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: 1258522
• 负责人: Joseph Baker
• 金额: $ 15.03万
• 依托单位: Virginia Polytechnic Institute and State University
• 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=1258522&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.

SuperDARN 和 DMSP 的电离层对流观测以及 ACE 太阳风观测将用于开发新的高纬度对流模型。该方法将使用数据插值正交函数 (DINEOF)。 DINEOF 是一种基于数据的技术，可确定一组有限的基本函数及其最能描述数据集的演化。基函数将用于研究极冠对流与太阳风之间的相互作用。 该技术允许组合两个电离层数据集，并将填补数据记录中的空白，而无需像目前所做的那样使用箱平均。该方法非常严格并提供误差估计。由此产生的模型将提供两个半球对流的空间和时间特征。该模型对于其他磁层和电离层研究人员将很有价值。它来自一位刚刚开始职业生涯的年轻研究员，将支持德克萨斯大学达拉斯分校的一名博士后研究员。弗吉尼亚理工大学将支持一名研究生。