Identification of Underlying Dynamics of the Magnetosphere

磁层潜在动力学的识别

• 批准号: 1058456
• 负责人: Simon Wing
• 金额: $ 36万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1058456&HistoricalAwards=false
• 关键词: Identification; Underlying; Dynamics; Magnetosphere

In order to build useful predictive space weather tools it is necessary to understand the most critical nonlinear dependencies among observed plasma and electromagnetic field variables in the coupled solar wind/magnetosphere system. This project will develop and apply information-theoretical tools such as mutual information, cumulant-based cost, transfer entropy and redundancy as information-dynamical measures of causal dependencies to understand: (1) the underlying dynamical response of the magnetosphere to the solar wind, (2) solar wind-magnetosphere coupling functions, (3) the information horizon and predictability of dynamics in the magnetospheric system, and (4) causal dependencies responsible for the recurrence time of magnetic substorms. This nonparametric, statistical approach assumes no intrinsic underlying dynamics. Information gained from the cumulant-based information flow can be used to guide the development of a variety of space weather tools. As a specific example of the application of the statistical methods, the project will evaluate energetic electron flux at geosynchronous orbit, the stretching of the magnetotail and magnetospheric power flux into the auroral zones. These results will make it possible to estimate a predictability horizon, which will indicate the maximum "look ahead" for which the space weather can be predicted. Information-dynamical measures will also be used to identify causal variables derived from solar wind data that maximize the information content about the magnetospheric response. The methods and techniques that will be used in this project do not presuppose an underlying dynamics and hence they will work across a wide range of areas in the Sun-Earth system. Detection of the most important nonlinear interactions in the systems will be used to identify the most important causal physical processes and will aid development of physical models and predictive capabilities for understanding severe space weather. The project include training of an undergraduate or beginning graduate student on the basic concepts in nonlinear dynamics with applications to space weather.

为了建立有用的空间天气预报工具，有必要了解太阳风/磁层耦合系统中观测到的等离子体和电磁场变量之间最关键的非线性依赖关系。该项目将开发和应用信息理论工具，如互信息、基于累积量的成本、传递熵和冗余度，作为因果依赖关系的信息动力学计量，以了解：(1)磁层对太阳风的基本动力学反应，(2)太阳风-磁层耦合函数，(3)磁层系统动力学的信息范围和可预测性，以及(4)磁亚风暴复发时间的因果依赖关系。这种非参数的统计方法没有假设内在的潜在动力。从基于累积量的信息流中获得的信息可用于指导各种空间气象工具的开发。作为应用统计方法的一个具体例子，该项目将评估地球同步轨道的高能电子通量、磁尾的伸展和进入极光区的磁层功率通量。这些结果将使估计可预测的地平线成为可能，这将表明可以预测的空间天气的最大“展望”。信息动力学措施还将用于确定从太阳风数据中得出的因果变量，从而最大限度地增加关于磁层响应的信息内容。该项目中将使用的方法和技术并不以潜在的动力学为前提，因此它们将在太阳-地球系统的广泛区域中发挥作用。探测系统中最重要的非线性相互作用将被用来确定最重要的因果物理过程，并将有助于开发物理模型和预测能力，以了解恶劣的空间天气。该项目包括对本科生或初级研究生进行关于非线性动力学的基本概念及其在空间气象中的应用的培训。