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.

利用SuperDARN和DMSP的电离层对流观测以及ACE太阳风观测，将建立一个新的高纬度对流模式。该方法将使用数据内插正交函数(DINEOF)。DINEOF是一种基于数据的技术，它确定了一组有限的基函数及其演化，最好地描述了数据集。利用基函数研究极冠对流与太阳风的相互作用。这项技术使两个电离层数据集能够合并，并将填补数据记录中的空白，而不像目前所做的那样使用面值平均。该方法是严格的，并提供了误差估计。该模型将给出两个半球对流的时空特征，对其他磁层和电离层的研究具有一定的参考价值。它来自一名刚刚开始职业生涯的年轻研究人员，将支持德克萨斯大学达拉斯分校的博士后研究人员。弗吉尼亚理工大学将资助一名研究生。