The role of small-scale coronal eruptive phenomena in the generation and heating of the solar wind remains an open question. Here, we investigate the role played by coronal jets in forming the solar wind by testing whether temporal variations associated with jetting in EUV intensity can be identified in the outflowing solar-wind plasma. This type of comparison is challenging due to inherent differences between remote-sensing observations of the source and in-situ observations of the outflowing plasma, as well as travel time and evolution of the solar wind throughout the heliosphere. To overcome these, we propose a novel algorithm combining signal filtering, two-step solar-wind ballistic back-mapping, window shifting, and Empirical Mode Decomposition. We first validate the method using synthetic data, before applying it to measurements from the Solar Dynamics Observatory and Wind spacecraft. The algorithm enables the direct comparison of remote-sensing observations of eruptive phenomena in the corona to in-situ measurements of solar-wind parameters, among other potential uses. After application to these datasets, we find several time windows where signatures of dynamics found in the corona are embedded in the solar-wind stream, at a time significantly earlier than expected from simple ballistic back-mapping, with the best-performing in-situ parameter being the solar-wind mass flux.

小规模日冕爆发现象在太阳风的产生和加热中的作用仍然是一个悬而未决的问题。在这里，我们通过测试是否可以在流出的太阳风等离子体中识别出与EUV强度喷射相关的时间变化，来研究日冕射流在形成太阳风中所起的作用。由于源的遥感观测与流出的等离子体的原地观测之间的固有差异，以及整个太阳圈的传播时间和太阳风的演变，这种类型的比较具有挑战性。为了克服这些问题，我们提出了一种新颖的算法，该算法结合了信号滤波，两步太阳风弹道反映射，窗口平移和经验模态分解。我们首先使用综合数据验证该方法，然后再将其应用于来自太阳动力学天文台和风力航天器。该算法可以将日冕中喷发现象的遥感观测结果与太阳风参数的原位测量结果进行直接比较，以及其他潜在用途。应用到这些数据集后，我们发现几个时间窗口，其中电晕中发现的动力学特征被嵌入到太阳风流中，而这个时间明显比简单弹道反制所期望的时间要早​​，并且具有最佳的原位表现。参数是太阳风质量通量。