Collaborative Research: Inferring High Latitude Convection Patterns Using SuperDARN, DMSP and ACE

合作研究：使用 SuperDARN、DMSP 和 ACE 推断高纬度对流模式

• 批准号: 1259508
• 负责人: Russell Stoneback
• 金额: $ 31.46万
• 依托单位: University of Texas at Dallas
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-02-01 至 2018-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1259508&HistoricalAwards=false
• 关键词: Collaborative; Research; Inferring; Latitude; Convection

Observations of ionospheric convection from SuperDARN and DMSP along with ACE solar wind observations will be used to develop a new high-latitude convection model. The approach will be to use Data Interpolating Orthogonal Functions (DINEOFs). DINEOFs are a data-based technique that determines a limited set of basis functions and their evolution that best describes a data set. The basis functions will be used to study the interaction between polar cap convection and the solar wind. The technique allows the two ionospheric data sets to be combined and will fill gaps in the data record without using bin-averaging as is presently done. The approach is rigorous and provides error estimates. The resulting model will provide spatial and temporal characteristics of the convection for both hemispheres.This model will be valuable to other magnetospheric and ionospheric researchers. It is from a young researcher just starting his career and will support a post-doctoral researcher at the University of Texas at Dallas. A graduate student will be supported at Virginia Tech.

从SuperDARN和DMSP以及ACE太阳风观测结果的电离层对流观测结果将用于开发新的高纬度对流模型。该方法将是使用数据插值正交函数（DINEOFS）。 DINEOFS是一种基于数据的技术，它决定了有限的基集功能及其最能描述数据集的演变。基础函数将用于研究极盖对流与太阳风之间的相互作用。 该技术允许将两个电离层数据集组合在一起，并将填补数据记录中的空白，而无需按照目前的方式使用bin平衡。该方法是严格的，并提供了错误估计。最终的模型将为两个半球提供对流的空间和时间特征。该模型将对其他磁层和电离层研究人员很有价值。这是一位年轻的研究人员刚刚开始他的职业生涯，并将支持德克萨斯大学达拉斯分校的博士后研究员。研究生将得到弗吉尼亚理工大学的支持。