Among various factors affecting the space weather effects of a coronal mass ejection (CME), its propagation trajectory in the interplanetary space is an important one determining whether and when the CME will hit the Earth. Many direct observations have revealed that a CME may not propagate along a straight trajectory in the corona, but whether or not a CME also experiences a deflected propagation in the interplanetary space is a question, which has never been fully answered. Here by investigating the propagation process of an isolated CME from the corona to interplanetary space during 12–19 September 2008, we present solid evidence that the CME was deflected not only in the corona but also in the interplanetary space. The deflection angle in the interplanetary space is more than 20° toward the west, resulting a significant change in the probability the CME encounters the Earth. A further modeling and simulation‐based analysis suggests that the cause of the deflection in the interplanetary space is the interaction between the CME and the solar wind, which is different from that happening in the corona.

Coronal mass ejections (CMEs) are arguably the most violent eruptions in the solar system. CMEs can cause severe disturbances in interplanetary space and can even affect human activities in many aspects, causing damage to infrastructure and loss of revenue. Fast and accurate prediction of CME arrival time is vital to minimize the disruption that CMEs may cause when interacting with geospace. In this paper, we propose a new approach for partial-/full halo CME Arrival Time Prediction Using Machine learning Algorithms (CAT-PUMA). Via detailed analysis of the CME features and solar-wind parameters, we build a prediction engine taking advantage of 182 previously observed geo-effective partial-/full halo CMEs and using algorithms of the Support Vector Machine. We demonstrate that CAT-PUMA is accurate and fast. In particular, predictions made after applying CAT-PUMA to a test set unknown to the engine show a mean absolute prediction error of ∼5.9 hr within the CME arrival time, with 54% of the predictions having absolute errors less than 5.9 hr. Comparisons with other models reveal that CAT-PUMA has a more accurate prediction for 77% of the events investigated that can be carried out very quickly, i.e., within minutes of providing the necessary input parameters of a CME. A practical guide containing the CAT-PUMA engine and the source code of two examples are available in the Appendix, allowing the community to perform their own applications for prediction using CAT-PUMA.

Stream interaction regions (SIRs) are important sources of geomagnetic storms. In this work, we first extend the end time of the widely used SIR catalog developed by Jian et al. (2006, https://doi.org/10.1007/s11207-006-0132-3), which covered the period from 1995 to 2009, to the end of 2016. Based on this extended SIR catalog, the geoeffectiveness of SIRs is discussed in detail. It was found that 52% of the SIRs caused geomagnetic storms with Dst_min ≤−30 nT, but only 3% of them caused intense geomagnetic storms with Dst_min ≤−100 nT. Furthermore, we found that 10 of the intense geomagnetic storms caused by SIRs were associated with complex structures due to interactions between SIRs and interplanetary coronal mass ejections (ICMEs). In such a structure, an ICME is embedded in the SIR and located between the slow and fast solar wind streams. In addition, we found that the geoeffectiveness of SIRs interacting with ICMEs is enhanced. The possibility of SIR‐ICME interaction structures causing geomagnetic storms is markedly higher than that of isolated SIRs or isolated ICMEs. In particular, the geoeffectiveness of SIR‐ICME interaction structures is similar to that of the Shock‐ICME interaction structures, which have been demonstrated to be the main causes of geomagnetic storms.