Multi-Hour-Ahead Dst Index Prediction Using Multi-Fidelity Boosted Neural Networks

The Disturbance storm time (Dst) index has been widely used as a proxy for the ring current intensity, and therefore as a measure of geomagnetic activity. It is derived by measurements from four ground magnetometers in the geomagnetic equatorial region. We present a new model for predicting Dst with a lead time between 1 and 6 hr. The model is first developed using a Gated Recurrent Unit (GRU) network that is trained using solar wind parameters. The uncertainty of the Dst model is then estimated by using the Accurate and Reliable Uncertainty Estimate method (Camporeale & Care, 2021, https://doi.org/10.1615/ int.j.uncertaintyquantification.2021034623). Finally, a multi-fidelity boosting method is developed in order to enhance the accuracy of the model and reduce its associated uncertainty. It is shown that the developed model can predict Dst 6 hr ahead with a root-mean-square-error of 13.54 nT. This is significantly better than a persistence model or a single GRU model.Plain Language Summary Geomagnetic storms pose one of the most severe space weather risks to our space-borne and ground-based electronic instruments, such as global navigation satellite systems and radio transmission systems. The Disturbance storm time (Dst) is one of the most used geomagnetic storm indicators. This study presents an innovative multi-fidelity boosted neural network method to forecast Dst 1-to-6 hours ahead. The new method improves the performance of the predictions by estimating their uncertainties.