Symbolic analysis of slow solar wind data using rank order statistics

TL;DR

This study applies rank order statistics to analyze slow solar wind velocity data from Helios spacecraft, revealing that the underlying dynamics are stable across solar cycles but fluctuations increase before solar maximum.

Contribution

The paper introduces the use of rank order statistics for analyzing solar wind data, demonstrating its effectiveness in identifying dynamical stability and fluctuation patterns across solar cycles.

Findings

Underlying solar wind dynamics remain stable during activity cycles

Fluctuations increase before solar activity maximum

Rank order statistics effectively analyze nonlinear time series

Abstract

We analyze time series data of the fluctuations of slow solar wind velocity using rank order statistics. We selected a total of 18 datasets measured by the Helios spacecraft at a distance of 0.32 AU from the sun in the inner heliosphere. The datasets correspond to the years 1975-1982 and cover the end of the solar activity cycle 20 to the middle of the activity cycle 21. We first apply rank order statistics to time series from known nonlinear systems and then extend the analysis to the solar wind data. We find that the underlying dynamics governing the solar wind velocity remains almost unchanged during an activity cycle. However, during a solar activity cycle the fluctuations in the slow solar wind time series increase just before the maximum of the activity cycle