Gap Filling of Solar Wind Data by Singular Spectrum Analysis

太阳风是地球上空间天气现象的主要驱动力。不幸的是，对太阳风的观测受到数据空白的困扰。磁层和电离层动力学的建模通常需要太阳风密度、速度和行星际磁场（IMF）的时间连续信息。该项目将利用奇异光谱分析（SSA）技术，通过使用各种地面观测数据来填补太阳风数据的空白，这些地面观测数据是连续的。SSA分析将应用于1963年至今的历史太阳风资料。所得数据集将用于辐射带的研究。该数据集还将通过虚拟辐射带天文台（ViRBO）提供给研究界。将产生的连续太阳风和国际货币基金组织数据将对各种有关空间天气的研究项目有用，因此这项工作将具有社会效益。通过研究生的参与，该项目还具有教育效益。

The solar wind is the primary driver for space weather phenomena at the Earth. Unfortunately, the observations of the solar wind are plagued by gaps in the data. Modeling of the dynamics of the magnetosphere and ionosphere generally require time-continuous information about the solar wind density, velocity and interplanetary magnetic field (IMF). This project will utilize singular spectrum analysis (SSA) techniques to fill in the gaps in the solar wind data by using data from a variety of ground-based observations where continuous data are available. The SSA analysis will be applied to the historical solar wind data from 1963 to the present.The resulting dataset will be used for studies of the radiaton belts. The dataset will also be made available to the research community through the Virtual Radiation Belt Observatory (ViRBO). The continuous solar wind and IMF data that will be produced will be useful in a variety of research projects concerning space weather and the work therefore will have societal benefits. The project also has educational benefits through the participation of a graduate student.