Search

Solar active regions (ARs) provide the required magnetic energy and the topology configuration for flares. Apart from conventional static magnetic parameters, the evolution of AR magnetic flux systems should have nonnegligible effects on magnetic energy store and the trigger mechanism of eruptions, which would promote the prediction for the flare using photospheric observations conveniently. Here we investigate 322 large (M- and X-class) flares from 2010 to 2019, almost the whole solar cycle 24. The flare occurrence rate is obviously higher in the developing phase, which should be due to the stronger shearing and complex configurations caused by affluent magnetic emergences. However, the probability of flare eruptions in decaying phases of ARs is obviously higher than that in the developing phase. The confined flares were in nearly equal counts to eruptive flares in developing phases, whereas the eruptive flares were half over confined flares in decaying phases. Yearly looking at flare eruption rates demonstrates the same conclusion. The relationship between sunspot group areas and confined/erupted flares also suggested that the strong field make constraints on the mass ejection, though it can contribute to flare productions. The flare indexes also show a similar trend. It is worth mentioning that all the X-class flares in the decaying phase were erupted, without the strong field constraint. The decaying of magnetic flux systems had facilitation effects on flare eruptions, which may be consequent on the splitting of magnetic flux systems.

Coronal mass ejection (CME) is a powerful solar phenomenon that can lead to severe space weather events. Forecasting whether and when the corresponding interplanetary coronal mass ejection (ICME) will reach the Earth is very important in space weather study and forecast. At present, many different kinds of models use the near-Sun CME observations as model inputs to predict its propagation with similar prediction accuracies for large sample events. Among a series of physics-based models, the best-performing version of the shock propagation model (SPM) for large sample events, i.e., SPM3, had achieved a good forecast effect for the 23rd Solar Cycle events (1997.02–2006.12). To further evaluate SPM3, we collected CME events from 2013 January to 2023 July from the Community Coordinated Modeling Center (CCMC) CME scoreboard as a new data set. SPM3 achieved a total prediction success rate of 57% for these new events with a mean absolute error of 8.93 hr and a rms error of 10.86 hr for the shock's arrival time. Interestingly, SPM3 provided better predictions for the CME/shock events during high solar activity years than low solar activity years. We also analyzed the influence of input parameters on CME propagation and found that the larger the angular width of the CME event, the higher the probability of the corresponding IP shock's reaching the Earth. Source latitude had little effect on the arrival probability of the corresponding shock, while source longitude did. The CMEs originating from around W15° had the largest probability of hitting the Earth.

The formation mechanism of light bridges (LBs) is strongly related to the dynamic evolution of solar active regions (ARs). To study the relationship between LB formation and AR evolution phases, we employ 109 LB samples from 69 ARs in 2014 using observational data from the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory. LBs are well matched with the weak field lanes (WFLs), except those aligned on the polarity inversion line of δ sunspots. For penumbral intrusion (type-A) and umbral-dot emergence (type-C) LBs, the WFLs represent the splitting of magnetic flux systems. The sunspots tend to decay and split into several parts after type-A and type-C LBs are formed. For sunspot/umbra-merging (type-B) LBs, the declining WFLs are caused by collisions of flux systems. The sunspots merged and remained stable after type-B LBs formed. We conclude that type-B LBs are formed by collisions of flux systems, while type-A and type-C LBs are generated by splits. The time differences ( δ T ) between LBs appearing and ARs peaking have an average value of 1.06, −1.60, and 1.82 days for type-A, B, and C LBs, with the standard deviations of 3.27, 2.17, and 1.89, respectively. A positive value of δ T means that the LB appears after the AR peaks, whereas a negative δ T means it appears before the peak. Type-A LBs tend to form in the decaying phase or around the peak time. Type-B LBs are more likely to be formed in the developing phase. Type-C LBs mostly take shape in the decaying phase of ARs.

Since fast head-on coronal mass ejections and their associated shocks represent potential hazards to the space environment of the Earth and even other planets, forecasting the arrival time of the corresponding interplanetary shock is a priority in space weather research and prediction. Based on the radio spectrum observations of the 16-element array of the Daocheng Solar Radio Telescope (DSRT), the flagship instrument of the Meridian Project of China, during its construction, this study determines the initial shock speed of a type II solar radio burst on 2022 April 17 from its drifting speed in the spectrum. Assuming that the shock travels at a steady speed during the piston-driven phase (determined from the X-ray flux of the associated flare) and then propagates through interplanetary space as a blast wave, we estimate the propagation and arrival time of the corresponding shock at the orbit of the Solar Terrestrial Relations Observatory-A (STEREO-A). The prediction shows that the shock will reach STEREO-A at 14:31:57 UT on 2022 April 19. The STEREO-A satellite detected an interplanetary shock at 13:52:12 UT on the same day. The discrepancy between the predicted and observed arrival time of the shock is only 0.66 hr. The purpose of this paper is to establish a general method for predicting the shock’s propagation and arrival time from this example, which will be utilized to predict more events in the future based on the observations of ground-based solar radio spectrometers or telescopes like DSRT.

As an important index of solar activity, the 10.7-cm solar radio flux (F10.7) can indicate changes in the solar EUV radiation, which plays an important role in the relationship between the Sun and the Earth. Therefore, it is valuable to study and forecast F10.7. In this study, the long short-term memory (LSTM) method in machine learning is used to predict the daily value of F10.7. The F10.7 series from 1947 to 2019 are used. Among them, the data during 1947–1995 are adopted as the training dataset, and the data during 1996–2019 (solar cycles 23 and 24) are adopted as the test dataset. The fourfold cross validation method is used to group the training set for multiple validations. We find that the root mean square error (RMSE) of the prediction results is only 6.20~6.35 sfu, and the correlation coefficient (R) is as high as 0.9883~0.9889. The overall prediction accuracy of the LSTM method is equivalent to those of the widely used autoregressive (AR) and backpropagation neural network (BP) models. Especially for 2-day and 3-day forecasts, the LSTM model is slightly better. All this demonstrates the potentiality of the LSTM method in the real-time forecasting of F10.7 in future.