论文标题
使用全盘SOHO图像对DST风暴的概率预测一日
Probabilistic prediction of Dst storms one-day-ahead using Full-Disk SoHO Images
论文作者
论文摘要
我们提出了一个新模型，即干扰风暴时间（DST）指数超过-100 nt的概率，交货时间在1到3天之间。 $ DST $提供了有关太阳风中质子和电子引起的环形电流强度的基本信息，并且通常将其用作地磁风暴的代理。该模型是使用使用SOHO图像（MDI，EIT和LASCO）训练的卷积神经网络（CNN）的集合开发的。许多研究人员已经研究了SOHO图像与太阳风之间的关系，但是这些研究尚未明确考虑使用SOHO图像来预测$ DST $指数。 这项工作提出了一种新的方法，可以通过使用定制的类平衡均值误差（CB-MSE）损失函数来训练各个模型并学习最佳的合奏权重迭代。 提出的模型可以预测DST <-100 nt 24小时的概率为0.62，MATTHEWS相关系数（MCC）为0.37。 Guastavino等人的加权TSS和MCC。 （2021）分别为0.68和0.47。还进行了非收到指导的CME期间的额外验证，该验证可产生良好的TSS和MCC得分。
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.
