Solar Wind Control of Radiation Belt Electron Flux

Lead Research Organisation: University of Reading
Department Name: Meteorology


Our increasing reliance upon space-based technologies (communications, location-finding, etc) means that predicting the "Space Weather" of Earth's Radiation Belts is very desirable. The high-energy electron flux within Earth's Outer Radiation Belt is highly variable on timescales of hours to days, but we do not yet know the major factors that control this variability. It is clear that important solar wind transient features, such as coronal mass ejections and stream interaction regions can alter the size and strength of the Outer Radiation Belt, but the changes are difficult to predict. Spacecraft anomalies including electrostatic discharges and single-event upsets are related to increases in the density of high-energy electrons in the spacecraft environment. The Outer Radiation Belt spans the distance from around 2.5 Earth radii from the centre of the Earth to beyond geosynchronous orbit, at 6.6 Earth radii, but until recently there have been few opportunities to scientifically sample the high-energy electron environment, principally because it is so hazardous to spacecraft and electronic instruments. Physics-based numerical models of the Outer Radiation Belt are in their infancy. It is a significant challenge to reduce the complex plasma physics of collisionless, relativistic electron dynamics to a numerical system that can be solved on the necessary timescales. This project will provide valuable insight into the most important physical processes linking the solar wind the Outer Radiation Belt that can be used to build such a physics-based model in the future.

There are two key novelties in the planned project. First, we will use data from the NASA Van Allen Probes mission to provide information on the variability of electron flux across a range of different distances from the Earth. This mission (launched in 2013) will provide the project with at least four years of in-situ space plasma data in unprecedented detail and in multiple locations simultaneously throughout the Outer Radiation Belt. Previous attempts to investigate the relationship between solar wind variability and Radiation Belt fluxes have been restricted to locations near geosynchronous orbit due to a lack of in situ data from other locations. Spacecraft data from the solar wind and from the Van Allen Probes will be combined to investigate how variability in the solar wind relates to variability in the Earth's Radiation Belts and whether there are repeatable patterns in both that may be predicted successfully.

Second, cutting-edge machine learning techniques will be used to investigate the relationship between solar wind variability and the electron flux and which sets of parameters best predict future radiation belt conditions. Machine-learning techniques can be used to find repeatable patterns in empirical data and then build them into predictive models. Solar wind parameters such as speed, number density and magnetic field orientation all contribute to changes in the Earth's magnetosphere, and especially in the Outer Radiation Belts, but they also exhibit inter-dependencies due to the physics of the solar wind. We will begin by studying patterns in the solar wind variability, where techniques will be developed for time series at a single point in space. Later, these techniques will be employed to build models of the variability of electron flux over a range of distances from the Earth, based upon inputs from the solar wind data. Carefully interpreted, machine-learning techniques allow us to determine those parameters that most influence the changing electron flux and provide indispensable clues for the physical mechanisms by which they exert that influence. By judiciously interpreting the results from machine-learning algorithms in the framework of space plasma physics, it is hoped to gain new insight into how the solar wind controls the variability of the whole Outer Radiation Belt.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
ST/R505031/1 30/09/2017 29/09/2021
1938087 Studentship ST/R505031/1 17/09/2017 05/12/2020 Teo Bloch