Volume 43 Issue 3
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Article Navigation >  Chinese Journal of Space Science  > 2023  >  43(3): 434-445 Open Access
XIAO Hui, TIAN Xinqin. Modeling of Auroral Electrojet Index with Ultraviolet Aurora Image (in Chinese). Chinese Journal of Space Science, 2023, 43(3): 434-445 doi: 10.11728/cjss2023.03.2022-0033
Citation: XIAO Hui, TIAN Xinqin. Modeling of Auroral Electrojet Index with Ultraviolet Aurora Image (in Chinese). Chinese Journal of Space Science, 2023, 43(3): 434-445 doi: 10.11728/cjss2023.03.2022-0033
shu

Modeling of Auroral Electrojet Index with Ultraviolet Aurora Image

doi: 10.11728/cjss2023.03.2022-0033 cstr: 32142.14.cjss2023.03.2022-0033
  • 1.

    College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016

  • 2.

    School of Environmental School, Nanjing Xiaozhuang University, Nanjing 211171

  • Received Date: 2022-07-15
  • Accepted Date: 2023-01-04
  • Rev Recd Date: 2023-01-09
    • Available Online: 2023-01-12
    Abstract
  • The auroral electrojet index AE is an important indicator to describe the intensity of geomagnetic substorms, and is closely related to the polar magnetosphere disturbance and the precipitation process of auroral particles. Therefore, it is of great significance to establish an accurate prediction model of the electrojet index for the study of space weather. In this paper, the correlation of the spatial distribution of aurora power IAP and AE index in different seasons are studied by using the ultraviolet aurora image data of Polar satellite in 1997, and on this basis, a prediction model of AE index based on the ultraviolet aurora image is proposed. The grid method is used to extract the spatial distribution characteristics of the aurora intensity of the ultraviolet aurora image. The generalized regression neural network GRNN is used to construct two AE index models, Cor-GRNN model and Var-GRNN model, by using the correlation coefficient method and variance selection method, and training is conducted for the three seasons. The results show that AE and IAP have a similar semi-annual change trend, and their correlation varies greatly in different seasons. Compared with the AE index neural network prediction model driven by the solar wind, the model based on aurora images is superior to other models in terms of ERMS and R2 standards. The normalized ERMS is less than 0.1, and the model’s interpretability for AE index changes is increased by about 10%.

     

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