Probabilistic Prediction of Dst Storms One‐Day‐Ahead Using Full‐Disk SoHO Images

@article{Hu2022ProbabilisticPO,
  title={Probabilistic Prediction of Dst Storms One‐Day‐Ahead Using Full‐Disk SoHO Images},
  author={Anna Hu and Carl Shneider and Animesh Tiwari and Enrico Camporeale},
  journal={Space Weather},
  year={2022},
  volume={20}
}
We present a new model for the probability that the disturbance storm time (Dst) index exceeds −100 nT, with a lead time between 1 and 3 days. Dst provides essential information about the strength of the ring current around the Earth caused by the protons and electrons from the solar wind, and it is routinely used as a proxy for geomagnetic storms. The model is developed using an ensemble of Convolutional Neural Networks that are trained using Solar and Heliospheric Observatory (SoHO) images… 
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