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，总统科学技术办公室建议我们采取措施，为我们国家的基础设施做好准备，以抵御危险的太空天气影响。地磁感应电流（GICs）是在空间天气事件中由地磁干扰引起的，可造成停电、列车系统故障和管道腐蚀。虽然工业和空间科学界广泛认识到地球地质灾害的风险，但人们对地球地质灾害的发生模式和造成地球地质灾害的空间/地面条件知之甚少，这主要是因为电力公司由于停电和任何其他技术问题可能引起的法律纠纷而不愿提供地球地质灾害数据。该项目将利用阿拉斯加州费尔班克斯大学和新罕布什尔大学在空间物理和数据科学方面的丰富专业知识来理解和预测gis。本项目重点研究地球空间环境中的地磁扰动、地磁扰动的触发因素以及地磁扰动的可能来源。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，并开发地磁干扰和gic风险的两个预测模型，这两个模型将在项目结束时提供给NOAA空间天气预报中心。此外，我们将通过地下空间天气（SWUG）计划，改进我们在阿拉斯加和新罕布什尔州这两个高GIC风险州的GIC预测。根据该计划，高中生和大学生将建造和部署磁力计，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，以准确地进行GIC建模和预测。项目团队包括职业生涯早期和代表性不足的科学家，并将为高中、本科和研究生提供研究项目和相关课程内容，包括少数民族服务机构和地区学院的学生。最近，总统科学技术办公室建议我们采取措施，为我们国家的基础设施做好准备，以抵御危险的太空天气影响。地磁感应电流（GICs）是在空间天气事件中由地磁干扰引起的，可造成停电、列车系统故障和管道腐蚀。虽然工业和空间科学界广泛认识到地球地质灾害的风险，但人们对地球地质灾害的发生模式和造成地球地质灾害的空间/地面条件知之甚少，这主要是因为电力公司由于停电和任何其他技术问题可能引起的法律纠纷而不愿提供地球地质灾害数据。该项目将利用阿拉斯加大学地球物理研究所（UAF）和新罕布什尔大学空间科学中心（UNH）在空间物理学方面的丰富专业知识，结合两所大学的数据科学专业知识，来理解和预测全球地质系统。本项目重点研究了地磁干扰、地磁干扰的触发因素以及太阳风、磁层和电离层中地磁干扰的可能来源。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，并开发地磁干扰和gic风险的两个预测模型，这两个模型将在项目结束时提供给NOAA空间天气预报中心。此外，UNH地下空间天气（SWUG）计划将扩展到新罕布什尔州和UAF。根据该计划，高中生和大学生将建造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，以准确地进行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。新罕布什尔州位于低纬度地区，但包括沿海地区和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，这两种状态为协作和比较结果提供了理想的条件，并将受益于改进的gic预测能力。拟议的项目将为NSF和其他地方的项目参与者提供新的资助机会，支持新的跨学科和跨管辖区合作，并为未来空间天气研究中的大数据科学建设能力。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。

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.