Solar Wind Magnetosphere Coupling Functions: Practicalities and Physicalities

最近发现了一个数学函数，可以预测在各种近地空间条件下以及磁层和电离层中各种现象下太阳风如何与磁层耦合。 这一发现引起了几个重要的地球物理问题和实际后果。 一种是通过两种不同的方式检查不平衡（快速）向阳面合并的实例来确定绝对向阳面磁合并率（而不是比例）。 一种方法使用电离层尖点的纬度与适当的磁场模型进行比较，另一种方法检查极帽通量在快速增长期间的变化。 这两个太阳风驱动的预测器允许改进地磁活动预测器的发展。 这将使得有可能产生一个更基于物理的，重要的磁性指标，如Kp和AE的操作预测。 全球合并率对行星际磁场（IMF）锥角的敏感性也将得到更充分的探索。部分资金将支持一位年轻的博士后科学家。该项目还将涉及弗吉尼亚理工大学和JHU/APL的科学家之间的合作。 这还将为国防气象卫星方案（DMSP）向一般空间科学界分发粒子降水数据提供部分资金。 DMSP数据，从原始数据到时间-能量谱图，再到各种高级数据抽象，被世界各地的研究人员使用。年轻的科学家是这个由NSF资助的数据分发系统的主要用户。

Recently a mathematical function was discovered that predicts how the solar wind couples to the magnetosphere for a wide variety of near-Earth space conditions and for a wide variety of phenomena in the magnetosphere and ionosphere. Several questions of significant geophysical interest and practical consequence arise from this discovery. One is to determine the absolute dayside magnetic merging rate (instead of a proportionality) by examining instances of unbalanced (rapid) dayside merging in two different ways. One method uses the latitude of the ionospheric cusp compared to appropriate magnetic field models, and the other examines changes in polar cap flux over an interval of rapid growth. These two predictors of solar wind driving allow the development of improved geomagnetic activity predictors. This will make it possible to produce a more physics based, operational predictor of important magnetic indices such as Kp and AE. The sensitivity of the global merging rate to the interplanetary magnetic field (IMF) cone angle will also be more fully explored.Part of this funding will support a young,post-doctoral scientist. The project will also involve a collaboration between scientists at the Virginia Tech and JHU/APL. This will also provide partial funding for the distribution of particle precipitation data from the Defense Meteorological Satellite Program (DMSP) to the general space science community. The DMSP data, from raw data to time-energy spectrograms to various high-level data abstractions, are used by researchers around the world. Young scientists are particularly heavy users of this NSF-funded data distribution system.