High-Resolution Empirical Reconstruction of the Geomagnetic Field as a Space Weather Research Tool

作为空间天气研究工具的地磁场高分辨率经验重建

• 批准号: 1157463
• 负责人: Mikhail Sitnov
• 金额: $ 30.19万
• 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: 2013
• 资助国家: 美国
• 起止时间: 2013-08-01 至 2016-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1157463&HistoricalAwards=false
• 关键词: Resolution; Empirical; Reconstruction; Geomagnetic; Field

Data-based modeling and forecasting of the geomagnetic field is a key element of space weather research. Storm and substorm perturbations of the Earth's main magnetic field are among the main indicators of space weather disturbances, and they also help assess other effects such as particle precipitation and plasma heating. A dramatic increase in the amount of data for the terrestrial magnetosphere has made possible a new generation of empirical geomagnetic field models with much larger numbers of degrees of freedom and sophisticated data-binning techniques. A set of the corresponding data-binning, data-fitting and visualization procedures known as the TS07D model is available and widely used in magnetospheric applications. In the project the TS07D model will be improved in three critical aspects. First, the data-binning process will be optimized to achieve a balance between the number of system-level parameters, such as the solar wind electric field, and their forecasting accuracy. As a part of this task the data-binning method will be tested as a pattern-recognition tool, which helps reveal new classes of magnetic storms such as the storms that include saw tooth events. Second, the model output in the form of the effective hot plasma pressure inferred by assuming static force balance for an isotropic plasma will be investigated. The inferred pressure can be used in global MHD models to improve the plasma equation of state and to describe the ring current buildup and decay. It can also be used for the improvement of external boundary conditions in kinetic ring current models, as well as for direct comparison with global plasma patterns retrieved from the energetic neutral atom imaging of the inner magnetosphere. Third, a modification of the field-aligned current part of the model will be studied to transform it into a flexible set of current modules whose foot-point distributions are consistent with low-altitude observations. The model will also be rewritten as a single-language code assuming different platforms and multi-thread operations on computer clusters. The results of this investigation will be an improved magnetospheric magnetic field model. While direct research with the model described above will be valuable, the model will have an even greater value. Magnetospheric magnetic field models are widely used in other applications in magnetospheric physics by other researchers. Since phenomena occurring in different parts of the magnetosphere and the ionosphere are coupled by the magnetic field the model can be used to map phenomena between these two regions. The models can be used to determine the trajectories of charged particles. Finally they can be used to give an investigator an estimate of the magnetic fields in regions where they do not have observations.

基于数据的地磁场建模和预报是空间天气研究的一个关键要素。地球主磁场的风暴和亚风暴扰动是空间天气扰动的主要指标之一，它们还有助于评估粒子沉降和等离子体加热等其他影响。地球磁层数据量的急剧增加使得新一代经验地磁场模型成为可能，这些模型具有更大的自由度和复杂的数据合并技术。一套相应的数据分箱、数据拟合和可视化程序，称为TS07D模型，可在磁层应用中广泛使用。在本项目中，TS07D模型将在三个关键方面进行改进。首先，将优化数据分箱过程，以实现系统级参数（如太阳风电场）的数量与其预测精度之间的平衡。作为这项任务的一部分，数据分箱方法将作为模式识别工具进行测试，这有助于揭示新的磁暴类别，例如包括锯齿事件的磁暴。其次，模型输出的有效热等离子体压力的形式推断，假设静力平衡的各向同性等离子体将进行调查。在全球MHD模型中，可以使用推断的压力来改进等离子体状态方程，并描述环电流的建立和衰减。它也可以用于改进动力学环电流模型的外部边界条件，以及直接比较与全球等离子体模式检索从高能中性原子成像的内磁层。第三，将研究对模型的场向电流部分进行修改，以将其转换为一组灵活的电流模块，其足点分布与低空观测一致。该模型也将被重写为一个单一的语言代码，假设不同的平台和多线程操作的计算机集群。这一研究结果将是一个改进的磁层磁场模型。虽然直接研究上述模型将是有价值的，但该模型将具有更大的价值。磁层磁场模型被其他研究人员广泛应用于磁层物理学的其他应用中。由于磁层和电离层的不同部分发生的现象是由磁场耦合的，该模型可用于映射这两个区域之间的现象。该模型可用于确定带电粒子的轨迹。最后，它们可以用来给研究人员一个估计的磁场在地区，他们没有观测。