The Sun's variable conditions significantly impact the geospace environment which comprises of the Interplanetary Medium (IPM), and the ionosphere-magnetosphere system, collectively known as space weather [1,2]. This space weather variation is primarily driven by CMEs [3], which are massive ejections of plasma and magnetic fields from the solar corona [4,5]. CMEs have the potential to compress the magnetosphere and trigger geomagnetic storms, which can adversely affect our telecommunication systems, power grids, and satellite electronics [6,7].

Solar variability, the root cause of these space weather effects, is closely associated with sunspots [8]. Sunspots are dark regions on the Sun's surface resulting from the twisting of toroidal magnetic fields due to processes in the solar convection zone. The appearance of sunspots defines the solar activity cycle (11-year cycle), with solar minimum periods characterized by few or no sunspots and solar maximum periods featuring numerous sunspots [8]. The characteristics of sunspots depend on the differential rotation between the Sun's poloidal and toroidal magnetic fields [9]. At a critical point in this differential rotation, the toroidal magnetic field becomes strong enough to rise and disrupt the solar surface [10,11]. This disruption leads to the reconnection of multipolar magnetic fields on the Sun's surface [11,12] resulting in the ejection of significant amounts of radiation in the form of solar flares and massive amounts of plasma known as CMEs into space [10]. If these solar events are directed toward Earth with their magnetic fields-oriented southward, they can have substantial negative impacts on space weather [13, 14]. Geomagnetic storms are more likely to occur during periods of high solar activity [15,16], studied the distribution and recovery phase of geomagnetic storms during Solar Cycles 23 and 24, and found a correlation between geomagnetic storm frequency and solar activity, using sunspot numbers as a solar activity proxy. It confirms that intense storms are more frequent during active solar cycles in this study, data from SCs 23 and 24 were selected for analysis. The research aims to enhance our understanding of solar variability and its implications for space weather.

Data Collection and Sources

The Sunspot Number (SSN) and Sunspot Area (SSA) data utilized in this study were retrieved from the OMINI Web Service Archive, accessible through NASA's Space Physics Data Facility, covering the period from 1996 to 2019. SC 23, spanning from August 1996 to December 2008, was followed by SC 24, which encompassed December 2008 to December 2019.

The data related to CMEs used in our research were obtained from the Large Angle and Spectrometer Coronagraph (LASCO) instrument, which is part of the Solar and Heliospheric Observatory (SOHO). The LASCO CME Catalogue, available online via the Catholic University's repository (http://cdaw.gsfc.nosa.gov/CME- list), contains a comprehensive list of CMEs identified manually from 1996 onwards. It should be noted that there were gaps in the collected CME data during certain periods: (i) in 1998 from 24 June to 22 October, (ii) failure of all three gyroscopes caused an interruption from 21 December 1998 to 6 February 1999, and (iii) in June 2003. Nominal observations resumed on July 10, 2003. From the LASCO CME Catalogue, we extracted these few essential CME parameters for our analysis: Central Position Angle (CPA): This angle, measured in degrees, represents the angle from the solar disk center to the point of CME ejection (halo CMEs, which cover the occultation disk, lack a defined CPA); Speed at 20R_SUN: This is the speed of a CME at a distance of 20 solar radii from the center of the Sun, measured in km/s, which is important as it provides information about the behavior of the CME as it leaves the influence of the Sun; Linear Speed (LS): The linear speed of CMEs which represents how fast the CME is moving away from the sun, measured in km/s; Mass (m): The mass of CMEs, is determined through white-light coronagraph images [17,18], measured in kg.

Data Analysis

We compared the variations of monthly averaged solar activity parameters for SCs 23 and 24 and conducted a yearly mean and median time series analysis for the selected CME parameters across the entire dataset to facilitate easy comparison of CME variations. We also plotted the histogram distributions of the 27- day average CME parameters to determine their skewness, and kurtosis and analyzed the 27-day averages of one parameter against another to determine the correlations between them. Additionally, we examined the relationship between the CME parameters and solar activity- SSN and SSA. The findings of this research are significant for solar cycle modeling and prediction, providing valuable insights for theoreticians to enhance our understanding of solar phenomena and their potential effects on Earth's space environment [19].

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