Wavelet and Cross-Correlation Analysis of Relativistic Electron Flux with Sunspot Number, Solar Flux, and Solar Wind Parameters

Abstract

The Geostationary Operational Environmental Satellites (GOES) have been monitoring the Earth's radiation environment and is providing the electron flux data (of energy >0.8 MeV, >2 MeV, and >4 MeV) by means of a connected sensor subsystem. Relativistic electron flux is one of the components of the radiation belt which not only affects the electrical system in satellites but also has an impact on Earth’s upper atmospheric climatic variation. We have carried out a study to determine the relation of sunspot number (R), solar flux (F10.7), and solar wind parameters i.e., solar wind velocity (Vsw), plasma density Nsw), the southern component of the interplanetary magnetic field (IMF-Bz), Plasma temperature (Tsw) with relativistic electron flux of energy >0.8 MeV, >2 MeV, and >4 MeV in outer radiation belt using the data of 24 years (1996-2020) covering solar cycle 23 and 24. Time series analysis, Cross-correlation and wavelet analysis techniques have been used in this study. The time series plot displayed that the radiation is occupied mostly by electron flux of energy less than 4 Mev and solar cycle 23 (1996-2008) was strong to produce more intensity of relativistic electron flux of all energy in comparison to cycle 24 (2008-2019). Results from cross-correlation analysis illustrated that Bz has no significant impact on the enhancement of relativistic electron flux of any energy range in the radiation belt. Whereas other studied parameters have a positive correlation with relativistic electron flux, but with significantly different coefficient values for different energy. We found that electron flux >0.8 MeV and >2 MeV has a strong positive association with sunspot number, solar flux, solar wind velocity, plasma density and temperature whereas weak correlation with electron flux of energy >4 MeV. This result leads us to conclude that solar activity and solar parameters have greater influence in producing relativistic electron flux of energy ~ 0.8-4 MeV, than of flux > 4 MeV. The study made to observe the distribution of relativistic electrons in radiation belt with time through continuous wavelet analysis showed that electron flux of energy >0.8 has a higher periodicity in comparison to the flux of other energy ranger.

Global Positioning System Observations of Ionospheric Total Electron Content Variations During the 15th January 2010 and 21st June 2020 Solar Eclipse

“…Cross-correlation is a standard tool for measuring statistical relation among distinctive variables and to measure the degree of similarity and additionally draw identical characters relative to each other in time to explore the unseen new information that can be obtained ; Adhikari Adhikari et al, 2019;Poudel et al, 2020). In this research, we have implemented the cross-correlation method to achieve correlation coefficients and time lag among TEC of eclipse incidence region (Nepal) and non-incidence region (Japan) during the day of the eclipse and quiet day.…”

Geomagnetically Quiet Period Analysis of Relativistic Electrons, Auroral Precipitation, Joule Heating, and Ring Current During the Years of 1999, 2000 and 2004

“…This work extends the cross-correlation to Joule heating, ring current, and auroral precipitation and, as explained above, finds high correlations with relativistic electron population. Poudel et al [34] found a good correlation of relativistic electrons with solar wind velocity and plasma density. The correlation with Joule heating, ring current, and auroral precipitation further supports the inference because the plasma density in the ionosphere is closely related to the magnitude of Joule heating, ring current, and auroral precipitation.…”

“…Specifically, the variation in all three factors and relativistic electron fluxes are in synchronization. Poudel et al [34] had performed a cross-correlation analysis of relativistic electron flux with sunspot number, solar flux, and solar wind parameters. This work extends the cross-correlation to Joule heating, ring current, and auroral precipitation and, as explained above, finds high correlations with relativistic electron population.…”

Statistical Analysis of the Monthly Activity of the Solar Flare

“…Cross-correlation (C-C) is a method of learning unlike parameters to measure likenesses and plot analogous relative features, which can tell new information (Poudel et al, 2020). It is convenient as a measure of the similarity between two-time series as a function of one's delay relation to the other since its highest happens at the lag at which the two-time series are greatest associated (Menke and Menke, 2012).…”