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

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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.