Understanding the Geospace Phenomena Connected to Localized Perturbations in Earth’s Magnetic Field

了解与地球磁场局部扰动相关的地球空间现象

• 批准号: 2331527
• 负责人: Amy Keesee
• 金额: $ 57.77万
• 依托单位: University of New Hampshire
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-03-01 至 2027-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2331527&HistoricalAwards=false
• 关键词: Understanding; Geospace; Phenomena; Connected; Localized

During intervals of increased geomagnetic activity, increased currents in geospace can induce currents in the ground or in long, manmade conductors, such as power lines. These geomagnetically induced currents (GICs) can drive power outages and damage power components while also affecting pipelines and train systems. Developing the ability to predict GICs is important to protecting infrastructure and limiting the impact of geomagnetic storms on public safety and the economy. This project addresses GIC prediction by seeking to understand the connection between localized temporal changes in magnetic field measurements (dB/dt) and the magnetospheric and ionospheric phenomena causing them. This work will support the training of two graduate students that will be prepared to enter the STEM workforce with knowledge of space science, space weather, and machine learning and support the career of a woman PI leading the project. This project is jointly funded by the Magnetospheric Physics program, the Established Program to Stimulate Competitive Research (EPSCoR), and the Aeronomy program.Several studies have shown that peaks in dB/dt can be very localized, on the scales of hundreds of km. Thus, forecasting is needed at a localized level to provide power companies with actionable warnings. The current physics-based models used by the Space Weather Prediction Center lack the resolution needed to include physical phenomena at the spatial scales of the dB/dt peaks. Higher resolution models are being used for scientific studies, but are computationally expensive and take longer to run, making them more challenging to use for timely forecasting. An advantage of machine learning (ML) models is that once trained, the runtime to make predictions is significantly lower than physics based direct modeling. ML networks’ ability to model nonlinear relationships in datasets can allow us to better understand the phenomena that result in localized dB/dt through the use of model explainability techniques. The following science questions will be addressed: (1) What are the spatial characteristics of localized magnetic perturbations? (2) What magnetosphere and ionosphere phenomena correlate with localized magnetic perturbations and why? The first question will be tackled using two methods that take advantage of the NSF-funded SuperMAG database: by characterizing the Region to Specific Difference (a parameter that compares the value at one location to the average values within a defined region) and by interpolation with spherical elementary current systems, and then comparing the results. The second question will utilize explainable machine learning techniques to explore magnetosphere and ionosphere phenomena that correlate with the spatially localized perturbations, incorporating datasets from satellites (e.g., DMSP, AMPERE, TWINS, and THEMIS). The results of this work will improve our understanding of geomagnetically active intervals, magnetosphere-ionosphere coupling, and the causes of localized perturbations in the ground magnetic field.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在地磁活动增加的间隔期间，地球空间中增加的电流可以在地面或长人造导体（如电力线）中感应电流。这些地磁感应电流（gic）可能导致停电和损坏电力元件，同时也会影响管道和火车系统。发展预测地球磁暴的能力对于保护基础设施和限制地磁风暴对公共安全和经济的影响非常重要。该项目通过寻求理解磁场测量的局部时间变化（dB/dt）与引起它们的磁层和电离层现象之间的联系来解决GIC预测。这项工作将支持培训两名研究生，他们将准备进入具有空间科学，空间天气和机器学习知识的STEM劳动力，并支持领导该项目的女性PI的职业生涯。该项目由磁层物理计划、刺激竞争研究的既定计划（EPSCoR）和航空计划共同资助。几项研究表明，dB/dt的峰值可能是非常局部的，在数百公里的尺度上。因此，需要在局部水平上进行预测，以便为电力公司提供可操作的警告。目前空间天气预报中心使用的基于物理的模型缺乏包括dB/dt峰值空间尺度上的物理现象所需的分辨率。更高分辨率的模型正被用于科学研究，但计算成本高，运行时间长，这使得它们更难以用于及时预测。机器学习（ML）模型的一个优势是，一旦经过训练，进行预测的运行时间明显低于基于物理的直接建模。机器学习网络在数据集中建模非线性关系的能力，可以让我们通过使用模型可解释性技术，更好地理解导致局部dB/dt的现象。以下科学问题将被解决：(1)局域磁扰动的空间特征是什么？(2)什么磁层和电离层现象与局部磁扰动相关？为什么？第一个问题将使用两种方法来解决，这两种方法利用了nsf资助的SuperMAG数据库：通过表征区域比差（将一个位置的值与定义区域内的平均值进行比较的参数）和球面初等电流系统的插值，然后比较结果。第二个问题将利用可解释的机器学习技术来探索与空间局部扰动相关的磁层和电离层现象，并结合来自卫星的数据集（例如，DMSP， AMPERE， TWINS和THEMIS）。这项工作的结果将提高我们对地磁活动间隔、磁层-电离层耦合以及地磁场局部扰动原因的理解。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。