The Role of Electric Fields in Space Weather

研究磁层对太阳风动态强迫的响应，对于预测这种驱动对空间和地球技术系统的影响特别有意义。在这方面，一个特别不好诊断的参数是由磁场重联驱动的电场。虽然我们了解的基本过程，我们仍然无知的影响，增强电场在磁层的所有领域，例如提供种子人口的其他过程，以创造更高的能量粒子，创造影响航天器轨道的密度变化等。该项目将主要侧重于分析各种不同系统从空间和地面测量的电场测量结果，并将涉及使用数值模拟来提高我们的预测能力。数据分析将基于以下方面的现有数据集：（一）欧空局的SWARM使命;（二）Cluster使命;（三）SuperDARN数据集，该数据集由来自北方半球多个观测站的数据组成。虽然存在几种数据分析方法来产生全球速度场，因此电场，学生将研究最合适的方法来汇集来自磁层不同位置的这些不同项目的数据。这将需要（一）确定选择感兴趣区间的标准;（二）利用这些标准确定这些区间;（三）确定每一数据集最适当的数据产品格式。随后的建模将包括一组试运行，以研究模型对电场变化的敏感性，包括时间和空间。

The investigation of the response of the magnetosphere to the dynamic forcing by the solar wind is of particular interest in forecasting the impact of this driving on technological systems, both space and earth based. A particularly poorly diagnosed parameter in this respect is the electric field which is driven by magnetic reconnection. While we understand the fundamental processes we remain ignorant of the impact of enhanced electric fields in all areas of the magnetospheres, for example providing seed populations for other processes to create higher energy particle, creating changes in the density which affect spacecraft orbits, etc. The project will investigate the impact of the electric field measured in the ionosphere on technological system. The project will primarily focus on the analysis of measurements of electric fields measured by a variety of different systems from both space and ground and will also touch upon the use of numerical simulation in terms of improving our predictive capability. The data analysis will be based on existing data sets from (i) the ESA mission SWARM; (ii) the Cluster mission) and finally (iii) SuperDARN data set which consists of data from multiple observatories in the northern hemisphere. Although several data analysis methods exist to produce the global velocity field, and hence electric field, the student will investigate the most appropriate ways to bring together data from these different projects which come from different locations in the magnetosphere. This will require (i) determining the criteria for selecting intervals of interest; (ii) using these criteria to identify those intervals; (iii) and determining the format of the data products most appropriate from each data set. The subsequent modeling will consist of a set of trial runs to investigate the sensitivity of the model to the electric field variations, both in time and space.