Space Weather Forecasts of Ground Level Space Weather with Machine Learning: Performance, Limitations and Challenges

Space weather describes the dynamic conditions in near-Earth space, mostly driven by the variable interaction between the continuous flow of the solar wind and the Earth’s magnetic field.  Extreme space weather has the potential to disrupt or damage key infrastructure on which we rely, for example through the generation of large, anomalous Geomagnetically Induced Currents (GICs) in power networks and transformers.  Accurately forecasting a risk of large GICs would enable key actions to be taken to mitigate their impact.

Given the sparsity of direct GIC measurements, and their inherent specificity to the contemporaneous network properties and configuration, we turn to forecasting the driving factor: the changing ground magnetic field (R).  In this talk we discuss a recent model developed to forecast whether the rate of change of the ground magnetic field (R) will exceed specific, high thresholds in the United Kingdom.  The model uses a common space weather forecasting framework: an interval of data from the upstream solar wind is used to make a prediction as to future conditions at the Earth.  We will use this model as an example to discuss forecasting performance, particularly with respect to different magnetospheric driving and processes.  We demonstrate the use of techniques such as SHAP (Shapley Additive exPlanations) to investigate how and why the model is making the predictions that it does.  What physical processes can this model set up capture?  Where do we need to go in the future?