Enabling Real-Time Geoelectric Field Forecasts with Machine Learning

Geomagnetically induced currents (GICs) and their effects on the terrestrial power grid are recognized as one of the most potentially critical impacts of space weather. Because GICs are associated with specific grid configurations and characteristics, it is not possible (or at least feasible) to develop a general model that directly links space weather information to GICs. Instead, it is widely recognized that the most useful physical quantity for assessing grid impacts is the geoelectric field that is produced by externally-driven magnetic disturbances and their associated telluric response.

Although geomagnetic field data is available in real time, it is desirable to have warning of potential hazard at least 10 minutes in advance so as to provide stakeholders with the opportunity to take mitigating actions. Towards this end, we have begun the development of a data-driven, machine-learning enabled forecasting system whose ultimate goal is to provide a high-quality, uncertainty-quantified prediction of geoelectric fields. Our system combines modern approaches from non-stationary signal processing, information theory, ensemble forecasting, and feature classification with high-quality geomagnetic data sets and three-dimensional electromagnetic transfer functions. In this talk, I will provide an overview of our modeling effort and how discuss how physical insights have driven the development of our machine learning approach.