We use the SYM-H index to indicate the ring current index. We find that there were two periods during which the SYM-H index decreased quickly during the main phase of the geomagnetic storm on 21–22 October 1999. The first period from 11:44 p.m. UT on 21 October 1999 to 1:35 a.m. UT on 22 October 1999 is defined as step 1. Another period from 3:36 a.m. UT to 5:49 a.m. UT on 22 October 1999 is defined as step 3. The durations of step 1 and step 3 are defined as $\Delta t_1$ and $\Delta t_3$, respectively. The variation of the pressure-corrected SYM-H index during step 1 and step 3 are defined as $\Delta SYM{H}_{ob1}^{\ast }$ and $\Delta SYM{H}_{ob3}^{\ast }$, respectively. The interplanetary (IP) sources responsible for $\Delta SYM{H}_{ob1}^{\ast }$ and $\Delta SYM{H}_{ob3}^{\ast }$ are determined as the solar wind during period 1 and period 3, respectively. We find that the largest southward component of the interplanetary magnetic field ($B_{smax}$) during period 3 was larger than that during period 1, and the largest solar wind dawn-to-dusk electric field ($E_{ymax}$) during period 3 was also larger than that during period 1. We also find that the time integral of $E_y$ during period 3 was much larger than that during period 1. However, we find that $|\Delta SYM{H}_{ob1}^{\ast }|$ was larger than $|\Delta SYM{H}_{ob3}^{\ast }|$, and $|\Delta SYM{H}_{ob1}^{\ast }/\Delta t1|$ was larger than $|\Delta SYM{H}_{ob3}^{\ast }/\Delta t3|$, indicating that the geomagnetic activity intensity during a period does not depend on $B_{smax}$ or $E_{ymax}$, nor does it depend on the time integral of $E_y$. What is the reason for this? We find that the solar wind dynamic pressure during period 1 was larger than that during period 3, indicating that the geomagnetic storm intensity during a period not only depends on the solar wind speed and $B_s$, but it also depends on the solar wind dynamic pressure. The magnetosphere took 4 min to respond to the IP shock. When the z-component of the interplanetary magnetic field (IMF) turned from northward to southward, the response time of the SYM-H index to the southward component of the IMF was 21 min.

With the rapid advancement of Internet of Things (IoT) technology, the volume of sensor data collection has increased significantly. These data are typically presented in the form of time series, gradually becoming a crucial component of big data. Traditional time series analysis methods struggle with complex patterns and long-term dependencies, whereas deep learning technologies offer new solutions. This study introduces the U-TSS, a U-net-based sequence-to-sequence fully convolutional network, specifically designed for one-dimensional time series segmentation tasks. U-TSS maps input sequences of arbitrary length to corresponding sequences of class labels across different temporal scales. This is achieved by implicitly classifying each individual time point in the input time series and then aggregating these classifications over varying intervals to form the final prediction. This enables precise segmentation at each time step, ensuring both global sequence awareness and accurate classification of complex time series data. We applied U-TSS to geomagnetic field observation data for the detection of high-voltage direct current (HVDC) interference events. In experiments, U-TSS achieved superior performance in detecting HVDC interference events, with accuracies of 99.42%, 94.61%, and 95.54% on the training, validation, and test sets, respectively, outperforming state-of-the-art models in accuracy, precision, recall, F1-score, and AUC. Our code can be accessed openly in the GitHub repository at https://github.com/wangmengyu1/U-TSS.

We examined the heliolatitude distribution and North-South (N-S) asymmetry of Ground Level Enhancement (GLE) event source locations from 1942 to 2024, finding distinct patterns between the periods 1942–1979 and 1980–2024. Between 1942 and 1979, 33 GLE events were recorded: 5 from the southern hemisphere within [S3, S11] and 28 from the northern hemisphere within [N7, N37]. The southeast quadrant was devoid of any source locations for GLE events. 45.4% of the source locations of the GLE events were within the latitudinal range of [S15, N15]. The remaining source locations of the GLE events were distributed at latitudes above 15° in the northern hemisphere. Between 1980 and 2024, 43 GLE events were recorded: 25 from the southern hemisphere and 18 from the northern hemisphere, with all events above 30° latitude originating from the northern hemisphere. Approximately 44.2% of the source locations of the GLE events were distributed within the latitudinal band [S15, N15]. Over the period from 1942 to 2024, 44.7% within [S15, N15], 63.2% within [S20, N20], 80.3% within [S25, N25], 88.2% within [S30, N30], and 11.8% at latitudes above 30°. N-S asymmetry was significant at latitudes above 0°, 5°, 10°, 15°, 20° and 30°, with northern hemisphere dominance. Moreover, a strong inverse correlation exists between the number of GLE events from the northern and southern hemispheres at latitudes above 0°, 5°, 10°, 15°, 20°, 25° and 30°.

We studied the latitudinal and solar cycle distribution of extreme (≥X5) solar flares spanning 1976–2018. We found that all such flares were confined within the latitudinal range of [S30, N35]. Nonetheless, the majority of these flares during different solar cycles were confined in different latitudinal scopes. Statistical results showed that the southeast quadrant experienced the highest activity of extreme flares. 47.5% of the extreme flares occurred within the latitudes ≤15° of the two hemispheres, with 26.2%, 31.1%, and 42.6% in the latitudinal bands [5°, 10°], >20° and [11°, 20°] of both hemispheres, respectively. Significant N–S asymmetries were observed in the ascending phase of SC 21, the descending phase of SC 23, and both phases of SC 24. Other phases showed asymmetries primarily in latitudinal distribution. The proportion of extreme flares in the ascending phases of SCs 21–24 was 22.2%, 33.3%, 38.9%, and 50%, respectively. Stronger flares (≥X10) were more likely to occur in the descending phase, with 39% of X5–X9 flares and 20% of (≥X10) flares occurring in the ascending phase. On average, 83.6% of extreme flares occurred within a period extending from two years prior to three years following the solar peak, according to our statistical analysis, with specific percentages for each cycle being 88.9%, 100%, 61.1%, and 75%.