Global Geomagnetic Perturbation Forecasting Using Deep Learning

@article{Upendran2022GlobalGP,
  title={Global Geomagnetic Perturbation Forecasting Using Deep Learning},
  author={Vishal Upendran and Panagiotis Tigas and Banafsheh Ferdousi and T{\'e}o Bloch and Mark C. M. Cheung and Siddha Ganju and Asti Bhatt and Ryan Mcgranaghan and Yarin Gal},
  journal={Space Weather},
  year={2022},
  volume={20}
}
Geomagnetically Induced Currents (GICs) arise from spatio‐temporal changes to Earth's magnetic field, which arise from the interaction of the solar wind with Earth's magnetosphere, and drive catastrophic destruction to our technologically dependent society. Hence, computational models to forecast GICs globally with large forecast horizon, high spatial resolution and temporal cadence are of increasing importance to perform prompt necessary mitigation. Since GIC data is proprietary, the time… 
On the Considerations of Using Near Real Time Data for Space Weather Hazard Forecasting
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