RII Track-2 FEC: Collaborative Research: Harnessing Big Data to Improve Understanding and Predictions of Geomagnetically Induced Currents

最近，总统科学和技术办公室建议我们采取措施，使我国的基础设施做好准备，以抵御危险的空间天气影响。地磁感应电流（GIC）是由空间天气事件中的地磁扰动引起的，可导致电力中断、列车系统故障和管道腐蚀。虽然GIC的风险在工业界和空间科学界得到广泛认可，但对GIC的发生模式和空间/地面条件了解甚少，主要是因为电力公司不愿提供其GIC数据，因为可能会对停电和任何其他技术问题产生法律的争议。该项目将利用阿拉斯加大学费尔班克斯和新罕布什尔州大学在空间物理学和数据科学方面的丰富专门知识来了解和预测GIC。该项目特别关注地磁扰动、GIC的触发以及地球空间环境中这种扰动的可能来源。我们将把最先进的机器学习技术应用于20多年的空间/地面观测，并为地磁扰动和GIC风险开发两个预测模型，这两个模型都将在项目结束时提供给NOAA空间天气预报中心。此外，我们将通过地下空间天气（SWUG）计划改进阿拉斯加和新罕布什尔州这两个GIC高风险州的GIC预测。根据该计划，高中和本科生将建立和部署磁力计，测量地磁干扰，并分析数据。通过改变磁力计之间的空间距离，我们可以研究地面磁力计的最佳数量和分布，以进行精确的GIC建模和预测。该项目团队包括早期职业和代表性不足的科学家，并将提供研究项目和相关课程内容，高中，本科和研究生，包括那些在少数服务机构和地区学院。最近，科学和技术办公室的总统建议，我们采取措施，准备我们国家的基础设施，以抵御危险的空间天气的影响。地磁感应电流（GIC）是由空间天气事件中的地磁扰动引起的，可导致电力中断、列车系统故障和管道腐蚀。虽然GIC的风险在工业界和空间科学界得到广泛认可，但对GIC的发生模式和空间/地面条件了解甚少，主要是因为电力公司不愿提供其GIC数据，因为可能会对停电和任何其他技术问题产生法律的争议。该项目将利用美国地球物理研究所在空间物理方面的丰富专业知识。美国阿拉斯加大学（UAF）和美国太空科学中心（U.S. Space Science Center）的合作。新罕布什尔州（UNH）的研究人员结合了两所大学的数据科学专业知识，以了解和预测GIC。该项目特别关注地磁扰动、GIC的触发以及太阳风、磁层和电离层中这种扰动的可能来源。我们将把最先进的机器学习技术应用于20多年的空间/地面观测，并为地磁扰动和GIC风险开发两个预测模型，这两个模型都将在项目结束时提供给NOAA空间天气预报中心。此外，UNH地下空间天气（SWUG）计划将在新罕布什尔州和UAF范围内扩展。根据该计划，高中和本科生将构建和部署磁力计并分析数据。通过改变磁力计之间的空间距离，我们可以研究地面磁力计的最佳数量和分布，以进行精确的GIC建模和预测。此外，SWUG数据集将改善AK和NH中的GIC预测。阿拉斯加位于高纬度地区，GIC风险增加。新罕布什尔州位于较低纬度，但包括沿海地区以及具有高电阻率的基岩，这迫使电流流过输电线路等结构。因此，这两个国家为合作和比较结果提供了理想的条件，并将受益于GIC预测能力的提高。拟议的项目将通过支持新的跨学科和跨管辖区合作以及为未来空间气象研究中的大数据科学建设能力，为NSF和其他地方的项目参与者提供新的资助机会。该奖项反映了NSF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。

Recently, the Office of Science and Technology for the President recommended that we take steps to prepare our nation's infrastructure to withstand the hazardous space weather impacts. Geomagnetically induced currents (GICs), caused by the geomagnetic disturbance during space weather events, can produce power outages, train system failures, and pipeline corrosion. Although the risk of GICs are widely acknowledged in the industry and space science community, the occurrence patterns and the space/ground conditions responsible for GICs are poorly understood mainly because power companies are hesitant to provide their GIC data due to a possible legal dispute over the power outages and any other technical problems. The project will take advantage of the wealth of expertise in space physics and data science within the University of Alaska Fairbanks and the University of New Hampshire to understand and predict the GICs. This project specifically focuses on the geomagnetic disturbance, the trigger of GICs, and the possible sources of such disturbance in our geospace environments. We will apply the state-of-the-art machine learning techniques to over two decades of space/ground-based observations and develop two prediction models for the geomagnetic disturbance and the GIC-risk, both of which will be provided to NOAA Space Weather Prediction Center at the end of project. Additionally, we will improve our GIC predictions in Alaska and New Hampshire, the two high GIC-risk states, via the Space Weather Underground (SWUG) program. Under this program, high-school and undergraduate students will build and deploy magnetometers, measure geomagnetic disturbances, and analyze the data. By varying the spatial distance between the magnetometers, we can investigate the optimal number and distribution of ground magnetometers for accurate GIC modeling and prediction. The project team includes early-career and under-represented scientists and will provide research projects and relevant course content to high-school, undergraduate, and graduate students, including those at a minority serving institution and regional colleges.Recently, the Office of Science and Technology for the President recommended that we take steps to prepare our nation's infrastructure to withstand the hazardous space weather impacts. Geomagnetically induced currents (GICs), caused by the geomagnetic disturbance during space weather events, can produce power outages, train system failures, and pipeline corrosion. Although the risk of GICs are widely acknowledged in the industry and space science community, the occurrence patterns and the space/ground conditions responsible for GICs are poorly understood mainly because power companies are hesitant to provide their GIC data due to a possible legal dispute over the power outages and any other technical problems. The project will take advantage of the wealth of expertise in space physics within the Geophysical Institute at the U. of Alaska (UAF) and the Space Science Center at the U. of New Hampshire (UNH) combined with data science expertise at both universities to understand and predict the GICs. This project specifically focuses on the geomagnetic disturbance, the trigger of GICs, and the possible sources of such disturbance in solar wind, magnetosphere, and ionosphere. We will apply the state-of-the-art machine learning techniques to over two decades of space/ground-based observations and develop two prediction models for the geomagnetic disturbance and the GIC-risk, both of which will be provided to NOAA Space Weather Prediction Center at the end of project. Additionally, the UNH Space Weather Underground (SWUG) program will be expanded within New Hampshire and to UAF. Under this program, high-school and undergraduate students will build and deploy magnetometers and analyze the data. By varying the spatial distance between the magnetometers, we can investigate the optimal number and distribution of ground magnetometers for accurate GIC modeling and prediction. Additionally, the SWUG dataset will improve the GIC predictions in AK and NH. Alaska is in a region of high latitude with increased GIC risk. New Hampshire is at lower latitudes, but includes coastal areas as well as bedrock with high resistivity, which forces the currents to flow through structures such as power transmission lines. Thus, these two states provide ideal conditions for collaboration and comparison of results and would benefit from improved predictive capabilities for GICs. The proposed project would open new funding opportunities for project participants within NSF and elsewhere by supporting new interdisciplinary and inter-jurisdictional collaborations and building capacity for future big data science in space weather research.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.