Global Geomagnetic Perturbation Forecasting Using Deep Learning

Geomagnetically Induced Currents (GICs) arise from spatiotemporal changes to Earth's magnetic field, which occur from the interaction of the solar wind with Earth's magnetosphere. They drive catastrophic destruction to our technologically dependent society. Hence, computational models to forecast GICs globally with large forecast horizons, high spatial resolution, and temporal cadence are increasingly essential to perform prompt mitigation. Since GIC data is proprietary, the time variability of the horizontal component of the magnetic field perturbation (dB/dt) is used as a proxy for GICs. In this work, we develop a fast, global dB/dt forecasting model, which forecasts 30 min into the future using only solar wind measurements as input. The model summarizes 2 hr of solar wind measurement using a Gated Recurrent Unit and generates forecasts of coefficients folded with a spherical harmonic basis to enable global forecasts. When deployed, our model produces results in under a second and generates global forecasts for horizontal magnetic perturbation components at 1 min cadence. We evaluate our model across models in literature for two specific storms of 5 August 2011 and 17 March 2015 while having a self-consistent benchmark model set. Our model outperforms or has consistent performance with state-of-the-practice high-time cadence local and low-time cadence global models while also outperforming/having comparable performance with the benchmark models. Such quick inferences at high temporal cadence and arbitrary spatial resolutions may ultimately enable accurate forewarning of dB/dt for any place on Earth, resulting in preventive measures to be taken in an informed manner.