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.
Bashi is currently serving as an assistant research professor in the Institute for the Study of Earth, Oceans, and Space (EOS) at the University of New Hampshire. Her current research focuses on the understanding of Magnetosphere-Ionosphere-Thermosphere Coupling using first principles-based modeling approaches and data-driven methods.
Bashi holds a bachelor’s degree in Physics from the Azad University in Iran, and has also received a Master’s degree in High-Energy Physics from the Southern Methodist University (SMU) in Texas. She obtained her PhD degree in the Department of Space Plasma Physics from the University of New Hampshire in 2017. She was advised by Prof. Joachim Raeder, and her thesis was on the Study of Magnetotail Dynamics and their Ionospheric Signatures Using the global model OpenGGCM.