Position: 4th year PhD student
Supervisor: Prof. Sandra Chapman
Project title: Statistical techniques for quantifying the variability of the solar wind over multiple solar cycles
Background: MPhys, University of Manchester, 2015
The activity of the Sun varies over an 11 year cycle, however each cycle is unique in its length and peak activity. This variability is mapped into the solar wind; a plasma which flows radially outwards from the solar corona. When it reaches Earth, the solar wind transfers some of its energy into Earth's magnetosphere, resulting in space weather events such as the aurora. These events, however, can have detrimental effects on GPS navigation, communication networks and power systems. It is therefore important to understand how the solar wind transports energy from the Sun, how much energy is transferred into Earth's magnetosphere, and how this varies within and between the different solar cycles.
My PhD research aims to address these questions using statistical techniques. The long, high time resolution observations of solar wind plasma parameters (e.g. from NASA's Wind satellite) have provided a substantial dataset which is ideal for statistical analysis. I have used QQ plots to study how statistical characteristics of the solar wind plasma change over time, and have studied continuous bursts over high thresholds to investigate the more extreme events we see in solar wind observations. In addition, I have used spatial clustering analysis to probe the impact of space weather on the Total Electron Content of Earth’s ionosphere. This work will improve understanding of the underlying dynamic processes of the solar wind plasma, and could also aid the prediction of the likely occurrence of extreme, potentially hazardous, space weather events.
 Tindale, E., and S.C. Chapman (2016), Solar cycle variation of the statistical distribution of the solar wind ε parameter and its constituent variables, Geophys. Res. Lett., 43(11), doi: 10.1002.2016GL068920.
 Graves, T., C.L.E. Franzke, N.W. Watkins, R.B. Gramacy, and E. Tindale (2017), Systematic inference of the long-range dependence and heavy-tail distribution parameters of ARFIMA models, Physica A: Stat. Mech. & App., 473, pp. 60-71, doi: 10.1016/j.physa.2017.01.028.
 Tindale, E., and S.C. Chapman (2017), Solar Wind Plasma Parameter Variability Across Solar Cycles 23 and 24: From Turbulence to Extremes, J. Geophys. Res.: Space Physics, 122, doi: 10.1002/2017JA024412.
 Chapman, S.C. N.W. Watkins, and E. Tindale (2018), Reproducible aspects of the climate of space weather over the last five solar cycles, Space Weather, 16, 10.1029/2018SW001884.
 Tindale, E., S.C. Chapman, N.R. Moloney, and N.W. Watkins (2018), The dependence of solar wind burst size on burst duration and its invariance across solar cycles 23 and 24, J. Geophys. Res.: Space Physics, 123, doi: 10.1029/2018JA025740.
Email: l dot tindale at warwick dot ac dot uk
Office: Physical Sciences building, room PS117