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Investigating the role of aerosol wet scavenging on global horizontal irradiance simulation in the WRF-Chem-solar model

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  • Wang, Su
  • Huang, Gang
  • Dai, Tie
  • Cao, Junji
  • Husi, Letu
  • Ma, Run
  • Li, Cuina

Abstract

Solar energy emerges as a vital renewable resource with profound implications for future energy consumption. To improve the accuracy of global horizontal irradiance (GHI) simulation, this study integrates the Thompson and Eidhammer aerosol-aware cloud microphysics scheme with the aerosol module Goddard Ozone Chemistry Aerosol Radiation and Transport (GOCART) in the WRF-Solar model. Based on the new coupled aerosol-cloud interaction, a physical parametric aerosol wet scavenging scheme is developed to investigate its effects on aerosol and GHI simulations. Our results demonstrate that the updated wet removal enhances the spatial representation of aerosol optical depth (AOD) across China, particularly in regions influenced by anthropogenic aerosols such as Northern, Central and Eastern China. The average absolute mean bias (BIAS) value for AOD decreases by 20.00 %, leading to a 36.47 % improvement in reducing the overestimation of GHI under clear sky. The most remarkable BIAS reductions occur in Central (104.30 %), Western (45.70 %), and Eastern China (41.42 %) for GHI under clear sky, especially in high surface solar radiation zones (>500 W/m2). Similar improvements are observed for GHI under all-sky conditions, with a national relative improvement of 29.91 %. Central, Eastern, and Western China show the most substantial BIAS reductions, with relative decreases of 57.48 %, 50.62 %, and 32.40 %, respectively. Overall, this study highlights the potential of the enhanced aerosol wet scavenging scheme to improve GHI simulation accuracy, providing valuable insights for advancing renewable energy initiatives.

Suggested Citation

  • Wang, Su & Huang, Gang & Dai, Tie & Cao, Junji & Husi, Letu & Ma, Run & Li, Cuina, 2025. "Investigating the role of aerosol wet scavenging on global horizontal irradiance simulation in the WRF-Chem-solar model," Applied Energy, Elsevier, vol. 396(C).
    • Handle: RePEc:eee:appene:v:396:y:2025:i:c:s0306261925007925
    DOI: 10.1016/j.apenergy.2025.126062
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    References listed on IDEAS

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    1. Shi, Hongrong & Yang, Dazhi & Wang, Wenting & Fu, Disong & Gao, Ling & Zhang, Jinqiang & Hu, Bo & Shan, Yunpeng & Zhang, Yingjie & Bian, Yuxuan & Chen, Hongbin & Xia, Xiangao, 2023. "First estimation of high-resolution solar photovoltaic resource maps over China with Fengyun-4A satellite and machine learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    2. Diagne, Maimouna & David, Mathieu & Lauret, Philippe & Boland, John & Schmutz, Nicolas, 2013. "Review of solar irradiance forecasting methods and a proposition for small-scale insular grids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 65-76.
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    1. Fan, Shimeng & Zhang, Jiquan & Song, Ziming & Wei, Xiao & Bole, Yi & Liu, Xingpeng & Tong, Zhijun, 2026. "Assessment of solar energy resource potential in the western region of Jilin Province: A multi-dimensional framework based on availability, stability and concentration," Renewable Energy, Elsevier, vol. 256(PG).

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