香京julia种子在线播放

In this work, we use the IP shock list provided by Oliveira (2023b). The list currently contains 603 events from January 1995 to May 2023. However, due to GIC data availability (see below), only 332 events from the list can be used in this study. The available events occurred from January 1999 to May 2023. Wind and ACE (Advanced Composition Explorer) solar wind plasma (particle number density, velocity, and temperature), and IMF data are used for shock detection and property computations. Solar wind data is explained by Ogilvie et al. (1995) for Wind, and by McComas et al. (1998) for ACE, whereas IMF data is detailed in Lepping et al. (1995) for Wind, and in Smith et al. (1998) for ACE. Before the computation of shock properties, the data was processed and interpolated as described in detail by Oliveira (2023b).

Shock properties including shock impact angles ( θ x n ) and shock speeds are computed with the Rankine-Hugoniot (RH) conditions. These conditions assume that energy and momentum across the shock front, normal magnetic field component, and tangential electric field component are conserved (Priest, 1981; Parks, 2004; Piel, 2010). Such effects are computed with RH equations using solar wind velocity data, magnetic field data, and equations that combine both data sets. For example, a shock normal vector can be calculated with the equation (Oliveira, 2017; 2023b): n ⃗ = ± B ⃗ u × V ⃗ d − V ⃗ u × B ⃗ d − B ⃗ u | B ⃗ u × V ⃗ d − V ⃗ u × B ⃗ d − B ⃗ u | . (1)

In Equation 1, B ⃗ is the magnetic field vector, and V ⃗ is the solar wind velocity vector. The indices u and d indicate observations in the upstream (non-shocked) and downstream (shocked) environments, respectively. Then, from a three-dimensional shock vector calculated with Equation 1 and given by n ⃗ = ( n x , n y , n z ) , θ x n can be computed as θ x n = cos − 1 n x . (2)

We choose the (−) sign of n ⃗ for θ x n defined in Geocentric Solar Ecliptic (GSE) coordinates because shock normals point toward the Earth in this case (Schwartz, 1998). Since the shocks are defined in GSE coordinates, a shock with θ x n = 180° indicates a purely frontal shock, whereas a shock with 90 ° < θ x n < 180° indicates an inclined shock, with the shock being more inclined as θ x n decreases. Figure 1 of Oliveira (2023a) shows pictorial representations of a purely frontal shock and a highly inclined shock. Additionally, animations showing simulations of two shocks with different inclinations using real data can be found here: https://dennyoliveira.weebly.com/phd.html.

According to the RH conditions, shock speeds of shocks with different inclinations can be calculated according to the expression (Oliveira, 2017; 2023b): v s = N d V ⃗ d − N u V ⃗ u ⋅ n ⃗ N d − N u , (3)

where N is the solar wind particle number density. As a result, the magnetosonic Mach number M _s is computed as M _s = u r / v m s f , where u r = v s − | V ⃗ u | , and v m s f is the fast magnetosonic speed. A solar wind structure is classified as a true IP shock if M _s > 1. See section 2.3 of Oliveira (2023b) for more details.

Geomagnetic activity is represented by SuperMAG ground geomagnetic indices. SuperMAG data comprises of an array with hundreds of stations located worldwide for the computation of several geomagnetic indices to capture effects of magnetospheric and ionospheric currents at different latitudes (Gjerloev, 2009). Ring current effects are accounted for by the SuperMAG ring current SMR index (Newell and Gjerloev, 2012), whereas auroral electrojet effects are represented by the SuperMAG total and regional SMU (upper envelope) and SML (lower envelope) indices (Newell and Gjerloev, 2012). As detailed by Newell and Gjerloev (2012), the SMR index is similar to the SYM-H index (Iyemori, 1990), but more low- and mid-latitude stations are used to compute the SMR index. Similar explanations are provided by Newell and Gjerloev (2011), who detail how the SMU and SML indices are calculated with more high-latitude stations in comparison to the traditional AU and AL indices (Davis and Sugiura, 1966). All SuperMAG data used in this study are 1-min resolution data.

Local ground-based magnetic field response is represented by IMAGE (International Monitor for Auroral Geomagnetic Effects) data (Viljanen and Häkkinen, 1997). IMAGE provides high-resolution data in northern Europe and eastern Greenland for studies of large-scale field-aligned current structures and dynamics of the high-latitude auroral electrojets (Tanskanen, 2009). IMAGE data resolutions are usually 10 s. In this study, we use data recorded at a single station, namely, the Nurmijärvi (NUR) station, located in southern Finland at geographic coordinates 60.50° latitude and 24.65° longitude. The magnetic field components of the NUR data are represented in the north-ward direction (x component), eastward direction (y component), and downward direction (z component).

GIC data recordings come from locations near the Mäntsälä pipeline compression station in southern Finland (latitude 25.20°, longitude 60.60°) maintained by the Finnish Meteorological Institute and the European Community’s Seventh Framework Programme. The Mäntsälä GIC data is obtained by two magnetometers, one located at Mäntsälä, and the other at NUR (Pulkkinen et al., 2001b; Viljanen et al., 2010). The ground dB/dt variations at and auroral dynamics above NUR account for natural variations of the geomagnetic field. The NUR field values are then subtracted from the Mäntsälä field values, and the difference is interpreted as field variations due to GIC effects (Pulkkinen et al., 2001b; Viljanen et al., 2010). Finally, by knowing the electromagnetic and geometric properties of the pipeline, along with the geoelectric field modeled by a framework shown in Pulkkinen et al. (2001a), the actual measurements of GICs are determined. GICs measured at Mäntsälä have an error of up to 1 A, which are smaller than the GIC peaks we intend to investigate in this work (GIC > 5 A). Such GIC levels can cause overtime damage (Béland and Small, 2005; Gaunt and Coetzee, 2007; Rodger et al., 2017) and power disruptions in electric power transmission systems (Allen et al., 1989; Bolduc, 2002; Oliveira and Ngwira, 2017). The Mäntsälä GIC data and the NUR geomagnetic field data have both resolutions of 10 s. As recommended by Viljanen et al. (2010), daily GIC average values were substrcted from the GIC data shown in this paper, even though these average values are very close to zero.

Panel A of Figure 1 shows the approximate location near the Mäntsälä compression station in southern Finland. The thick green lines represent magnetic latitudes computed with the Altitude-Adjusted Corrected GeoMagnetic (AACGM) model (Baker and Wing, 1989; Shepherd, 2014) for the year 2015. This figure shows that Mäntsälä can certainly be underneath the auroral oval during intense geomagnetic storms and substorms, where it can reach magnetic latitudes as low as 50° (e.g., Boteler, 2019; Hayakawa et al., 2020a). Panel B in the same figure shows a snapshot of southern Finland with the geographic locations of Mäntsälä (red star) and NUR (blue star). NUR is located approximately 40 km southwest of Mäntsälä, which is way within the separation of 600 km between ground stations for adequate GIC modeling (Ngwira et al., 2008). The time evolution of Mäntsälä’s magnetic latitude in the time span of this study (1999–2023) is shown in Figure 2. The variation of the magnetic latitudes was near 0.5° in the period, which is neglegible for this study. Therefore, effects caused by different MLATs at Mäntsälä can safely be ignored in this study.

Figure 3 shows a comparison between the two main data sets used in this study, namely, the shock and GIC data sets. Panel A shows annual number distributions of IP shocks in the Oliveira (2023b) data base (salmon bars), and Carrington-averaged ( ∼ 27 days) sunspot numbers from January 1995 to May 2023 (solid black line) (Clette et al., 2015). As discussed by Oliveira (2023b), both numbers of shocks and sunspots correlate quite well, meaning that shocks are more likely to occur during solar maxima (Oh et al., 2002; Kilpua et al., 2015; Oliveira and Raeder, 2015). Panel B of Figure 3 shows observation numbers of GIC observations plotted and color coded in 1 year × 1-h MLT (AACGM magnetic local time) bins. The plot shows that there are no GIC measurements recorded at the Mäntsälä pipeline before 1999. On the other hand, the number of observations in all MLT bins for a particular year are nearly the same, which indicates that eventual lack of observations may cover a few days. In addition, most years provide on average 8–10× 10⁴ data points (1 data point ≡ 10 s). Although there are good coverages for the maximum and declining phases of solar cycle 23 (SC23), there is good coverage for the ascending phase of SC24, but lower coverages during maximum and initial declining phases of SC24 (years 2015 and 2016). Finally, GIC data has good coverage in the ascending phase of the current SC25. Therefore, for this study, there’s coverage of approximately two entire solar cycles with shocks and GIC concomitant data.

In this subsection, we compare GIC effects caused by the impacts of two shocks with different inclinations, but with similar strengths as represented by magnetosonic Mach numbers. We choose a nearly frontal shock, hereafter NFS1, with θ x n = 161.77° and M _s = 2.3, that struck the magnetosphere at 1400 UT on 18 April 2023. We also select a highly inclined shock, hereafter HFS1, with θ x n = 128.92° and M _s = 2.6, that hit the magnetosphere at 1708 UT on 11 November 2004. This approach has been shown to be very effective for comparisons of geomagnetic activity triggered by shocks with different inclinations (Wang et al., 2006; Selvakumaran et al., 2017; Oliveira et al., 2018; Shi et al., 2019; Xu et al., 2020). As will become clearer later, these shocks were also chosen because their impacts on the magnetosphere occurred when Mäntsälä was around dusk (MLT ∼16 h and MLT ∼ 19 hr, respectively). In both plots, the black dashed vertical lines indicate the corresponding UT of shock impact of the magnetosphere. NFS1 was observed by Wind, whereas HIS1 was observed by ACE. Table 1 summarizes some general properties of the shocks used for comparisons of compression effects.

Figure 4 and Figure 5 show time series for solar wind, IMF, geomagnetic index, ground magnetic field, and GIC data for the NFS1 and HFS1, respectively. Both figures show the three components of the IMF (panels A), IMF magnitude (panel B), three components of the solar wind velocity (x, panel C; y and z, panel D), plasma number density (panel E), dynamic pressure m _p NV ², where m _p is the proton mass (panel F), temperature (panel G), SMR (panel H), SMU (panel I), regional SMU plotted as a function of time and MLT (panel J), ground dB/dt at NUR (panel K); and GIC at Mäntsälä (panel L). In order to compare geomagnetic effects caused by IP shock compression, we plot data ±1 h around shock impact time (dashed black vertical line) and focus on the following 20 min. This time has been shown to be adequate to focus only on shock compression effects (Selvakumaran et al., 2017; Oliveira et al., 2018; Rudd et al., 2019).

Both figures show that IMF conditions are very similar before shock impacts (B _x slightly negative, with B _y and B _z near zero values). After shock impacts, the IMF magnitudes increase from values near 5 nT to values around 13 nT. In both time series, V _x shows a sharper decrease, with V _z showing a much more intense variation in the HIS1 case with respect to the NFS1 case. Although the shock compression rates (downstream to upstream plasma number density ratio) are similar (2.6 and 2.2, respectively), the dynamic pressure compression ratio is higher in the case of the NFS1 (see Table 1). These observations indicate that the HIS1 is indeed more inclined and stronger than NFS1. As theoretically demonstrated by Samsonov (2011), nearly frontal shocks compress the magnetosphere mostly in the x direction, whereas highly inclined shocks compress the magnetosphere more significantly in the y and z directions in comparison to y and z directions in nearly frontal shocks. As demonstrated by many works (see, e.g., Oliveira, 2023a), these asymmetric compression effects caused by highly inclined shocks usually lead to very different geomagnetic activity in terms of asymmetries and intensities in comparison to nearly frontal shocks.

The shock impact angle effects caused by the two shocks can be clearly seen in the remaining panels of Figure 4 and Figure 5. Panels H and I show that SMR and SMU are more intense and develop faster in the NFS1 case in comparison to the HIS1 case. These effects have been shown in many works, including simulations and observations (Takeuchi et al., 2002; Guo et al., 2005; Wang et al., 2006; Rudd et al., 2019; Shi et al., 2019; Oliveira et al., 2021). The regional SMU index shows strong enhancements around MLT = 6 h, but a relative SMU change is higher in the NFS1 case. Ground–dB/dt variations at NUR are more intense (magnitude near 2.5 nT/s) and peak earlier in the first case in comparison to the second case. These effects have already been reported to be observed in ground magnetometer data in geospace (Oliveira et al., 2020) and on the ground (Takeuchi et al., 2002; Oliveira et al., 2020; 2021). As expected, similar trends are observed in the GIC observations shown by the red lines in both plots, which are supported by the papers mentioned above and many others (Oliveira and Samsonov, 2018; Oliveira, 2023a). Finally, our NUR–dB/dt and Mäntsälä GIC observations agree with a trend reported by Viljanen et al. (2010), who pointed out that GIC measurements almost always follow–dB/dt measurements (which are plotted in the figures) in the x direction because the Mäntsälä pipeline is positive-oriented in the eastward direction from Mäntsälä.

Now we focus on GIC enhancements caused by substorm effects triggered by shocks. We select two shocks, a nearly frontal shock (NFS2), with θ x n = 162.62° and M _s = 2.4, and a highly inclined shock (HFS2), with θ x n = 130.78° and M _s = 2.4. The impact of the NFS2 on the magnetosphere occurred at 2300 UT of 07 September 2017, whereas the impact of the HFS2 took place at 1833 UT of 15 February 2010. Table 2 summarizes the main properties of NFS2 and HIS2. These shocks were selected because their impacts occurred at MLT = 0.9 h and MLT = 20.6 h at Mäntsälä for NFS2 and HIS2, which allows for the observation of magnetotail effects on GICs around the magnetic midnight terminator (MLT = 00 h). Solar wind, IMF, geomagnetic index, ground magnetic field, and GIC data are plotted for the NFS2 and HFS2 in Figure 6 and Figure 7. These figures are similar to Figure 4 and Figure 5, except for two differences: first, the SMU index time series and regional SMU index are replaced by SML, and data is plotted 1 and 2 h around the shock impact onset. The time span after the shock impacts are adequate to account for substorm activity triggered by solar wind driving including shocks (Bargatze et al., 1985; Freeman and Morley, 2004; Oliveira and Raeder, 2014; Oliveira et al., 2024).

In both shock cases, IMF B _z values in the upstream region were close to −5 nT. Preconditioning effects are very important conditions for substorm triggering (Zhou and Tsurutani, 2001; Yue et al., 2010), and they determine whether a substorm is triggered or not. On the other hand, it is very clear that IMF B _z is much more depleted in the NSF2 case in comparison to the HFS2 case because the nearly head-on impact amplifies the southward condition of IMF B _z in comparison to the other case. These different magnetospheric compression conditions were shown with simulation by Oliveira and Raeder (2014) to be very effective in determining the intensity of substorm triggering, being much more intense in the frontal case in comparison to the inclined case. This is clearly seen in Table 2, with the downstream to upstream ram pressure ratio being higher in the NFS2 case in comparison to the HIS2 case. SMR amplitudes are more intense and occur earlier in the nearly frontal case in comparison to the highly inclined case (Guo et al., 2005; Selvakumaran et al., 2017; Rudd et al., 2019). Time series for the SML index (magenta lines) indicate much more intense magnetotail activity in the first case (SML < −3,000 nT) in comparison to the second case (SML around −600 nT). Similar effects were shown with SuperMAG data by Oliveira and Raeder (2015). The regional SuperMAG index data show very intense SML activity (SML < −1,500 nT) around the magnetic midnight for the NFS2, whereas mild SML activity (SML ∼ −500 nT) is seen near dawn for the HIS2. These results agree with the simulations conducted by Oliveira and Raeder (2014) for shocks with different orientations.

As depicted in Figure 6 and Figure 7, after shock impacts, some NUR–dB/dt variations and Mäntsälä GIC variations are observed at their respective locations in the NFS2 case, but close to none observations are seen after shock impact in the HIS2 case (note that panels K and L in the figures are not to scale). Later, intense ground–dB/dt and GIC variations are seen around 90 min after shock impacts in the NFS2 case, whereas no noticeable observations are recorded at NUR and Mäntsälä. These observations strongly agree with the results shown by Oliveira et al. (2021): ground–dB/dt variations are more intense during substorms triggered by nearly head-on shock impacts on the magnetosphere. These results are also supported by the statistical and superposed epoch analysis study reported by Oliveira et al. (2024).

A superposed epoch analysis of GIC peaks for all shocks is shown in Figure 8. The top panels show counts or number of data points or observations (1 data point ≡ 10 s) of GIC peaks caused by shock compressions (panel A) and substorm effects (panel B). The lower panels show the GIC peaks associated with shock compression effects (panel C) and substorm effects (panel D). In all panels, data are plotted as a function of MLT and θ x n , with the color codes representing number of observations (panels A and B) and GIC peaks (panels C and D).

In order to explore shock impact angle effects on the subsequent GIC peak response, we classify events as highly inclined shocks (HIS, with θ x n < 140°); moderately inclined shocks (MIS, 140 ° ≤ θ x n < 160°); and nearly frontal shocks (NFS, θ x n ≥ 160°). Such shock impact angle classifications have been shown to be very effective in capturing impact angle effects on the following geomagnetic activity caused by shocks (e.g., Wang et al., 2006; Oliveira and Raeder, 2015; Selvakumaran et al., 2017; Oliveira et al., 2018; Rudd et al., 2019; Shi et al., 2019; Xu et al., 2020; Oliveira et al., 2024).

In the shock compression case, panels show that observation counts indicate that most bins show observation numbers less than 200, but a few bins show observation numbers greater than 300. In the other case, most bins indicate observation numbers greater than 600, and fewer bins indicate observation numbers greater than 800. Therefore, although the numbers of observations are spread out in the bins, there are no particular biases introduced by either MLT or θ x n in the observations. The overall number of observations is higher in the substorm effects case in comparison to the shock compression case due to the time span of observations around shock onset (within 20 min and within 20 and 120 min for both cases, respectively).

GIC peaks ( > 5 A) in the shock compression case (Figure 8C) occur more often for NFSs. There are very few peaks (1–2 A) for the HIS case, but they are apparently more concentrated around MLT = 12 h. MISs show peak intensities in between the HIS and NFS categories, with MLT coverage in between. Although these observations agree with previous works (Oliveira et al., 2018; Xu et al., 2020), our results are novel in two ways: first, this is the first time IP shock impact angle effects are observed on actual GICs, and, second, there is broader and more intense GIC peak response around the dusk sector (MLT ∼ 18 h), which is much more evident for NFSs.

In Figure 8D, GIC peaks are shown in the same way as in panel C, but for the case accounting for substorm effects. However, since GIC peaks in this case are more intense, the panel highlights GIC peaks greater than 10 A. Most of these peaks occur around MLT = 00 h for NFSs, but a few peaks occur for MISs with 20 h < MLT < 22 h. These results agree withVsubstorm effects triggered by nearly head-on shock impacts on the subsequent ground dB/dt variations in the case study reported by Oliveira et al. (2021), and the statistical study provided by Oliveira et al. (2024).

Figure 9 shows the same data represented in panels C and D of Figure 8, but organized in pie diagrams with the relative occurrence number of events with GIC peaks greater than 5 A (panels A and B), and GIC peaks greater than 10 A (panel C) and greater than 20 A (panel D). Shock inclination categories are represented in blue, NFS; magenta, MIS; and green, HIS. The first column is for events caused by magnetospheric compression by the shocks (within 20 min after shock onset), whereas the second column is for magnetotail or substorm effects (between 20 min and 120 min after shock onset).

Results show that NFSs dominate GIC peaks for both GIC thresholds and space weather drivers (magnetospheric compression and substorm effects). For GIC peaks ≥5 A, compression effects, NFSs account for more than three-quarters of the events, with ∼16% of events classified as MISs, and ∼6% classified as HISs. As shown in Table 3, there is only one event classified as HIS. Still for compression effects, nearly three-quarters of the events are NFS, whereas nearly one-quarter of the events are MIS. There are no HIS events. Therefore, these results clearly show that shock impact angles significantly control the subsequent GIC peaks at Mäntsälä, particularly for GIC peaks greater than 20 A occurring during substorm events (panel D). Tables 3 (shock compression effects) and 4 (substorm effects) summarize the shock properties, geomagnetic index, and GIC peak responses to all events investigated in this study with GIC peaks grater than 5 A.