# 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…

## One Citation

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