Identification of Underlying Dynamics of the Magnetosphere

为了建立有用的预测空间天气工具，有必要了解在耦合的太阳风/磁层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、基于累积的成本、传递熵和冗余作为因果关系的信息动态度量来理解：(1)磁层对太阳风的潜在动力响应；(2)太阳风-磁层耦合函数；(3)磁层系统动力学的信息视界和可预测性；(4)磁亚暴复发时间的因果关系。这种非参数的统计方法假定没有内在的潜在动力。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天气工具的开发。作为统计方法应用的具体实例，该项目将评估地球同步轨道上的高能电子通量、磁尾的拉伸和磁层功率通量进入极光区。这些结果将使估计可预测性视界成为可能，这将表明可以预测空间天气的最大“展望”。信息动力学措施也将用于确定从太阳风数据中得出的因果变量，以最大限度地提高有关磁层响应的信息内容。在这个项目中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-地球系统的广泛领域中工作。系统中最重要的非线性相互作用的检测将用于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研究生的非线性动力学基本概念及其在空间天气中的应用的培训。

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.