UNIVERSITY OF ALBERTA (CA)
Electromagnetic Ion Cyclotron (EMIC) waves are electromagnetic plasma waves in near-Earth space, which play an important role in outer radiation belt dynamics. Specifically, these waves remove electrons from the outer radiation belt by scattering them into the Earth’s atmosphere. Energetic so-called “killer” electrons trapped in the outer belt pose dangers to satellites. Understanding the processes responsible for the loss of such “killer electrons” is therefore important for developing improved space weather forecasts, and for determining when harsh space radiation returns to more benign levels. Ultimately a better understanding of the dynamics of space radiation leads to improved protections for satellites from adverse effects through appropriate shielding design and on-orbit operations procedures. It is therefore important for radiation belt models to include EMIC wave driven loss. However, EMIC waves can be highly localized in both space and time and are therefore difficult to detect using data from typical slow-moving high altitude magnetospheric satellites, which are often in the wrong place at the wrong time to observe the wave. EMIC waves can be observed by ground magnetometer arrays, but these observations have their own problems, as EMIC waves observed from the ground undergo various ducting effects, meaning that a wave observed on the ground often does not match the location of the wave in space. Since it is challenging to accurately know when and where EMIC wave loss processes should be applied, a number of radiation belt simulations invoke EMIC wave scattering without direct observational evidence.
The constellation of Swarm satellites in Low-Earth Orbit (LEO) can observe EMIC waves much more frequently than magnetospheric satellites, due to their shorter orbital period. The challenge with using Swarm is distinguishing EMIC waves from nearby field-aligned currents, which can look electromagnetically very similar from LEO. This project builds upon previous work to use a range of techniques, including multi-point observations and frequency domain analysis, to solve this problem. This will make it possible to generate a large database of EMIC waves observed by Swarm. This database will be cross-referenced with observations of energetic electron precipitation (EEP), making it possible to quantify exactly the character of EMIC wave-related radiation belt loss. This has significant implications for improved radiation belt models, and for assessing the effects of electron precipitation on upper atmospheric chemistry and climate. On an as time-allows basis, we will also investigate using machine learning (ML), to be trained and validated on the EMIC wave database and EEP data, for EMIC wave detection and assessing their impacts for EEP. This may make it possible to scale the statistics to >11 years of Swarm observations, covering an entire solar cycle. At the end of the work, the project aims to comprehensively characterize and quantify the link between EMIC waves and EEP, and to better characterize the EMIC waves themselves. This will fill a major capability gap in radiation belt modelling and provide the possibility of near-real time EMIC wave forecasting, opening an important new capability for Swarm to be used as a space weather monitor.
