Shangxi Li

Coronal mass ejections (CMEs) are intense solar eruptions that, once reaching Earth, pose catastrophic risks to the near-Earth space environment. Thus, improving CME transit time forecast accuracy to mitigate damage to ground-based facilities is critical for space weather forecasting. In this study, we constructed a 3D convolutional neural network (3D CNN) framework that learns from Solar and Heliospheric Observatory Large Angle and Spectrometric Coronagraph white-light image sequences to predict CME transit times. Our main contributions are threefold. (1) Unlike prior work, we first integrate the temporal dimension and use 3D CNN for joint spatiotemporal modeling of CME sequences to predict transit times. (2) To address the uneven sequence-length variations across events and to optimize data set usage, we propose a method that samples multiple fixed-length subsequences for each event and applies mean pooling to their features, which enhances model performance and generalization. (3) To enhance interpretability, we apply gradient-weighted class activation mapping across all convolutional layers to reveal each layer’s attention focus. The results show that shallow layers focus on the CME body and evolve over time, while deeper layers show stronger responses in the background and weaker responses to the CME body. On an independent test set, the model achieves a mean absolute error (MAE) of 11.04 hr; in the transit time range with the most samples, MAE is 6.37 hr, comparable to prior studies. These results confirm 3D CNN’s effectiveness in modeling CME spatiotemporal evolution and predicting transit times, offering a promising path for physically interpretable, data-driven space weather forecasting.

The angular width of coronal mass ejections (CMEs) reflects their eruption scale, dynamics, and potential geoeffectiveness. To deepen our understanding of the hemispheric distribution characteristics of CME activities, we systematically investigate the north–south asymmetry of CMEs with different angular widths. Based on the white-light CME catalog from the Coordinated Data Analysis Workshop during solar cycles 23–25, our main results are as follows: (1) the temporal evolution of CME activity across solar cycles is primarily governed by normal and partial-halo CMEs, while narrow CMEs contribute relatively little to the overall variation; (2) CME activity in cycles 23 and 25 exhibits similar hemispheric patterns, with both cycles showing southern hemisphere dominance, in contrast to the northern dominance observed in cycle 24 (this difference may be attributed to the weaker heliospheric pressure during cycle 24); (3) the phase lag between CME activities in the two hemispheres is generally within five months. These findings reveal the solar activity cycle evolution of CME angular width distributions and hemispheric asymmetries, providing new physical constraints for understanding interhemispheric coupling in solar dynamo processes and improving space weather forecasting models.

Solar active regions are believed to provide significant information on the mutual conversion of the poloidal and toroidal components of the global magnetic field. However, the multiscale periodic variations, in particular the quasi-biennial oscillations (QBOs), of solar active regions are not fully understood. In the present study, the flux, area, and number of solar active regions, as well as the sunspot number data in the period from 1996 May to 2023 November, are studied in detail. The multiscale periodic components in the above four data sets are investigated by the techniques of ensemble empirical mode decomposition and cross-correlation analysis. The main results are as follows. (1) The four data sets exhibit similar periodic components, including the 11 yr Schwabe cycle, the QBOs, and a Rieger-type period. (2) The multiscale periodicity of solar active regions shows different physical characteristics. Under different periodic scales, the highest correlation is between active region flux and area, indicating that active region flux and area better reflect the evolution of active regions. (3) By superimposing the QBOs on the 11 yr Schwabe cycle, the Gnevyshev gap phenomenon was clearly observed, implying that the Gnevyshev gap may be caused by the modulation of the 11 yr Schwabe cycle. (4) The active region flux in both hemispheres shows similar periodic components to the full disk, but the periodic variations are uneven between the northern and southern hemispheres. The results of our analysis could be beneficial for the understanding of the spatiotemporal distribution of solar active regions, and could also provide statistical constraints on solar dynamo theories.