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

RII Track-2 FEC：协作研究：利用大数据提高对地磁感应电流的理解和预测

• 批准号: 1920965
• 负责人: Donald Hampton
• 金额: $ 399.79万
• 依托单位: University of Alaska Fairbanks Campus
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1920965&HistoricalAwards=false
• 关键词: RII; Track; FEC; Collaborative; Research

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.

最近，总统科学和技术办公室建议我们采取措施，为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(GICs)是在空间天气事件期间由地磁扰动引起的，可能导致停电、列车系统故障和管道腐蚀。尽管GIC的风险在工业界和空间科学界得到广泛承认，但人们对GIC的发生模式和空间/地面条件知之甚少，主要是因为电力公司由于可能存在关于停电和任何其他技术问题的法律纠纷而不愿提供其GIC数据。该项目将利用阿拉斯加大学费尔班克斯大学和新汉普郡大学在空间物理和数据科学方面的丰富专业知识来了解和预测GIC。该项目特别侧重于地磁扰动，即GICs的触发因素，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，并开发两个地磁扰动和GIC-Risk的预测模型，这两个模型将在项目结束时提供给NOAA空间天气预测中心。此外，我们将通过空间天气地下(SWUG)计划，改善我们在阿拉斯加和新罕布夏州这两个GIC高危州的GIC预测。根据这一计划，高中生和本科生将建造和部署磁力仪，测量地磁扰动，并分析数据。通过改变磁强计之间的空间距离，我们可以研究地面磁强计的最佳数量和分布，以实现准确的GIC建模和预测。该项目团队包括职业生涯早期和代表性不足的科学家，将向高中、本科生和研究生提供研究项目和相关课程内容，包括少数族裔服务机构和地区性大学的学生。最近，总统科学和技术办公室建议我们采取措施，准备我国的基础设施，以抵御危险的太空天气影响。地磁感应电流(GICs)是在空间天气事件期间由地磁扰动引起的，可能导致停电、列车系统故障和管道腐蚀。尽管GIC的风险在工业界和空间科学界得到广泛承认，但人们对GIC的发生模式和空间/地面条件知之甚少，主要是因为电力公司由于可能存在关于停电和任何其他技术问题的法律纠纷而不愿提供其GIC数据。该项目将利用阿拉斯加大学地球物理研究所(UAF)和新汉普郡大学(UNH)空间科学中心在空间物理方面的丰富专业知识，并结合这两所大学的数据科学专业知识来了解和预测GIC。该项目特别侧重于地磁扰动、GICs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，并开发两个地磁扰动和GIC-Risk的预测模型，这两个模型将在项目结束时提供给NOAA空间天气预测中心。此外，UNH空间天气地下(SWUG)计划将在新汉普郡和UAF扩大。根据这项计划，高中生和本科生将建造和部署磁力仪并分析数据。通过改变磁强计之间的空间距离，我们可以研究地面磁强计的最佳数量和分布，以实现准确的GIC建模和预测。此外，SWUG数据集将改进AK和NH的GIC预测。阿拉斯加位于高纬度地区，GIC风险增加。新汉普郡位于较低纬度，但包括沿海地区以及高电阻率的基岩，这迫使电流流经输电线路等结构。因此，这两个国家为协作和比较成果提供了理想的条件，并将受益于全球综合信息中心改进的预测能力。拟议的项目将为国家科学基金会和其他地方的项目参与者打开新的资金机会，支持新的跨学科和跨司法管辖区的合作，并为未来空间气象研究中的大数据科学建设能力。这一奖项反映了国家科学基金会的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Global Survey of Plasma Sheet Electron Precipitation due to Whistler Mode Chorus Waves in Earth's Magnetosphere

• DOI: 10.1029/2020gl088798
• 发表时间: 2020-07
• 期刊: Geophysical Research Letters
• 影响因子: 5.2
• 作者: Q. Ma;H. Connor;X.‐J. Zhang;W. Li;X. Shen;D. Gillespie;C. Kletzing;W. Kurth;G. Hospodarsky;S. Claudepierre;G. Reeves;H. Spence

A Contrastive Learning Approach to Auroral Identification and Classification

• DOI: 10.1109/icmla52953.2021.00128
• 发表时间: 2021-09
• 期刊: 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)
• 作者: Jeremiah W. Johnson;Swathi Hari;D. Hampton;H. Connor;A. Keesee

Changes in the Magnetic Field Topology and the Dayside/Nightside Reconnection Rates in Response to a Solar Wind Dynamic Pressure Front: A Case Study

• DOI: 10.1029/2020ja028768
• 发表时间: 2021-06
• 期刊: Journal of Geophysical Research: Space Physics
• 作者: A. Boudouridis;H. Connor;D. Lummerzheim;A. Ridley;E. Zesta

Probabilistic Forecasting of Ground Magnetic Perturbation Spikes at Mid‐Latitude Stations

中纬度站地磁扰动尖峰的概率预报

• DOI: 10.1029/2023sw003446
• 发表时间: 2023
• 期刊: Space Weather
• 影响因子: 3.7
• 作者: Coughlan, Michael;Keesee, Amy;Pinto, Victor;Mukundan, Raman;Marchezi, José Paulo;Johnson, Jeremiah;Connor, Hyunju;Hampton, Don
• 通讯作者: Hampton, Don

Comparison of Deep Learning Techniques to Model Connections Between Solar Wind and Ground Magnetic Perturbations

深度学习技术对太阳风与地磁扰动之间关系模型的比较

• DOI: 10.3389/fspas.2020.550874
• 发表时间: 2020
• 期刊: Frontiers in Astronomy and Space Sciences
• 影响因子: 3
• 作者: Keesee, Amy M.;Pinto, Victor;Coughlan, Michael;Lennox, Connor;Mahmud, Md Shaad;Connor, Hyunju K.
• 通讯作者: Connor, Hyunju K.