Probabilistic prediction of Dst storms one-day-ahead using Full-Disk SoHO Images

TL;DR

This paper introduces a novel ensemble CNN model trained on SoHO images to probabilistically predict geomagnetic storms (Dst index below -100 nT) one day ahead, achieving competitive skill scores.

Contribution

It develops a new ensemble CNN approach with a custom loss function to improve one-day-ahead geomagnetic storm prediction using solar images.

Findings

Achieved TSS of 0.62 and MCC of 0.37 for storm prediction.

Demonstrated robustness during non-Earth-directed CME periods.

Introduced a novel ensemble weighting method with a custom loss function.

Abstract

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 (CNNs) that are trained using SoHO images (MDI, EIT and LASCO). The relationship between the SoHO images and the solar wind has been investigated by many researchers, but these studies have not explicitly considered using SoHO images to predict the $Dst$ index. This work presents a novel methodology to train the individual models and to learn the optimal ensemble weights iteratively, by using a customized class-balanced mean square error (CB-MSE) loss function tied to a least-squares (LS) based ensemble. The proposed model can predict the probability that Dst<-100 nT 24 hours ahead with a True Skill Statistic (TSS) of 0.62 and Matthews Correlation Coefficient (MCC) of 0.37. The weighted TSS and MCC from Guastavino et al. (2021) is 0.68 and 0.47, respectively. An additional validation during non-Earth-directed CME periods is also conducted which yields a good TSS and MCC score.