Characterization of Geomagnetic Storms Using Superposed Epoch and Correlation Analyses: A Study of ICME and Magnetic Cloud Events (1998–2018) for Space Weather Forecasting

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Mustajab This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6586520/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study analyzes geomagnetic disturbances from 1998 to 2018, focusing on the role of Interplanetary Coronal Mass Ejections (ICMEs) and Magnetic Clouds (MCs), particularly their impact when accompanied by shocks. Events are classified into four intensity levels—Quiet, Weak, Moderate, and Intense—and trends are studied across Solar Cycles 23 and 24. Using superposed analysis (via Fortran programming) and correlation analysis (via Origin software), the research examines relationships between storm intensity (Dst index) and plasma/field parameters, including solar wind speed (Vmax), magnetic field strength (Bmax), southward Bz, and convective electric field (Ey). Data is sourced from the OMNIWeb database, and events are categorized using the Richardson and Cane catalog. The study reveals that shocks significantly enhance the geoeffectiveness of ICMEs and MCs, with storms associated with these shocks showing the strongest geomagnetic responses. By comparing events with and without shocks, this work demonstrates that southward Bz and energy coupling parameters like Vmax × Bzmin are the most reliable predictors of storm strength. The results provide valuable insights into the drivers of geomagnetic activity, emphasizing the role of magnetic field orientation and energy transfer in shaping the intensity of geomagnetic disturbances. This analysis offers important contributions to space weather forecasting, enhancing the understanding of solar-terrestrial interactions and supporting efforts to mitigate the effects of geomagnetic storms on technological systems. These findings support improved understanding and forecasting of geomagnetic storms and their technological impacts. Coronal mass ejections geomagnetic storm solar cycle magnetic cloud solar wind Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction This study investigates the occurrence, intensity, and driving factors of geomagnetic disturbances over the period 1998 to 2018, with particular emphasis on the effects of Interplanetary Coronal Mass Ejections (ICMEs) and Magnetic Clouds (MCs), Richardson, I. G., & Cane, H. V.,(2010, 2011) Gopalswamy, N. ( 2006 )(2010) , Gonzalez, W. D., et al., ( 1994 ), Tsurutani, B. T., & Gonzalez, W. D. (1997)and Zhang, J., et al., (2007) . The disturbances are categorized based on Dst index values into four levels: Quiet, Weak, Moderate, and Intense. Their distribution and frequency are analyzed across Solar Cycles 23 and 24 to identify long-term patterns in geomagnetic activity. A notable finding is that Quiet events dominate the dataset, especially in the earlier years, at the same time Moderate and Intense storms show marked increases during specific active solar years, such as 2012 and 2015. Ahmed, O. M., Ahmad, B. Z., & Derouich, M. (2024). Le, G.-M., Liu, G.-A., & Zhao, M.-X. (2020). The work employs superposed epoch analysis , performed using custom Fortran programming, to investigate the averaged temporal behavior of key space weather parameters during storm events Zhang, J., et al., ( 2004 ) and Huttunen, K. E. J., Koskinen, H. E. J., & Schwenn, R. (2002); Moraes-Santos, D., et al. ( 2024 ). Additionally, correlation analysis is carried out using Origin software to assess the relationships between geomagnetic storm intensity and interplanetary plasma/field parameters. These parameters include solar wind speed (Vmax), total magnetic field strength (Bmax), the southward component of the interplanetary magnetic field (Bz), and the convective electric field (Ey). Wu, C.-C., et al., ( 2006 ) and Echer, E., Gonzalez, W. D., & Tsurutani, B. T. (2008). Sun, X., et al., ( 2024 ). The plasma and geomagnetic data used in this study are obtained from the OMNIWeb database , which provides 1-hour resolution solar wind and geomagnetic indices. Event identification and classification are based on the Richardson and Cane ICME catalog , allowing for systematic analysis of ICME- and MC-associated storms, both with and without shock signatures. The results reveal that shocks play a crucial role in enhancing the geoeffectiveness of ICMEs and MCs, with the strongest geomagnetic responses observed in cases where shocks precede the main structure. Further analysis shows that among various coupling parameters, the southward Bz component and the product Vmax × Bzmin serve as the most consistent predictors of geomagnetic storm strength. Santoso, Aet al., ( 2025 ). Krauss, S., et al., ( 2015 ). Joshi, N. C., et al., ( 2010 ). Overall, the study contributes to a better understanding of the solar wind-magnetosphere interaction processes, highlighting the importance of magnetic field orientation and energy coupling in geomagnetic storm development. These insights are valuable for improving space weather forecasting models and for developing strategies to mitigate the effects of geomagnetic disturbances on technological systems and infrastructure. 2. Methodology 2.1 Data Sources Solar Wind and Geomagnetic Data : Hourly-averaged plasma and field parameters, including solar wind speed (V_max), magnetic field strength (B_max), the southward component of the interplanetary magnetic field (B_z), and the convective electric field (E_y), were obtained from the OMNIWeb database .omniweb.gsfc.nasa.gov Event Categorization : ICME and MC events were identified and categorized using the Richardson and Cane ICME catalog .SIDC 2.2 Analytical Techniques Superposed Epoch Analysis : Implemented using Fortran programming, this technique involves aligning multiple events based on a common reference point (e.g., storm onset) to identify average behaviors and patterns across events. Kamide, Y., & Kokubun, S. (1996) and Huttunen, K. E. J., at al., (2002). Correlation Analysis : Using Origin software, this analysis examines the relationships between geomagnetic storm intensity (e.g., Dst index) and key plasma/field parameters (e.g., V_max, B_max, B_z, E_y) Newell, P. T. et al., (2007), Borovsky, J. E., & Denton, M. H. (2006), Echer, E., Gonzalez, W. D., & Tsurutani, B. T. (2008); Wu, C.-C., et al., (2006). 2.3 Classification of Geomagnetic Disturbances Geomagnetic disturbances were classified based on Dst index values into four categories: Quiet : Dst > -30 nT ResearchGate+15arXiv+15PMC+15 Weak : -50 nT < Dst ≤ -30 nT arXiv Moderate : -100 nT < Dst ≤ -50 nT Intense : Dst ≤ -100 nT arXiv+2arXiv+2MDPI+2 2.4. Temporal Scope The study period spans from 1998 to 2018, covering Solar Cycles 23 and 24, allowing for the examination of solar activity variations and their impact on geomagnetic disturbances. 3. Result and Discussion This study investigates geomagnetic disturbances associated with Interplanetary Coronal Mass Ejections (ICMEs) and Magnetic Clouds (MCs) from 2009 to 2018. Using superposed analysis with Fortran programming and correlation analysis with Origin software, we analyze key plasma/field parameters, including solar wind speed, magnetic field strength, and geomagnetic indices such as Dst and Ap. The analysis reveals a significant variation in geomagnetic disturbance intensity, showing that ICMEs and MCs with shocks produce stronger disturbances, with the highest geomagnetic storm intensity observed during periods of shock interaction. The study highlights the critical role of magnetic field orientation and energy coupling in determining the intensity of geomagnetic storms. Figure 1a shows the annual distribution of four categories of geomagnetic storm events ( Q, W, M, S ) from 2009 to 2018 . Event counts vary year to year, but stronger events (M, S) appear consistently across the decade. Indicates ongoing solar activity and recurring geomagnetic storms with varying intensity. Table 1 Distribution of Geomagnetic Disturbances (2009–2018). Storm 2009 (in %) 2010 (in %) 2011 (in %) 2012 (in %) 2013 (in %) 2014 (in %) 2015 (in %) 2016 (in %) 2017 (in%) 2018 (in%) Total (in %) Q 9 (82%) 9 (60%) 20 (62.5%) 8(+ 2)* (24.2%) 8 (32%) 8 (40%) 7(+ 1)* (21.8%) 7 (54%) 4 (44.4%) 5 (62.5%) 85 (43%) W 1 (9%) 2 (13%) 4 (12.5%) 7 (21.2%) 7 (28%) 4 (20%) 5 (15.6%) 2 (15%) 2 (22.2%) 2 (25%) 36 (18%) M 1 (9%) 4 (27%) 5 (15.6%) 12 (36.4%) 8 (32%) 7 (35%) 13 (40.6%) 3 (23%) 1 (11.1%) 0 54 (27%) S 0 0 3 (9.4%) 6 (18.2%) 2 (8%) 1 (5%) 7 (21.8%) 1 (8%) 2 (22.2%) 1 (12.5%) 23 (12%) total 11 15 32 33 25 20 32 13 9 8 198 *use for additional events those are having positive Dst value Table 1 . shows the distribution of geomagnetic disturbances by intensity ( Quiet, Weak, Moderate, and Intense ) from 2009 to 2018 . It includes the number of events and percentages for each intensity in each year, with a total count at the bottom. On yearly basis Quiet storms were dominant in most years, particularly in the earlier years (2009–2018). Weak disturbances were generally low, peaking slightly in years like 2012 and 2018. Moderate storms showed significant variability, particularly peaking in 2012 and 2015. Intense storms were rare, peaking in 2012 but generally making up a small fraction of events. In short, Quiet storms were consistently the most common category. Weak disturbances remained relatively minor. Moderate storms were present in fluctuating numbers, with higher peaks in specific years like 2012 and 2015. Intense storms were generally rare, showing a clear absence in many years. Table 2 Distribution of Geomagnetic Disturbances (1998–2008, Solar Cycle 23) Storm 1998 (in %) 1999 (in %) 2000 (in %) 2001 (in %) 2002 (in %) 2003 (in %) 2004 (in %) 2005 (in %) 2006 (in %) 2007 (in %) 2008 (in%) Total (in %) Q 10 (27.8%) 13 (39.4%) 13 (25.5%) 11 (23.9%) 4 (15.4%) 4 (18.2%) 2 (10.5%) 5 (17.2%) 5 (45.4%) 1 (50%) 1 (100%) 69 (25.1%) W 8 (22.2%) 8 (24.2%) 12 (23.5%) 8 (17.4%) 7 (26.9%) 4 (18.2%) 6 (31.6%) 6 (20.7%) 1 (9.1%) 0 0 60 (21.7%) M 10 (27.8%) 7 (21.2%) 13 (25.5%) 13 (28.3%) 6 (23.1%) 8 (36.4%) 4 (21%) 13 (44.8%) 4 (36.4%) 1 (50%) 0 79 (28.6%) S 8 (22.2%) 5 (15.2%) 13 (25.5%) 14 (30.4%) 9 (31%) 6 (27.2%) 7 (36.9%) 5 (17.2%) 1 (9.1%) 0 0 68 (24.6%) total 36 33 51 46 26 22 19 29 11 2 1 276 Table 2 . The table shows the distribution of geomagnetic disturbances by intensity— Quiet (Q), Weak (W), Moderate (M), and Intense (S) —over solar cycle 23 (1998–2008) , listing both the number of events and their percentages for each year. In year 2000 had the highest activity (51 disturbances), with a balanced distribution across all categories. Intense storms were most frequent between 1998 and 2004, particularly in 2000 and 2001. Moderate storms remained a significant category, peaking in 2005. Weak storms showed no clear temporal trend, with the lowest frequency in the later years (2006–2008). Quiet storms were more frequent in the early and late years of the solar cycle. In short, Intense storms dominated the middle years of the solar cycle (1998–2004), especially in 2000 and 2001. Moderate storms were consistently present, reaching a peak in 2005. Quiet storms remained a notable category but were less frequent than in the 2009–2018 period. Weak storms were the least frequent, peaking in the earlier years. Figure 1b shows the b reaks down how each storm category (Q, W, M, S) is associated with different solar wind structures (ICMEs and MCs), depending on the presence of shocks and discontinuities . Strong storms (S and M) are most often caused by complex structures , especially those with both shocks and discontinuities. Weak storms (Q and W) tend to be associated with simpler or less energetic structures. Table 3 Geomagnetic Disturbances Associated with ICMEs and MCs (2009–2018) Storms / groups Total no. Of events Quiet Weak Moderate Intense ICMEs without disturbance without shocks 48(+ 2)* 30 (62.5%) 9 (18 .75%) 9 (18.75%) 0 ICMEs with disturbance and without shocks 47 27 (57%) 8 (17%) 12(25.5%) 0 ICMEs with disturbance with shocks 101(+ 1)* 30 (29.7%) 17 (16.8%) 33 (32.6%) 21 (20.8%) MCs without disturbance and without shocks 15 5 (33.3%) 3 (20.0%) 7 (46.7%) 0 MCs with disturbance and without shocks 15 6 (40.0%) 5 (33.3%) 4 (26.7%) 0 MCs with disturbance with shocks 51(+ 1)* 9 (17.6%) 5 (9.8%) 22 (43.2%) 15 (29.4%) *number of events which are existing in the same group but having the + ve Dst value. Not count in the total no of events for that Table 3 provides the distribution of geomagnetic disturbances by intensity (Quiet, Weak, Moderate, Intense) associated with different types of ICMEs (Interplanetary Coronal Mass Ejections) and MCs (Magnetic Clouds) from 2009 to 2018 , based on the presence or absence of disturbances and shocks. Some events have positive Dst values (marked with *), indicating no true geomagnetic storm and thus excluded from total counts. In short, the presence of shocks significantly increases the occurrence of Moderate and Intense storms. MCs with shocks produced the highest levels of Intense and Moderate disturbances, showing that shocks are a key factor in producing geoeffective storms. Events without shocks were mostly associated with Quiet and Weak disturbances. Table 4 Geomagnetic Disturbances During Solar Cycle 23 (1998–2008) with ICMEs and Shocks Storm 1998 (in %) 1999 (in %) 2000 (in %) 2001 (in %) 2002 (in %) 2003 (in %) 2004 (in %) 2005 (in %) 2006 (in %) 2007 (in%) total Q 3 (16.7%) 3 (18.7%) 4 (12.9%) 2 (6.9%) 2 (11.1%) 1 (7.2%) 0 (%) 0 (%) 1 (25%) 0 (%) 16 (10.5%) W 3 (16.7%) 4 (25%) 7 (22.6%) 5 (17.2%) 3 (16.7%) 3 (21.4%) 1 (10%) 3 (25%) 0 (%) 0 (%) 29 (19.1%) M 6 (33.3%) 4 (45%) 7 (22.6%) 10 (34.5%) 4 (22.2%) 5 (35.7%) 2 (20%) 5 (41.7%) 2 (50%) 0 45 (29.6%) I 6 (33.3%) 5 (31.3%) 13 (41.9%) 12 (41.4%) 9 (50%) 5 (35.7%) 7 (70%) 4 (33.3%) 1 (25%) 0 62 (40.8%) total 18 16 31 29 18 14 10 12 4 0 152 Table 4 summarizes the distribution of geomagnetic disturbances by intensity ( Quiet, Weak, Moderate, and Intense ) during solar cycle 23 (1998–2008) , specifically when ICMEs were associated with shocks . The data includes the number of events and their corresponding percentages for each year, along with a total count for each intensity. Found o bservations; Intense storms were most prevalent when ICMEs were associated with shocks. Moderate storms showed a steady presence, peaking in the mid-2000s. Weak and Quiet storms were much less frequent in this cycle compared to later years. On the bases of Figs. 1a and 1b over the decade, geomagnetic storms occur regularly, with strong storms (M and S) not uncommon. These stronger storms are most likely triggered by ICMEs and MCs that contain both a shock and a magnetic discontinuity, highlighting the importance of event complexity in storm severity. Table 5 De tailed statistical summary of plasma/field parameters and geomagnetic indices derived from a superposed epoch analysis of different interplanetary structures from 2009 to 2018 from Figs. 2, 3, 4, and 5. It includes Dst, Ap, solar wind speed (V), magnetic field strength (B), its standard deviation (σB), and related parameters (Bz, σBz, Ey, BV) , along with the mean amplitudes (Am) and standard errors (± Sm) for each parameter. Parameters/Groups No of events Time(tm) Dst/Ap Dst Ap V B sigmaB (hour) Am \(\:\pm\:\) Sm Am \(\:\pm\:\) Sm Am \(\:\pm\:\) Sm Am \(\:\pm\:\) Sm Am \(\:\pm\:\) Sm ICME with shocks/no shocks 198 10/01 -27.298 2.456 24.207 2.440 450.439 7.083 9.623 0.388 0.819 0.060 ICME with shock 101 08/01 -31.662 3.061 29.486 3.135 467.270 8.694 10.526 0.474 0.919 0.075 ICME without shocks 97 32/26 -22.260 5.203 14.740 3.885 425.960 16.420 7.580 0.535 0.608 0.115 Disturbance with shock/no shock 198 26/02 -27.045 2.390 25.359 2.569 450.677 7.040 9.654 0.408 1.374 0.118 Disturbance with shocks 101 16/02 -36.089 3.721 38.792 4.526 445.172 10.806 11.925 0.652 2.090 0.203 Disturbance no shocks 97 32/26 -19.990 2.700 13.309 2.352 420.907 8.195 7.790 0.361 0.649 0.052 Magnetic cloud with shocks/ no shocks 82 34/03 -34.683 3.290 32.732 4.297 446.805 10.391 12.094 0.615 1.649 0.201 Magnetic cloud with shocks 52 34/03 -40.885 4.447 42.538 6.255 468.731 14.463 13.165 0.970 2.073 0.296 Magnetic cloud with no shocks 30 28/07 -25.067 4.854 21.767 3.891 416.533 15.766 11.333 0.633 0.910 0.113 tm-storm sudden commandment Sm- standard error of mean Continuation of table 5 Parameters/Groups No of events Time(tm) Dst/Ap Bz sigmaBz Ey BV (hour) Am \(\:\pm\:\) Sm Am \(\:\pm\:\) Sm Am \(\:\pm\:\) Sm Am \(\:\pm\:\) Sm ICME with shocks/no shocks 198 10/01 -0.639 0.461 2.344 0.148 0.356 0.220 4.435 0.224 ICME with shock 101 08/01 -0.995 0.575 2.635 0.186 0.535 0.281 5.008 0.277 ICME without shocks 97 32/26 -1.060 0.680 1.702 0.236 0.496 0.311 3.029 0.231 Disturbance with shock/no shock 198 26/02 -0.64 0.337 2.883 0.198 0.261 0.144 4.539 0.236 Disturbance with shocks 101 16/02 -0.973 0.685 4.055 0.329 0.417 0.335 5.902 0.393 Disturbance no shocks 97 32/26 -0.954 0.398 1.662 0.127 0.382 0.188 3.263 0.167 Magnetic cloud with shocks/ no shocks 82 34/03 -1.199 0.663 3.543 0.540 0.478 0.275 5.551 0.381 Magnetic cloud with shocks 52 34/03 -1.597 0.929 4.293 0.533 0.646 0.416 6.276 0.567 Magnetic cloud with no shocks 30 28/07 -2.210 1.103 2.257 0.274 0.957 0.463 4.662 0.310 Table 5 shows the summery of Figs. 2, 3, 4, and 5: Shock Presence Significantly Increases Geoeffectiveness . Found the structures with shocks consistently show higher values of: Dst depressions (more negative) → indicates stronger geomagnetic storms. Ap index → higher overall geomagnetic activity. Magnetic field (B) and its fluctuations (σB) . Electric field (Ey) and BV → indicators of storm-driving energy. Magnetic clouds (MCs) with shocks : -40.89 nT (strongest Dst depression). ICMEs with shocks : -31.66 nT . Events without shocks (ICMEs or MCs): Dst values are significantly weaker (e.g., MCs no shocks: -25.07 nT ; ICMEs no shocks: -22.26 nT ). ICMEs with shocks and MCs with shocks have the strongest fields (~ 468 nT), while events without shocks have weaker fields (~ 416–425 nT). σB and σBz are highest in disturbed/shocked environments , particularly in disturbed MCs with shocks , indicating more turbulent fields. Peak values occur in MCs with shocks found Ey : 6.276 mV/m and BV : 4.293 nT·km/s Shocks dramatically enhance the storm-driving potential of ICMEs and MCs. Magnetic Clouds with shocks are the most geoeffective, with the strongest storms and energy inputs. This analysis supports that interplanetary structures with both magnetic field complexity and shock features are the main drivers of intense geomagnetic activity . In short Shocks dramatically enhance the storm-driving potential of ICMEs and MCs. Magnetic Clouds with shocks are the most geoeffective, with the strongest storms and energy inputs. This analysis supports that interplanetary structures with both magnetic field complexity and shock features are the main drivers of intense geomagnetic activity . These relationships are shown with scatter plots and correlation coefficients. The correlation is stronger in non-shock events , meaning magnetic field direction matters more than shock presence for storm intensity. Table 6 Correlation scattered plot result between Dst and Plasma field parameters for average individual peak values of all events during the period from 2009 to 2018 Groups Total number of events Correlation coefficient with Dst V max B max Bz min Ey dVxBz V max xBz min Total individual events 198 -0.382 -0.474 0.745 -0.689 0.575 0.720 Shocks 101 -0.317 -0.456 0.683 -0.614 0.523 0.646 No Shocks 97 -0.222 -0.324 0.781 -0.736 0.475 0.793 Table 6 presents the correlation coefficients between the Dst index (a measure of geomagnetic storm intensity as shown in firgue 6) and several plasma/field parameters based on average individual peak values for events from 2009 to 2018 . The data is grouped by total events , events with shocks , and events without shocks . Strongest Correlations with Dst: Bz min shows the strongest positive correlation with Dst across all groups, especially without shocks (0.781) . Vmax × Bzmin and Ey also show strong correlations, indicating their importance in storm strength. Shock vs No-Shock Events: No-shock events show higher correlation coefficients than shock events, suggesting that non-shock-driven storms might be more directly influenced by magnetic field orientation and energy coupling. For no-shock events: Ey: Strongest negative correlation ( -0.736 ) – indicates stronger electric fields drive more negative Dst (intense storms). Vmax × Bzmin: Strongest overall coupling indicator ( 0.793 ). Weaker Correlations: Vmax (solar wind speed alone) has the weakest correlation in all cases, suggesting that speed alone is not a reliable predictor of storm strength without considering magnetic field orientation. The southward Bz component and derived coupling parameters like Vmax × Bzmin and Ey are the most reliable predictors of geomagnetic storm intensity (Dst) . Shocks are not the dominant factor in determining the strength of correlation; field orientation and energy transfer are more critical. Here is the heat map showing the correlation between Dst and various plasma/field parameters across different event groups. The strongest positive correlation is with Bz min and Vmax × Bzmin , especially for no-shock events , while Ey also shows strong negative correlations with Dst. 4. Comparison and Conclusion Intense storms : More common in solar cycle 23 (1998-2008) when associated with ICMEs and shocks (40.8%). In contrast, during 2009-2018 , intense storms were rare, typically around 10-12%. Moderate storms : Showed varying patterns across both cycles. In 2009-2018 , moderate disturbances were more frequent than in 1998-2008 , especially in years like 2012 and 2015, while moderate storms were consistently present in solar cycle 23 , peaking in 2005. Quiet storms : Were dominant throughout both periods, particularly in 2009-2018 , with quiet disturbances making up 43% of all events, much higher than the 25% in solar cycle 23 . Weak storms : Were consistently low in both periods, though there was a slight increase in 2012 (21.2%) in 2009-2018 compared to the earlier solar cycle. Key Pattern: The presence of shocks in both cycles was a clear factor driving the increase in Moderate and Intense geomagnetic disturbances. Shocks, especially associated with MCs , produced the most significant and geoeffective storms in both periods, emphasizing the importance of shock-driven disturbances in geomagnetic activity. In conclusion, solar cycle 23 showed a higher frequency of Intense disturbances compared to the 2009-2018 period , where Quiet storms were far more prevalent. The key takeaway is that the presence of shocks greatly enhances the geomagnetic effects, leading to stronger and more frequent disturbances. 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NASA Goddard Space Flight Center. https://omniweb.gsfc.nasa.gov/html/ow_data.html Richardson, I. G., & Cane, H. V. (2010). Near-Earth Interplanetary Coronal Mass Ejections During Solar Cycle 23 (1996–2009): Catalog and Summary of Properties. Solar Physics, 264 ( 1 ), 189–237. https://doi.org/10.1007/s11207-010-9568-6 Richardson, I. G., & Cane, H. V. (2011). Geoeffectiveness (Dst and Kp) of interplanetary coronal mass ejections during 1995–2009 and implications for space weather forecasting. Space Weather, 9(7) . https://doi.org/10.1029/2011SW000670 Santoso, A., Sismanto, S., Priyatikanto, R., Hartantyo, E., & Martiningrum, D. R. (2025). The intensity of geomagnetic storms associated with the interplanetary magnetic field and solar wind parameters during Solar Cycle 24. Earth and Planetary Physics, 9 ( 2 ), 375–386. http://doi.org/10.26464/epp2024069 Sun, X., Zhima, Z., Duan, S., Hu, Y., Lu, C., & Ran, Z. (2024). 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Journal of Geophysical Research: Space Physics, 109 , A09101. https://doi.org/10.1029/2003JA010062 Zhang, J., Richardson, I. G., Webb, D. F., Gopalswamy, N., Huttunen, K. E. J., Kasper,J. C., … Poomvises, W. (2007). Solar and Interplanetary Sources of Major Geomagnetic Storms (Dst ≤ – 100 nT) During 1996–2005. Journal of Geophysical Research: Space Physics, 112(A10) . https://doi.org/10.1029/2007JA012321 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6586520","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":503825842,"identity":"dafd5fe7-7b12-487c-b944-1729a66bfdd7","order_by":0,"name":"F. Mustajab","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYBAC9gYwxcZgAGYZWBDWwnMASBwAaQGzDCSI1gJSnADiE6NF7Izx5w81fPLmks+vbvhRIMHA396dgF+LdI6ZxIFjbIY7Z+eU3ewBOkzizNkNeLXYA7UwHGBjY9xwOyftBg9Qi4FELn4tQFuMPxz4x2a/4eaZtJt/iNRiIHGwjS1xww32Y7eJtCWtTOJsH1vyzp4cttsyBhI8BP3CI528+UPFt2O229mPP7v55o+NHH97L34tUHAMpNsAbAYxykGgBojZHxCrehSMglEwCkYYAACZpUfkU/wIXwAAAABJRU5ErkJggg==","orcid":"","institution":"Jamia Millia Islamia","correspondingAuthor":true,"prefix":"","firstName":"F.","middleName":"","lastName":"Mustajab","suffix":""}],"badges":[],"createdAt":"2025-05-04 03:53:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6586520/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6586520/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90284342,"identity":"5fd327de-0beb-4c49-bc84-438364030d57","added_by":"auto","created_at":"2025-09-01 05:52:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":194882,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6586520/v1/4fc61480f70b241134f6344b.png"},{"id":90284343,"identity":"61d835a1-deb6-4a96-aa9e-0d22daccb2d5","added_by":"auto","created_at":"2025-09-01 05:52:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":155916,"visible":true,"origin":"","legend":"\u003cp\u003eSuperposed epoch profiles of key geomagnetic and interplanetary parameters during 198 geomagnetic disturbance events (Dst-based), 198 ICMEs, and 82 Magnetic Cloud (MC) events recorded between 2008 and 2018. The zero epoch (0 hr) marks the onset of each respective event type. Parameters shown include Dst index, Ap index, solar wind speed (V), total magnetic field strength (B), southward magnetic component (Bz), RMS fluctuations of B and Bz (σB, σBz), convective electric field (Ey), and the solar wind-magnetic field coupling parameter (BV). The plots reveal that MC events exhibit the most pronounced geoeffective features, including deeper Dst depressions, more negative Bz, and elevated Ey and BV, highlighting their stronger geomagnetic impact compared to ICMEs and general disturbance events. Peaks in these parameters following the onset time confirm the critical role of shock arrival and southward Bz orientation in driving intense geomagnetic storms.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6586520/v1/81762d32e3aecee312484525.png"},{"id":90284346,"identity":"7b9ed803-a9a5-4169-9f9a-bc9125031a69","added_by":"auto","created_at":"2025-09-01 05:52:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":131523,"visible":true,"origin":"","legend":"\u003cp\u003eSuperposed epoch analysis of interplanetary and geomagnetic parameters during geomagnetic disturbances associated with ICMEs, categorized by the presence (N = 101) or absence (N = 97) of shocks. Zero hour represents the onset of the disturbance. For all plasma as well as geomagnetic parameters. Events with shocks show stronger and more abrupt responses in all parameters, including deeper Dst depressions, higher Ap values, stronger southward Bz, and greater energy coupling (Ey and BV), confirming their higher geoeffectiveness compared to non-shock ICMEs.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6586520/v1/1fb80c736ceb13ae8ce659f7.png"},{"id":90284340,"identity":"0100e2b5-8e4a-442e-9b79-3a5d364cf092","added_by":"auto","created_at":"2025-09-01 05:52:23","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":140913,"visible":true,"origin":"","legend":"\u003cp\u003eSuperposed epoch analysis of geomagnetic and solar wind parameters during Interplanetary Coronal Mass Ejection (ICME) events with (N = 101) and without (N = 97) shocks, using the ICME onset time as zero hour. Parameters include Dst index, Ap index, solar wind speed (V), magnetic field strength (B), RMS magnetic field (σB), southward magnetic component (Bz), RMS of Bz (σBz), convective electric field (Ey), and the solar wind–magnetosphere coupling parameter (BV). ICME events with shocks show stronger and faster geomagnetic responses, including deeper Dst minima, enhanced Ap activity, higher solar wind speeds and magnetic field strengths, more negative Bz, and elevated Ey and BV values. These results reinforce the key role of shocks in enhancing the geoeffectiveness of ICME structures.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6586520/v1/57c05f259e7d5d5714100136.png"},{"id":90284348,"identity":"4fb8660c-893b-4c02-af55-bad866339f5a","added_by":"auto","created_at":"2025-09-01 05:52:23","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":129655,"visible":true,"origin":"","legend":"\u003cp\u003eSuperposed epoch analysis of geomagnetic and interplanetary parameters during Magnetic Cloud (MC) events with shocks (N = 52) and without shocks (N = 30), using the MC onset time as zero hour. Parameters include Dst index, Ap index, solar wind speed (V), magnetic field strength (B), RMS magnetic field (σB), southward magnetic component (Bz), RMS Bz (σBz), convective electric field (Ey), and coupling parameter (BV). MCs associated with shocks show stronger geoeffectiveness, marked by deeper Dst depressions, higher Ap activity, elevated solar wind speeds and magnetic fields, more southward Bz, and significantly increased energy transfer indicators (Ey and BV). These results underscore the crucial role of shocks in intensifying geomagnetic disturbances driven by MCs.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6586520/v1/56cb2e38dae030231797ec62.png"},{"id":90284350,"identity":"4086b0d7-28c9-4462-9416-28ea2b09cc10","added_by":"auto","created_at":"2025-09-01 05:52:23","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":121028,"visible":true,"origin":"","legend":"\u003cp\u003eshows how the \u003cstrong\u003eDst index\u003c/strong\u003e, which measures geomagnetic storm strength, relates to various solar wind and magnetic field parameters. Across \u003cstrong\u003e198 events\u003c/strong\u003e, stronger storms (lower Dst) are most closely linked to: \u003cstrong\u003eSouthward magnetic field (Bzmin), Electric field (Ey) and Combinations like Vmax.Bzmin. And summarized in table 6\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6586520/v1/fb22e960bd5d1e26616aee03.png"},{"id":90284357,"identity":"da261a74-c20e-4f8a-9b9e-a15adc99d9c6","added_by":"auto","created_at":"2025-09-01 05:52:24","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":58291,"visible":true,"origin":"","legend":"\u003cp\u003eUnnumbered image in the \u003cstrong\u003eResult and Discussion\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"UnumberFigure.png","url":"https://assets-eu.researchsquare.com/files/rs-6586520/v1/2c968e58afea6b5a6f3d7389.png"},{"id":107485536,"identity":"4108801b-0bce-4e43-ae7c-a9a51a3bdc70","added_by":"auto","created_at":"2026-04-22 02:35:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2679935,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6586520/v1/9ad7cf72-b8ff-4286-96d3-f141600c378b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Characterization of Geomagnetic Storms Using Superposed Epoch and Correlation Analyses: A Study of ICME and Magnetic Cloud Events (1998–2018) for Space Weather Forecasting","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThis study investigates the occurrence, intensity, and driving factors of geomagnetic disturbances over the period 1998 to 2018, with particular emphasis on the effects of Interplanetary Coronal Mass Ejections (ICMEs) and Magnetic Clouds (MCs), \u003cb\u003eRichardson, I. G., \u0026amp; Cane, H. V.,(2010, 2011)\u003c/b\u003e Gopalswamy, N. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2006\u003c/span\u003e\u003cb\u003e)(2010)\u003c/b\u003e, Gonzalez, W. D., et al., (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1994\u003c/span\u003e\u003cb\u003e), Tsurutani, B. T., \u0026amp; Gonzalez, W. D. (1997)and Zhang, J., et al., (2007)\u003c/b\u003e. The disturbances are categorized based on Dst index values into four levels: Quiet, Weak, Moderate, and Intense. Their distribution and frequency are analyzed across Solar Cycles 23 and 24 to identify long-term patterns in geomagnetic activity. A notable finding is that Quiet events dominate the dataset, especially in the earlier years, at the same time Moderate and Intense storms show marked increases during specific active solar years, such as 2012 and 2015. Ahmed, O. M., Ahmad, B. Z., \u0026amp; Derouich, M. (2024). Le, G.-M., Liu, G.-A., \u0026amp; Zhao, M.-X. (2020).\u003c/p\u003e\u003cp\u003eThe work employs \u003cb\u003esuperposed epoch analysis\u003c/b\u003e, performed using custom Fortran programming, to investigate the averaged temporal behavior of key space weather parameters during storm events Zhang, J., et al., (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) \u003cb\u003eand Huttunen, K. E. J., Koskinen, H. E. J., \u0026amp; Schwenn, R. (2002);\u003c/b\u003e Moraes-Santos, D., et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, \u003cb\u003ecorrelation analysis\u003c/b\u003e is carried out using Origin software to assess the relationships between geomagnetic storm intensity and interplanetary plasma/field parameters. These parameters include solar wind speed (Vmax), total magnetic field strength (Bmax), the southward component of the interplanetary magnetic field (Bz), and the convective electric field (Ey). Wu, C.-C., et al., (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2006\u003c/span\u003e\u003cb\u003e) and Echer, E., Gonzalez, W. D., \u0026amp; Tsurutani, B. T. (2008).\u003c/b\u003e Sun, X., et al., (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe plasma and geomagnetic data used in this study are obtained from the \u003cb\u003eOMNIWeb database\u003c/b\u003e, which provides 1-hour resolution solar wind and geomagnetic indices. Event identification and classification are based on the \u003cb\u003eRichardson and Cane ICME catalog\u003c/b\u003e, allowing for systematic analysis of ICME- and MC-associated storms, both with and without shock signatures.\u003c/p\u003e\u003cp\u003eThe results reveal that \u003cb\u003eshocks play a crucial role in enhancing the geoeffectiveness\u003c/b\u003e of ICMEs and MCs, with the strongest geomagnetic responses observed in cases where shocks precede the main structure. Further analysis shows that among various coupling parameters, the \u003cb\u003esouthward Bz component and the product Vmax \u0026times; Bzmin\u003c/b\u003e serve as the most consistent predictors of geomagnetic storm strength. Santoso, Aet al., (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Krauss, S., et al., (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Joshi, N. C., et al., (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOverall, the study contributes to a better understanding of the solar wind-magnetosphere interaction processes, highlighting the importance of magnetic field orientation and energy coupling in geomagnetic storm development. These insights are valuable for improving \u003cb\u003espace weather forecasting models\u003c/b\u003e and for developing strategies to mitigate the effects of geomagnetic disturbances on technological systems and infrastructure.\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003ch3\u003e\u003cstrong\u003e2.1 Data Sources\u003c/strong\u003e\u003c/h3\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eSolar Wind and Geomagnetic Data\u003c/strong\u003e: Hourly-averaged plasma and field parameters, including solar wind speed (V_max), magnetic field strength (B_max), the southward component of the interplanetary magnetic field (B_z), and the convective electric field (E_y), were obtained from the OMNIWeb database .omniweb.gsfc.nasa.gov\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eEvent Categorization\u003c/strong\u003e: ICME and MC events were identified and categorized using the Richardson and Cane ICME catalog .SIDC\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003cstrong\u003e2.2 Analytical Techniques\u003c/strong\u003e\u003c/h3\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eSuperposed Epoch Analysis\u003c/strong\u003e:\u0026nbsp;Implemented using Fortran programming, this technique involves aligning multiple events based on a common reference point (e.g., storm onset) to identify average behaviors and patterns across events. \u003cstrong\u003eKamide, Y., \u0026amp; Kokubun, S. (1996) and Huttunen, K. E. J., at al., (2002).\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCorrelation Analysis\u003c/strong\u003e: Using Origin software, this analysis examines the relationships between geomagnetic storm intensity (e.g., Dst index) and key plasma/field parameters (e.g., V_max, B_max, B_z, E_y) \u003cstrong\u003eNewell, P. T. et al., (2007), Borovsky, J. E., \u0026amp; Denton, M. H. (2006), Echer, E., Gonzalez, W. D., \u0026amp; Tsurutani, B. T. (2008); Wu, C.-C., et al., (2006).\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003cstrong\u003e2.3 Classification of Geomagnetic Disturbances\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eGeomagnetic disturbances were classified based on Dst index values into four categories:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eQuiet\u003c/strong\u003e: Dst \u0026gt; -30 nT ResearchGate+15arXiv+15PMC+15\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eWeak\u003c/strong\u003e: -50 nT \u0026lt; Dst \u0026le; -30 nT arXiv\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eModerate\u003c/strong\u003e:\u0026nbsp;-100 nT \u0026lt; Dst \u0026le; -50 nT\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eIntense\u003c/strong\u003e: Dst \u0026le; -100 nT arXiv+2arXiv+2MDPI+2\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\u003cstrong\u003e2.4. Temporal Scope\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe study period spans from 1998 to 2018, covering Solar Cycles 23 and 24, allowing for the examination of solar activity variations and their impact on geomagnetic disturbances.\u003c/p\u003e"},{"header":"3. Result and Discussion","content":"\u003cp\u003eThis study investigates geomagnetic disturbances associated with Interplanetary Coronal Mass Ejections (ICMEs) and Magnetic Clouds (MCs) from 2009 to 2018. Using superposed analysis with Fortran programming and correlation analysis with Origin software, we analyze key plasma/field parameters, including solar wind speed, magnetic field strength, and geomagnetic indices such as Dst and Ap. The analysis reveals a significant variation in geomagnetic disturbance intensity, showing that ICMEs and MCs with shocks produce stronger disturbances, with the highest geomagnetic storm intensity observed during periods of shock interaction. The study highlights the critical role of magnetic field orientation and energy coupling in determining the intensity of geomagnetic storms.\u003c/p\u003e\n\u003cp\u003eFigure 1a shows the \u003cstrong\u003eannual distribution\u003c/strong\u003e of four categories of geomagnetic storm events (\u003cstrong\u003eQ, W, M, S\u003c/strong\u003e) from \u003cstrong\u003e2009 to 2018\u003c/strong\u003e. Event counts vary year to year, but stronger events (M, S) appear consistently across the decade. Indicates ongoing solar activity and recurring geomagnetic storms with varying intensity.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDistribution of Geomagnetic Disturbances (2009\u0026ndash;2018).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStorm\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2009 (in %)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003cp\u003e(in %)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003cp\u003e(in %)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003cp\u003e(in %)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003cp\u003e(in %)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003cp\u003e(in %)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003cp\u003e(in %)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003cp\u003e(in %)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003cp\u003e(in%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2018 (in%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003e(in %)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003cp\u003e(82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003cp\u003e(60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003cp\u003e(62.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(+\u0026thinsp;2)*\u003c/p\u003e\n \u003cp\u003e(24.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e(32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e(40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7(+\u0026thinsp;1)*\u003c/p\u003e\n \u003cp\u003e(21.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e(54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e(44.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e(62.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003cp\u003e(43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e(9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e(13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e(12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e(21.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e(28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e(20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e(15.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e(15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e(22.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e(25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003cp\u003e(18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e(9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e(27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e(15.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003cp\u003e(36.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e(32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e(35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003cp\u003e(40.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003cp\u003e(23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e(11.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003cp\u003e(27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003cp\u003e(9.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003cp\u003e(18.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e(8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e(5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e(21.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e(8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e(22.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e(12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003cp\u003e(12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003etotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e198\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\"\u003e*use for additional events those are having positive Dst value\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. shows the distribution of geomagnetic disturbances by intensity (\u003cstrong\u003eQuiet, Weak, Moderate, and Intense\u003c/strong\u003e) from \u003cstrong\u003e2009 to 2018\u003c/strong\u003e. It includes the \u003cstrong\u003enumber of events\u003c/strong\u003e and \u003cstrong\u003epercentages\u003c/strong\u003e for each intensity in each year, with a total count at the bottom. On yearly basis\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eQuiet storms\u003c/strong\u003e were dominant in most years, particularly in the earlier years (2009\u0026ndash;2018).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eWeak disturbances\u003c/strong\u003e were generally low, peaking slightly in years like 2012 and 2018.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eModerate storms\u003c/strong\u003e showed significant variability, particularly peaking in 2012 and 2015.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eIntense storms\u003c/strong\u003e were rare, peaking in 2012 but generally making up a small fraction of events.\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eIn short, Quiet storms\u003c/strong\u003e were consistently the most common category. \u003cstrong\u003eWeak disturbances\u003c/strong\u003e remained relatively minor. \u003cstrong\u003eModerate storms\u003c/strong\u003e were present in fluctuating numbers, with higher peaks in specific years like 2012 and 2015. \u003cstrong\u003eIntense storms\u003c/strong\u003e were generally rare, showing a clear absence in many years.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDistribution of Geomagnetic Disturbances (1998\u0026ndash;2008, Solar Cycle 23)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStorm\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1998\u003c/p\u003e\n \u003cp\u003e(in %)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1999\u003c/p\u003e\n \u003cp\u003e(in %)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2000\u003c/p\u003e\n \u003cp\u003e(in %)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2001\u003c/p\u003e\n \u003cp\u003e(in %)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2002\u003c/p\u003e\n \u003cp\u003e(in %)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2003\u003c/p\u003e\n \u003cp\u003e(in %)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2004\u003c/p\u003e\n \u003cp\u003e(in %)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2005\u003c/p\u003e\n \u003cp\u003e(in %)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2006\u003c/p\u003e\n \u003cp\u003e(in %)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2007 (in %)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2008\u003c/p\u003e\n \u003cp\u003e(in%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal (in %)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003cp\u003e(27.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003cp\u003e(39.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003cp\u003e(25.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003cp\u003e(23.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e(15.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e(18.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e(10.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e(17.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e(45.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e(50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e(100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003cp\u003e(25.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e(22.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e(24.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003cp\u003e(23.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e(17.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e(26.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e(18.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003cp\u003e(31.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003cp\u003e(20.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e(9.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003cp\u003e(21.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003cp\u003e(27.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e(21.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003cp\u003e(25.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003cp\u003e(28.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003cp\u003e(23.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e(36.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e(21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003cp\u003e(44.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e(36.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e(50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003cp\u003e(28.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e(22.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e(15.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003cp\u003e(25.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003cp\u003e(30.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003cp\u003e(31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003cp\u003e(27.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e(36.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e(17.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e(9.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003cp\u003e(24.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003etotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e276\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The table shows the distribution of geomagnetic disturbances by intensity\u0026mdash;\u003cstrong\u003eQuiet (Q), Weak (W), Moderate (M), and Intense (S)\u003c/strong\u003e\u0026mdash;over \u003cstrong\u003esolar cycle 23 (1998\u0026ndash;2008)\u003c/strong\u003e, listing both the \u003cstrong\u003enumber of events\u003c/strong\u003e and their \u003cstrong\u003epercentages\u003c/strong\u003e for each year. In year \u003cstrong\u003e2000\u003c/strong\u003e had the highest activity (51 disturbances), with a balanced distribution across all categories. \u003cstrong\u003eIntense storms\u003c/strong\u003e were most frequent between 1998 and 2004, particularly in 2000 and 2001. \u003cstrong\u003eModerate storms\u003c/strong\u003e remained a significant category, peaking in 2005. \u003cstrong\u003eWeak storms\u003c/strong\u003e showed no clear temporal trend, with the lowest frequency in the later years (2006\u0026ndash;2008). \u003cstrong\u003eQuiet storms\u003c/strong\u003e were more frequent in the early and late years of the solar cycle.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIn short, Intense storms\u003c/strong\u003e dominated the middle years of the solar cycle (1998\u0026ndash;2004), especially in 2000 and 2001. \u003cstrong\u003eModerate storms\u003c/strong\u003e were consistently present, reaching a peak in 2005. \u003cstrong\u003eQuiet storms\u003c/strong\u003e remained a notable category but were less frequent than in the 2009\u0026ndash;2018 period. \u003cstrong\u003eWeak storms\u003c/strong\u003e were the least frequent, peaking in the earlier years.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1b shows the b\u003c/strong\u003ereaks down how each storm category (Q, W, M, S) is associated with different \u003cstrong\u003esolar wind structures\u003c/strong\u003e (ICMEs and MCs), depending on the presence of \u003cstrong\u003eshocks\u003c/strong\u003e and \u003cstrong\u003ediscontinuities\u003c/strong\u003e.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eStrong storms (S and M)\u003c/strong\u003e are most often caused by \u003cstrong\u003ecomplex structures\u003c/strong\u003e, especially those with both shocks and discontinuities.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eWeak storms (Q and W)\u003c/strong\u003e tend to be associated with simpler or less energetic structures.\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGeomagnetic Disturbances Associated with ICMEs and MCs (2009\u0026ndash;2018)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStorms / groups\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal no. Of events\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQuiet\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWeak\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIntense\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICMEs without disturbance without shocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48(+\u0026thinsp;2)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 (62.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (18\u0026nbsp;.75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9 (18.75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICMEs with disturbance and without shocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 (57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12(25.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICMEs with disturbance with shocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101(+\u0026thinsp;1)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u0026nbsp;(29.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (16.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33\u0026nbsp;(32.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u0026nbsp;(20.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMCs without disturbance and without shocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u0026nbsp;(33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u0026nbsp;(20.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u0026nbsp;(46.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMCs with disturbance and without shocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u0026nbsp;(40.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u0026nbsp;(33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u0026nbsp;(26.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMCs with disturbance with shocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51(+\u0026thinsp;1)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u0026nbsp;(17.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u0026nbsp;(9.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u0026nbsp;(43.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u0026nbsp;(29.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e*number of events which are existing in the same group but having the +\u0026thinsp;ve Dst value. Not count in the total no of events for that\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e provides the distribution of geomagnetic disturbances by \u003cstrong\u003eintensity (Quiet, Weak, Moderate, Intense)\u003c/strong\u003e associated with different types of \u003cstrong\u003eICMEs (Interplanetary Coronal Mass Ejections)\u003c/strong\u003e and \u003cstrong\u003eMCs (Magnetic Clouds)\u003c/strong\u003e from \u003cstrong\u003e2009 to 2018\u003c/strong\u003e, based on the presence or absence of disturbances and shocks. Some events have positive Dst values (marked with *), indicating no true geomagnetic storm and thus excluded from total counts.\u003c/p\u003e\n\u003cp\u003eIn short, the presence of \u003cstrong\u003eshocks\u003c/strong\u003e significantly increases the occurrence of \u003cstrong\u003eModerate\u003c/strong\u003e and \u003cstrong\u003eIntense\u003c/strong\u003e storms. \u003cstrong\u003eMCs with shocks\u003c/strong\u003e produced the highest levels of \u003cstrong\u003eIntense\u003c/strong\u003e and \u003cstrong\u003eModerate\u003c/strong\u003e disturbances, showing that shocks are a key factor in producing geoeffective storms. Events without shocks were mostly associated with \u003cstrong\u003eQuiet\u003c/strong\u003e and \u003cstrong\u003eWeak\u003c/strong\u003e disturbances.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGeomagnetic Disturbances During Solar Cycle 23 (1998\u0026ndash;2008) with ICMEs and Shocks\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStorm\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1998\u003c/p\u003e\n \u003cp\u003e(in %)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1999\u003c/p\u003e\n \u003cp\u003e(in %)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2000\u003c/p\u003e\n \u003cp\u003e(in %)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2001\u003c/p\u003e\n \u003cp\u003e(in %)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2002\u003c/p\u003e\n \u003cp\u003e(in %)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2003\u003c/p\u003e\n \u003cp\u003e(in %)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2004\u003c/p\u003e\n \u003cp\u003e(in %)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2005\u003c/p\u003e\n \u003cp\u003e(in %)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2006\u003c/p\u003e\n \u003cp\u003e(in %)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2007\u003c/p\u003e\n \u003cp\u003e(in%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003etotal\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003cp\u003e(16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003cp\u003e(18.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e(12.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e(6.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e(11.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e(7.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e(25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003cp\u003e(10.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003cp\u003e(16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e(25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e(22.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e(17.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003cp\u003e(16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003cp\u003e(21.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e(10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003cp\u003e(25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003cp\u003e(19.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003cp\u003e(33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e(45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e(22.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003cp\u003e(34.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e(22.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e(35.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e(20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e(41.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e(50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003cp\u003e(29.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003cp\u003e(33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e(31.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003cp\u003e(41.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003cp\u003e(41.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003cp\u003e(50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e(35.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e(70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e(33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e(25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003cp\u003e(40.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003etotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e152\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e summarizes the distribution of geomagnetic disturbances by intensity (\u003cstrong\u003eQuiet, Weak, Moderate, and Intense\u003c/strong\u003e) during \u003cstrong\u003esolar cycle 23 (1998\u0026ndash;2008)\u003c/strong\u003e, specifically when \u003cstrong\u003eICMEs were associated with shocks\u003c/strong\u003e. The data includes the \u003cstrong\u003enumber of events\u003c/strong\u003e and their corresponding \u003cstrong\u003epercentages\u003c/strong\u003e for each year, along with a \u003cstrong\u003etotal count\u003c/strong\u003e for each intensity. Found o\u003cstrong\u003ebservations;\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eIntense storms\u003c/strong\u003e were most prevalent when ICMEs were associated with shocks.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eModerate storms\u003c/strong\u003e showed a steady presence, peaking in the mid-2000s.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eWeak and Quiet storms\u003c/strong\u003e were much less frequent in this cycle compared to later years.\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOn the bases of Figs. 1a and 1b over the decade, geomagnetic storms occur regularly, with strong storms (M and S) not uncommon. These stronger storms are most likely triggered by ICMEs and MCs that contain both a shock and a magnetic discontinuity, highlighting the importance of event complexity in storm severity.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eDe\u003c/strong\u003etailed statistical summary of \u003cstrong\u003eplasma/field parameters and geomagnetic indices\u003c/strong\u003e derived from a \u003cstrong\u003esuperposed epoch analysis\u003c/strong\u003e of different interplanetary structures from 2009 to 2018 from Figs. 2, 3, 4, and 5. It includes \u003cstrong\u003eDst, Ap, solar wind speed (V), magnetic field strength (B), its standard deviation (\u0026sigma;B), and related parameters (Bz, \u0026sigma;Bz, Ey, BV)\u003c/strong\u003e, along with the \u003cstrong\u003emean amplitudes (Am)\u003c/strong\u003e and \u003cstrong\u003estandard errors (\u0026plusmn;\u0026thinsp;Sm)\u003c/strong\u003e for each parameter.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eParameters/Groups\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo of events\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTime(tm)\u003c/p\u003e\n \u003cp\u003eDst/Ap\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDst\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAp\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eV\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003esigmaB\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(hour)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAm\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003eSm\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAm\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003eSm\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAm\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003eSm\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAm\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003eSm\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAm\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003eSm\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICME with shocks/no shocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10/01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-27.298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e450.439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICME with shock\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e08/01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-31.662\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e467.270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICME without shocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32/26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-22.260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e425.960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisturbance with shock/no shock\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26/02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-27.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e450.677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisturbance with shocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16/02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-36.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e445.172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.652\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.203\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisturbance no shocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32/26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-19.990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e420.907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMagnetic cloud with shocks/ no shocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34/03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-34.683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e446.805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMagnetic cloud with shocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34/03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-40.885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e468.731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.970\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.296\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMagnetic cloud with no shocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28/07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-25.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e416.533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"13\"\u003etm-storm sudden commandment\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"13\"\u003eSm- standard error of mean\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eContinuation of table 5\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eParameters/Groups\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo of events\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTime(tm)\u003c/p\u003e\n \u003cp\u003eDst/Ap\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eBz\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003esigmaBz\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eEy\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eBV\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(hour)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAm\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003eSm\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAm\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003eSm\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAm\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003eSm\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAm\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003eSm\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICME with shocks/no shocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10/01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.224\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICME with shock\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e08/01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.277\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICME without shocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32/26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisturbance with shock/no shock\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26/02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisturbance with shocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16/02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.393\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisturbance no shocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32/26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.662\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMagnetic cloud with shocks/ no shocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34/03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.381\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMagnetic cloud with shocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34/03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.567\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMagnetic cloud with no shocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28/07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.662\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.310\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e shows the summery of Figs. 2, 3, 4, and 5: \u003cstrong\u003eShock Presence Significantly Increases Geoeffectiveness\u003c/strong\u003e. Found the structures \u003cstrong\u003ewith shocks\u003c/strong\u003e consistently show \u003cstrong\u003ehigher values\u003c/strong\u003e of:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eDst depressions (more negative)\u003c/strong\u003e \u0026rarr; indicates stronger geomagnetic storms.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eAp index\u003c/strong\u003e \u0026rarr; higher overall geomagnetic activity.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eMagnetic field (B) and its fluctuations (\u0026sigma;B)\u003c/strong\u003e.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eElectric field (Ey)\u003c/strong\u003e and \u003cstrong\u003eBV\u003c/strong\u003e \u0026rarr; indicators of storm-driving energy.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eMagnetic clouds (MCs) with shocks\u003c/strong\u003e: \u003cstrong\u003e-40.89 nT\u003c/strong\u003e (strongest Dst depression).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eICMEs with shocks\u003c/strong\u003e: \u003cstrong\u003e-31.66 nT\u003c/strong\u003e.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eEvents without shocks\u003c/strong\u003e (ICMEs or MCs): Dst values are significantly \u003cstrong\u003eweaker\u003c/strong\u003e (e.g., MCs no shocks: \u003cstrong\u003e-25.07 nT\u003c/strong\u003e; ICMEs no shocks: \u003cstrong\u003e-22.26 nT\u003c/strong\u003e).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eICMEs with shocks\u003c/strong\u003e and \u003cstrong\u003eMCs with shocks\u003c/strong\u003e have the \u003cstrong\u003estrongest fields\u003c/strong\u003e (~\u0026thinsp;468 nT), while events \u003cstrong\u003ewithout shocks\u003c/strong\u003e have \u003cstrong\u003eweaker fields\u003c/strong\u003e (~\u0026thinsp;416\u0026ndash;425 nT).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026sigma;B and \u0026sigma;Bz\u003c/strong\u003e are highest in \u003cstrong\u003edisturbed/shocked environments\u003c/strong\u003e, particularly in \u003cstrong\u003edisturbed MCs with shocks\u003c/strong\u003e, indicating more turbulent fields.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003ePeak values occur in \u003cstrong\u003eMCs with shocks\u003c/strong\u003e found \u003cstrong\u003eEy\u003c/strong\u003e: 6.276 mV/m and \u003cstrong\u003eBV\u003c/strong\u003e: 4.293 nT\u0026middot;km/s\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eShocks\u003c/strong\u003e dramatically enhance the storm-driving potential of ICMEs and MCs.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eMagnetic Clouds with shocks\u003c/strong\u003e are the most geoeffective, with the strongest storms and energy inputs.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eThis analysis supports that \u003cstrong\u003einterplanetary structures with both magnetic field complexity and shock features are the main drivers of intense geomagnetic activity\u003c/strong\u003e.\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn short \u003cstrong\u003eShocks\u003c/strong\u003e dramatically enhance the storm-driving potential of ICMEs and MCs. \u003cstrong\u003eMagnetic Clouds with shocks\u003c/strong\u003e are the most geoeffective, with the strongest storms and energy inputs. This analysis supports that \u003cstrong\u003einterplanetary structures with both magnetic field complexity and shock features are the main drivers of intense geomagnetic activity\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InternalRef\"\u003eThese relationships are shown with scatter plots and correlation coefficients. The \u003cstrong\u003ecorrelation is stronger in non-shock events\u003c/strong\u003e, meaning magnetic field direction matters more than shock presence for storm intensity.\u003c/span\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCorrelation scattered plot result between Dst and Plasma field parameters for average individual peak values of all events during the period from 2009 to 2018\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGroups\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eTotal number of events\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eCorrelation coefficient with Dst\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eV\u003csub\u003emax\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eB\u003csub\u003emax\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBz \u003csub\u003emin\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEy\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003edVxBz\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eV\u003csub\u003emax\u003c/sub\u003exBz\u003csub\u003emin\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal individual events\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.720\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.646\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo Shocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.793\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6\u003c/strong\u003e presents the \u003cstrong\u003ecorrelation coefficients\u003c/strong\u003e between the \u003cstrong\u003eDst index\u003c/strong\u003e (a measure of geomagnetic storm intensity as shown in firgue 6) and several \u003cstrong\u003eplasma/field parameters\u003c/strong\u003e based on average individual peak values for events from \u003cstrong\u003e2009 to 2018\u003c/strong\u003e. The data is grouped by \u003cstrong\u003etotal events\u003c/strong\u003e, \u003cstrong\u003eevents with shocks\u003c/strong\u003e, and \u003cstrong\u003eevents without shocks\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003e\u003cstrong\u003eStrongest Correlations with Dst:\u003c/strong\u003e\n \u003cul type=\"circle\"\u003e\n \u003cli\u003e\u003cstrong\u003eBz min\u003c/strong\u003e shows the \u003cstrong\u003estrongest positive correlation\u003c/strong\u003e with Dst across all groups, especially \u003cstrong\u003ewithout shocks (0.781)\u003c/strong\u003e.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eVmax \u0026times; Bzmin\u003c/strong\u003e and \u003cstrong\u003eEy\u003c/strong\u003e also show strong correlations, indicating their importance in storm strength.\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eShock vs No-Shock Events:\u003c/strong\u003e\n \u003cul type=\"circle\"\u003e\n \u003cli\u003e\u003cstrong\u003eNo-shock events\u003c/strong\u003e show \u003cstrong\u003ehigher correlation coefficients\u003c/strong\u003e than shock events, suggesting that \u003cstrong\u003enon-shock-driven storms\u003c/strong\u003e might be more directly influenced by magnetic field orientation and energy coupling.\u003c/li\u003e\n \u003cli\u003eFor no-shock events:\u003cul type=\"square\"\u003e\n \u003cli\u003e\u003cstrong\u003eEy:\u003c/strong\u003e Strongest negative correlation (\u003cstrong\u003e-0.736\u003c/strong\u003e) \u0026ndash; indicates stronger electric fields drive more negative Dst (intense storms).\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eVmax \u0026times; Bzmin:\u003c/strong\u003e Strongest overall coupling indicator (\u003cstrong\u003e0.793\u003c/strong\u003e).\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eWeaker Correlations:\u003c/strong\u003e\n \u003cul type=\"circle\"\u003e\n \u003cli\u003e\u003cstrong\u003eVmax\u003c/strong\u003e (solar wind speed alone) has the \u003cstrong\u003eweakest correlation\u003c/strong\u003e in all cases, suggesting that \u003cstrong\u003espeed alone is not a reliable predictor\u003c/strong\u003e of storm strength without considering magnetic field orientation.\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe \u003cstrong\u003esouthward Bz component\u003c/strong\u003e and derived coupling parameters like \u003cstrong\u003eVmax \u0026times; Bzmin\u003c/strong\u003e and \u003cstrong\u003eEy\u003c/strong\u003e are \u003cstrong\u003ethe most reliable predictors of geomagnetic storm intensity (Dst)\u003c/strong\u003e. \u003cstrong\u003eShocks are not the dominant factor\u003c/strong\u003e in determining the strength of correlation; \u003cstrong\u003efield orientation and energy transfer\u003c/strong\u003e are more critical.\u003c/p\u003e\n\u003cp\u003eHere is the heat map showing the correlation between Dst and various plasma/field parameters across different event groups. The strongest positive correlation is with \u003cstrong\u003eBz min\u003c/strong\u003e and \u003cstrong\u003eVmax \u0026times; Bzmin\u003c/strong\u003e, especially for \u003cstrong\u003eno-shock events\u003c/strong\u003e, while \u003cstrong\u003eEy\u003c/strong\u003e also shows strong negative correlations with Dst.\u003c/p\u003e"},{"header":"4.\tComparison and Conclusion","content":"\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eIntense storms\u003c/strong\u003e: More common in \u003cstrong\u003esolar cycle 23 (1998-2008)\u003c/strong\u003e when associated with ICMEs and shocks (40.8%). In contrast, during \u003cstrong\u003e2009-2018\u003c/strong\u003e, intense storms were rare, typically around 10-12%.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eModerate storms\u003c/strong\u003e: Showed varying patterns across both cycles. In \u003cstrong\u003e2009-2018\u003c/strong\u003e, moderate disturbances were more frequent than in \u003cstrong\u003e1998-2008\u003c/strong\u003e, especially in years like 2012 and 2015, while moderate storms were consistently present in \u003cstrong\u003esolar cycle 23\u003c/strong\u003e, peaking in 2005.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eQuiet storms\u003c/strong\u003e: Were dominant throughout both periods, particularly in \u003cstrong\u003e2009-2018\u003c/strong\u003e, with quiet disturbances making up 43% of all events, much higher than the 25% in \u003cstrong\u003esolar cycle 23\u003c/strong\u003e.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eWeak storms\u003c/strong\u003e: Were consistently low in both periods, though there was a slight increase in 2012 (21.2%) in \u003cstrong\u003e2009-2018\u003c/strong\u003e compared to the earlier solar cycle.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eKey Pattern:\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eThe presence of \u003cstrong\u003eshocks\u003c/strong\u003e in both cycles was a clear factor driving the increase in \u003cstrong\u003eModerate\u003c/strong\u003e and \u003cstrong\u003eIntense\u003c/strong\u003e geomagnetic disturbances.\u003c/li\u003e\n \u003cli\u003eShocks, especially associated with \u003cstrong\u003eMCs\u003c/strong\u003e, produced the most significant and geoeffective storms in both periods, emphasizing the importance of shock-driven disturbances in geomagnetic activity.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn conclusion, \u003cstrong\u003esolar cycle 23\u003c/strong\u003e showed a higher frequency of \u003cstrong\u003eIntense\u003c/strong\u003e disturbances compared to the \u003cstrong\u003e2009-2018 period\u003c/strong\u003e, where \u003cstrong\u003eQuiet\u003c/strong\u003e storms were far more prevalent. The key takeaway is that the presence of \u003cstrong\u003eshocks\u003c/strong\u003e greatly enhances the geomagnetic effects, leading to stronger and more frequent disturbances.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThe author confirms sole responsibility for the conceptualization, data collection, analysis, methodology development, programming, visualization, and writing of this manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eI would like to acknowledge for the continuous support from the Centre for Theoretical Physics, JMI, particularly Prof. Sushant G Ghosh. I also acknowledge and appreciate the publication of the ICMEs catalog by I. G. Richardson and Cane, https://izw1.caltech.edu/ACE/ASC/DATA/level3/icmetable2.htm, 2019, as well as the use of the OmniWeb database of NASA/GSFC(OMNIWeb Results).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhmed, O. M., Ahmad, B. Z., \u0026amp; Derouich, M. (2024). Characteristics and development of the main phase disturbance in geomagnetic storms (Dst \u0026le; -50 nT). arXiv preprint \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003earXiv:\u003c/span\u003e\u003cspan address=\"http://arXiv:\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e2402.03261. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://arxiv.org/abs/2402.03261\u003c/span\u003e\u003cspan address=\"https://arxiv.org/abs/2402.03261\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBorovsky, J. 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Solar and Interplanetary Sources of Major Geomagnetic Storms (Dst\u0026thinsp;\u0026le;\u0026thinsp;\u0026ndash;\u0026thinsp;100 nT) During 1996\u0026ndash;2005. \u003cem\u003eJournal of Geophysical Research: Space Physics, 112(A10)\u003c/em\u003e. https://doi.org/10.1029/2007JA012321\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Coronal mass ejections, geomagnetic storm, solar cycle, magnetic cloud, solar wind","lastPublishedDoi":"10.21203/rs.3.rs-6586520/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6586520/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study analyzes geomagnetic disturbances from 1998 to 2018, focusing on the role of Interplanetary Coronal Mass Ejections (ICMEs) and Magnetic Clouds (MCs), particularly their impact when accompanied by shocks. Events are classified into four intensity levels\u0026mdash;Quiet, Weak, Moderate, and Intense\u0026mdash;and trends are studied across Solar Cycles 23 and 24. Using superposed analysis (via Fortran programming) and correlation analysis (via Origin software), the research examines relationships between storm intensity (Dst index) and plasma/field parameters, including solar wind speed (Vmax), magnetic field strength (Bmax), southward Bz, and convective electric field (Ey). Data is sourced from the OMNIWeb database, and events are categorized using the Richardson and Cane catalog. The study reveals that shocks significantly enhance the geoeffectiveness of ICMEs and MCs, with storms associated with these shocks showing the strongest geomagnetic responses.\u003c/p\u003e\u003cp\u003eBy comparing events with and without shocks, this work demonstrates that southward Bz and energy coupling parameters like Vmax \u0026times; Bzmin are the most reliable predictors of storm strength. The results provide valuable insights into the drivers of geomagnetic activity, emphasizing the role of magnetic field orientation and energy transfer in shaping the intensity of geomagnetic disturbances. This analysis offers important contributions to space weather forecasting, enhancing the understanding of solar-terrestrial interactions and supporting efforts to mitigate the effects of geomagnetic storms on technological systems. These findings support improved understanding and forecasting of geomagnetic storms and their technological impacts.\u003c/p\u003e","manuscriptTitle":"Characterization of Geomagnetic Storms Using Superposed Epoch and Correlation Analyses: A Study of ICME and Magnetic Cloud Events (1998–2018) for Space Weather Forecasting","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-01 05:52:17","doi":"10.21203/rs.3.rs-6586520/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"22748f56-7174-4cce-851d-fdae07b21884","owner":[],"postedDate":"September 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T23:23:39+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-01 05:52:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6586520","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6586520","identity":"rs-6586520","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}