论文标题

来自太阳冠状孔的地磁风暴预测

Geomagnetic storm forecasting from solar coronal holes

论文作者

Nitti, Simona, Podladchikova, Tatiana, Hofmeister, Stefan J., Veronig, Astrid M., Verbanac, Giuliana, Bandić, Mario

论文摘要

冠状孔(CHS)是太阳风中高速流(HSS)的来源,其与缓慢的太阳风相互作用会在地球层中产生旋转相互作用区域(CIR)。每当CIR击中地球时,它们都会造成地磁风暴。我们开发了一种方法,可以直接使用CH区域和相关磁场极性来预测CIR/HSS驱动的地磁风暴的强度。首先,我们构建了一个数据集,该数据集包括CHS在太阳,相关的HSSS,CIRS和L1处的星际磁场(IMF)的方向以及通过Geomagnetic Indices DST和KP的相关地磁风暴的强度。然后,我们使用高斯工艺模型预测DST和KP指数,该模型解释了地球磁场轴方向的年变化。我们证明,与其CH源的极性相比,与CIRS相关的IMF的极性保留在约83%的病例中。在2010 - 2020年期间测试我们的模型,我们获得了r = 0.63/0.73的预测和观察到的DST指数,而HSSS的kP指数为r = 0.65/0.67,对于朝着/远离太阳的HSS。这些发现证明了直接从太阳能观测值中预测CIR/HSS驱动的地磁风暴的可能性,并将预测的交货时间延长至几天,这与增强空间天气预测有关。

Coronal holes (CHs) are the source of high-speed streams (HSSs) in the solar wind, whose interaction with the slow solar wind creates corotating interaction regions (CIRs) in the heliosphere. Whenever the CIRs hit the Earth, they can cause geomagnetic storms. We develop a method to predict the strength of CIR/HSS-driven geomagnetic storms directly from solar observations using the CH areas and associated magnetic field polarity. First, we build a dataset comprising the properties of CHs on the Sun, the associated HSSs, CIRs, and orientation of the interplanetary magnetic field (IMF) at L1, and the strength of the associated geomagnetic storms by the geomagnetic indices Dst and Kp. Then, we predict the Dst and Kp indices using a Gaussian Process model, which accounts for the annual variation of the orientation of Earth's magnetic field axis. We demonstrate that the polarity of the IMF at L1 associated with CIRs is preserved in around 83% of cases when compared to the polarity of their CH sources. Testing our model over the period 2010-2020, we obtained a correlation coefficient between the predicted and observed Dst index of R = 0.63/0.73, and Kp index of R = 0.65/0.67, for HSSs having a polarity towards/away from the Sun. These findings demonstrate the possibility of predicting CIR/HSS-driven geomagnetic storms directly from solar observations and extending the forecasting lead time up to several days, which is relevant for enhancing space weather predictions.

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