Low-dimensional chaos in the magnetospheric dynamics.
#A QUIET STORM BT SERIES#
Using the time series data of magnetospheric activity alone such as auroralĮlectrojet (AE), amplitude lower (AL) and D st index (Vassiliadis etĪl., 1990 Baker and Klimas, 1990 Vassiliadis et al., 1991 Shan et al.,ġ991 Pavlos, 1994 Klimas et al., 1996 Valdivia et al., 2005 Several works have been presented on the chaotic and dynamical complexityīehaviour of the magnetospheric dynamics based on an autonomous concept, i.e. Signals and the magnetospheric activity index ( D st) as output duringĭifferent categories of geomagnetic storms. The chaotic behaviour of the solar wind electric field ( VB s) as input Magnetospheric time series must be related to the concept of input–outputĭynamical process (Russell et al., 1974 Burton et al., 1975 Gonzalez etĪl., 1989, 1994). That the electrodynamic response of the magnetosphere to solar wind driversĪre non-autonomous in nature (Price and Prichard, 1993 Price et al., 1994 Signal becomes more complex and non-linear in nature. Geomagnetic storm increases, the fluctuation behaviour in the D st One obvious reason is that as the intensity of the Geomagnetic storm, fluctuations in the D st signals differ (OludehinwaĮt al., 2018). Solar-wind–magnetospheric coupling processes. These mayĪrise from the changes in the interplanetary electric fields driven by the Ordinarily, one can easily anticipate thatįluctuations in a D st signal appear chaotic and complex. The D st fluctuations exhibit different signatures for differentĬategories of geomagnetic storm. Reveal the severity of a geomagnetic storm (Dessler and Parker, 1959). Index remains one of the most popular global indicators that can precisely Of the total energy of the ring current particles. (Sugiura, 1964 Sugiura and Kamei, 1991) unveiled a quantitative measure Notably, the introduction of the disturbance storm time ( D st) index Mendes et al., 2017 Hajra and Tsurutani, 2018 Tsurutani and Hajra, 2021 Russell, 2001). Last from days to weeks (Akasofu, 1964 Tsurutani and Meng, 1972 Meng et al.,ġ973 Tsurutani and Gonzalez, 1987 Hajra et al., 2013 Liou et al., 2013 Magnetopause leads to isolated substorms or convection events such as the high-intensity long-duration continuous AE activity (HILDCAA, where AE represents auroral The sporadic magnetic reconnectionīetween the southward component of the Alfvén waves and the earth's Sheath and an ICME magnetic cloud (Gonzalez and Tsurutani, 1987 TsurutaniĪnd Gonzalez, 1987 Tsurutani et al., 1988 Cowley, 1995 Tsutomu, 2002 Interplanetary coronal mass ejection (ICME), or by a combination of the Which indicates that solar-wind–magnetosphere coupling is in progress, wasĬonfirmed on many occasions for which such a geomagnetic storm was driven byĬo-rotating interaction regions (CIRs), by the sheath preceding an Prolonged southward turning of interplanetary magnetic field (IMF, B z), Thereby initiating a geomagnetic storm (Dungey, 1961 Pavlos et al., 1992). This is due toĬhanges in the interplanetary electric fields imposed on the magnetopauseĪnd those penetrating the inner magnetosphere and sustaining convection, Magnetospheric dynamics varies (Tsurutani et al., 1990). The response of chaos and dynamical complexity behaviour with respect to Suggest that the magnetospheric dynamics are non-linear, and the solar windĭynamics are consistently stochastic in nature. VB s as input and D st as output of the magnetospheric system Based on these findings, the dynamical features obtained in the Series during major geomagnetic storm reveals the strongest non-linearityįeatures. The test for non-linearity in the D st time
However, the MLE and ApEn values obtained from VB s indicate thatĬhaotic and dynamical complexity responses are high with no significantĭifference between the periods that are associated with minor, moderate and Geomagnetic storms and decline further during major geomagnetic storms. Responses are high during minor geomagnetic storms, reduce at moderate The MLE andĪpEn values of the D st indicate that chaotic and dynamical complexity Non-linear parameter and the categories of geomagnetic storm. We found a significant trend between each
The time series data of the D stĪnd VB s are analysed for a period of 9 years using non-linearĭynamics tools (maximal Lyapunov exponent, MLE approximate entropy, ApEn Īnd delay vector variance, DVV). Response to the disturbance storm time ( D st) and solar wind electricįield ( VB s) during different categories of geomagnetic storm (minor, moderate and major geomagnetic storm). In this study, we examine the magnetospheric chaos and dynamical complexity
0 kommentar(er)
