Daily Global Solar Radiation in China Estimated From High-Density Meteorological Observations: A Random Forest Model Framework

Zhaoliang Zeng, Zemin Wang*, Ke Gui, Xiaoyu Yan, Meng Gao, Ming Luo, Hong Geng, Tingting Liao, Xiao Li, Jiachun An, Haizhi Liu, Chao He, Guicai Ning, Yuanjian Yang*

*Corresponding author for this work

    Research output: Contribution to journalJournal articlepeer-review

    51 Citations (Scopus)


    Accurate estimation of the spatiotemporal variations of solar radiation is crucial for assessing and utilizing solar energy, one of the fastest-growing and most important clean and renewable resources. Based on observations from 2,379 meteorological stations along with scare solar radiation observations, the random forest (RF) model is employed to construct a high-density network of daily global solar radiation (DGSR) and its spatiotemporal variations in China. The RF-estimated DGSR is in good agreement with site observations across China, with an overall correlation coefficient (R) of 0.95, root-mean-square error of 2.34 MJ/m2, and mean bias of −0.04 MJ/m2. The geographical distributions of R values, root-mean-square error, and mean bias values indicate that the RF model has high predictive performance in estimating DGSR under different climatic and geographic conditions across China. The RF model further reveals that daily sunshine duration, daily maximum land surface temperature, and day of year play dominant roles in determining DGSR across China. In addition, compared with other models, the RF model exhibits a more accurate estimation performance for DGSR. Using the RF model framework at the national scale allows the establishment of a high-resolution DGSR network, which can not only be used to effectively evaluate the long-term change in solar radiation but also serve as a potential resource to rationally and continually utilize solar energy.

    Original languageEnglish
    Article numbere2019EA001058
    JournalEarth and Space Science
    Issue number2
    Publication statusPublished - 1 Feb 2020

    Scopus Subject Areas

    • Environmental Science (miscellaneous)
    • Earth and Planetary Sciences(all)

    User-Defined Keywords

    • global solar radiation
    • high-density meteorological observations
    • random forest
    • selection of variables


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