预测到达地球的日冕物质抛射内的磁矢量：1。初始架构
Predicting the magnetic vectors within coronal mass ejections arriving at Earth: 1. Initial Architecture
摘要：
太阳在短时间尺度上影响地球环境的过程主要是由太阳风和地球磁层之间的磁重联量驱动的。当太阳风磁场有一个向南的分量时，重联发生得最有效。最严重的撞击发生在日冕物质抛射（CME）到来的时候，这时磁层既受到压缩，又与日光层环境磁连接。不幸的是，预测日冕物质抛射内的磁场矢量仍然是难以捉摸的。在这里，我们报告如何，通过结合一个统计上强大的螺旋规则的CME的太阳起源与简化的磁通绳拓扑结构的磁矢量内的地球定向段的CME可以预测。为了测试这种概念验证架构在CME内估计磁矢量的有效性，共研究了8个CME事件（2010年至2014年）。这项工作的重点是2014年1月的大假警报，强调了将CME的早期演化效应纳入预测目的的重要性。预测的磁场中的角旋转紧密遵循原位数据中看到的广泛旋转结构。这种随时间变化的字段估计实施到一个过程中，定量预测随时间变化的Kp指数，详细描述在文件II。未来的统计工作，量化这一过程中的不确定性，可能会改善早期预测系统使用的更启发式的方法。
英文摘要：
The process by which the Sun affects the terrestrial environment on short timescales is predominately driven by the amount of magnetic reconnection between the solar wind and Earth's magnetosphere. Reconnection occurs most efficiently when the solar wind magnetic field has a southward component. The most severe impacts are during the arrival of a coronal mass ejection (CME) when the magnetosphere is both compressed and magnetically connected to the heliospheric environment. Unfortunately, forecasting magnetic vectors within coronal mass ejections remains elusive. Here we report how, by combining a statistically robust helicity rule for a CME's solar origin with a simplified flux rope topology the magnetic vectors within the Earth-directed segment of a CME can be predicted. In order to test the validity of this proof-of-concept architecture for estimating the magnetic vectors within CMEs, a total of eight CME events (between 2010 and 2014) have been investigated. With a focus on the large false alarm of January 2014, this work highlights the importance of including the early evolutionary effects of a CME for forecasting purposes. The angular rotation in the predicted magnetic field closely follows the broad rotational structure seen within the in situ data. This time-varying field estimate is implemented into a process to quantitatively predict a time-varying Kp index that is described in detail in paper II. Future statistical work, quantifying the uncertainties in this process, may improve the more heuristic approach used by early forecasting systems.
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