 2026 reply
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- 1
I found the manuscript interesting and generally well written. In particular, the Introduction provides a clear and informative overview of the scientific background and motivation of the study. I recommend a number of clarifications and minor revisions that would improve the manuscript's clarity, reproducibility, and overall presentation prior to publication.
MAJOR COMMENTS:
- Please include a clear definition of the reported losses, preferably inlcuding the corresponding equation. The reference used to compute the percentage losses should be explicitly stated. Some related definitions appear later in the manuscript (Line 375), but this concept should be introduced earlier and described in greater detail.
- A map showing the geographical distribution of the considered sites would help the reader better understand the study area and the analysis presented in Section 2.
- Methodology: a description of the broadband radiation measurements is missing. Please provide information on the measurement sites, instruments/sensors, measured variables, quality-control procedures, and any other relevant details.
- How is GTI obtained from SSR? Please clarify the procedure used for this conversion, as this step can introduce significant uncertainties. If this calculation is performed internally within GSEE, please state so and briefly describe the methodology in the Section 2.
-The description of the GSEE implementation is not sufficiently detailed. Please specify explicitly which irradiance components (GHI, DHI, DNI and/or plane-of-array irradiance) were provided as inputs to GSEE for each simulation setup (measurements, libRadtran and METAL-WRF), and clarify whether the transposition to the PV plane was performed internally by GSEE. Also include some details in the PV generation model.
- Figure 4: please clarify what is meant by "solar energy loss". Does it refer to tilted irradiance, PV energy production, or another quantity? Also indicate the reference used for the normalization and percentage calculations.
- Please include the optimum fixed tilt angle used at each site.
- Please provide more details on the clear-sky selection procedure.
MINOR COMMENTS:
- L35 - Please clarify that the sensitivity analysis is performed under clear skies conditions
- Fig.1. the figure is difficult to interpret. Is the color coding important for the analysis? If so, please clarify its meaning and improve readability.
- There is a missmatch between figure numbering and the figure references in the text. (e.g. Figures 5, 6 an 7). Please check figure numbering throughout the manuscript. Also, use a consistent format when referring to figures. For example Fig. X
- Ssec 3.3 Please refer to figures in order of appearance. For example, Fig. 11 appears referenced before Fig. 9.
- L260: fixed tilt: which angle?
- Fig. 9 and Fig. 11: please indicate which days are considered clear-sky cases.
- In the sensitivity analysis, please specify the wavelength associated with the reported AOD values.
- Fig. 11 and Fig. 1comparing outputs from a clear sky radiative transfer model (libRadtran in this case) with measurements affected by cloudiness is difficult to interpret and does not provide a meaningful assessment of model performance.
- conclusions: consider removing "Under cloudy conditions, the comparisons lead to greater differences and an overall overestimation in the case of libRadtran", as this result is expected when comparing clear-sky simulations with cloudy-sky observations.
SUGGESTIONS:
- Consider including a glossary of the main acronyms and variables used throughout the manuscript.
- L120: is there any estimate of the annual energy losses associated with dust events over the study region? Although outside the main scope of this work, such information could help place the reported event-scale impacts into a broader context.
- Since completely clear days are relatively scarce, the authors could consider using clear-sky periods selected from different days (even when a full day is not entirely cloudless) through the application of a clear-sky detection algorithm. This could potentially increase the amount of data available for the final analysis.