Why the study?
Aurora activities undergo rapid changes in spatial and intensity distribution during a substorm, especially during the expansion phase.
A ConvLSTM-based model can predict the evolution of aurora intensities and poleward boundary movement during substorm expansion phases with reasonable accuracy.
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May support substorm forecasting tools; leaves open operational validation before routine use.
Aurora is an important manifestation of solar‐terrestrial physical processes. The aurora activities have rapid changes in spatial and intensity distribution during a substorm, especially the expansion phase. In this paper, a newly developed aurora evolution model is built based on the Convolutional Long Short‐Term Memory network, using the aurora images captured by the ultraviolet imager on the Polar satellite during the substorm expansion phases. Given the images after the onset, the model can predict the evolution of aurora with reasonable accuracy. The structure similarity, Peak Signal‐to‐Noise Ratio (Peak Signal to Noise Ratio), and Root Mean Square Error are used to evaluate the similarity between the predicted and observed images. The results demonstrate that the model performs well at different scales of evolution. Additionally, the model can estimate the evolution of both the aurora intensities and the movement of the poleward boundary. Furthermore, three cases are displayed to illustrate the behavior of the model and its limitation.
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Jiang et al. (2023) studied this question.
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