Advancing Extreme Event Scenario Simulations Using Realistic, Self-Consistent Solar Wind Drivers

Understanding extreme space weather scenarios and their resulting ground magnetic disturbances (GMD) is a critical step towards building resiliency in power grids to geomagnetically induced currents (GIC). The fundamental challenge, however, is obtaining a set of solar wind and interplanetary magnetic field (IMF) time series that is both realistic and self-consistent between variables. Such a data set can be used to drive physics-based models and understand how extreme storms produce GMD across the globe. Previous works have leveraged idealized input time series or scaled up observed strong storm events to elicit a stronger ground response. Though important first steps, these scenarios produce solar wind values that are not physically consistent across the magnetohydrodynamic state variables (density, thermal pressure, velocity, and magnetic field) and represent combinations that may not be possible. This introduces additional uncertainty on estimates of extreme GMD.

In this work, we present GMD values from numerical simulations driven by a new, self-consistently-generated set of extreme solar wind and IMF conditions. The new dataset uses machine-learning-based regression techniques trained on a superposed epoch storm database of coronal mass ejection storms. Using the relationships between the MHD state variables of each storm, we can specify an extreme time series for one variable (e.g., Earthward velocity), and obtain self-consistent time series for all other variables. The resulting extreme specification is used to drive the Space Weather Modeling Framework (SWMF) configured for geospace. The SWMF simulates the geospace response, including ground magnetic disturbances, by coupling the BATS-R-US global MHD model, the Ridley Ionosphere Model (RIM), and the Rice Convection Model (RCM) of the ring current. The resulting GMD predictions are compared to non-extreme values, estimates of extremes from statistical analysis, and estimates from other extreme simulations.