Next Generation, Physics-Inspired AI for Space Weather Forecasting

Space weather describes the variability of conditions in near-Earth space. One of the primary ways in which space weather can impact society is through the generation of anomalous currents (termed Geomagnetically Induced Currents, or GICs) in power networks and pipelines on the ground. These GICs can accelerate the ageing of systems, or more critically lead to the immediate failure of components such as power transformers. This research will take a leap forward in understanding and predicting when we are at risk of suffering large GICs on the ground.

GICs are driven by rapid changes in the Earth's magnetic field, and there are a range of phenomena in near-Earth space that are responsible, but one of the most important is the magnetospheric substorm. During a substorm, interactions between the magnetic field of the Earth and the incident solar wind results in the transfer of energy. This additional energy is principally stored in plasma and magnetic field energy on the nightside of a planet in a region known as the magnetotail. Energy is stored until the system reaches the limit of stability, at which point the energy is explosively released, again through the process of magnetic reconnection. This leads to observable phenomena such as the aurora. However, this process can have dire space weather consequences, causing extreme ionospheric currents and posing risks to satellites and other infrastructure, yet even our most sophisticated methods struggle to predict when it will occur.

Understanding and forecasting magnetic field variability is a hugely difficult problem when the myriad of sporadic and localised processes at the start of a magnetosphere substorm are poorly understood. One of the fundamental issues is the scale of the system. The processes involved are sporadic and localised, and the domain in which they could operate is huge. The aim of this fellowship is to understand the processes and instabilities by which the magnetosphere becomes unstable, and use this to generate cutting-edge, physics-inspired space weather forecasting models.

I will accurately and robustly process huge volumes of data from several missions at the Earth using 'big data' techniques to characterize and predict the conditions under which the substorm is likely to occur. I will develop Bayesian Monte Carlo methods to estimate their spatial and temporal scales and determine causality. I will then use this understanding to generate cutting-edge machine learning models of when and where substorms will occur, as well as the properties and location of the auroral oval. I will then put this together to create a physics-inspired model of forecasting geomagnetic perturbations. This is necessary to provide precise and reliable predictions of when regions are at risk of dangerous GICs. The physics-inspired process will ensure that the model extrapolations to extreme conditions are more reliable than 'black box' extrapolations.

During the course of this fellowship I will collaborate with world leading experts on plasma stability (MSSL) and magnetotail dynamics (Michigan), utilizing cutting edge global models (Michigan) to inform state-of-the-art machine learning models. I will then create robust and reliable models for the benefit of stakeholders (Met Office).