Calculating the High‐Latitude Ionospheric Electrodynamics Using a Machine Learning‐Based Field‐Aligned Current Model
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V. Sai Gowtam↗
Engineering topics
Charles Owolabi
Publications and source records attributed to Charles Owolabi.
Calculating the High-Latitude Ionospheric Electrodynamics Using A Machine Learning-Based Field-Aligned Current Model
We introduce a new framework called Machine Learning (ML) based Auroral Ionospheric electrodynamics Model (ML-AIM). ML-AIM solves a current continuity equation by utilizing the ML model of Field Aligned Currents of Kunduri et al. (2020, https://doi.org/10.1029/2020JA027908), the FAC-derived auroral conductance model of Robinson et al. (2020, https://doi.org/10.1029/2020JA028008), and the solar irradiance conductance model of Moen and Brekke (1993, https://doi.org/10.1029/92gl02109). The ML-AIM inputs are 60-min time histories of solar wind plasma, interplanetary magnetic fields (IMF), and geomagnetic indices, and its outputs are ionospheric electric potential, electric fields, Pedersen/Hall currents, and Joule Heating. We conduct two ML-AIM simulations for a weak geomagnetic activity interval on 14 May 2013 and a geomagnetic storm on 7–8 September 2017. ML-AIM produces physically accurate ionospheric potential patterns such as the two-cell convection pattern and the enhancement of electric potentials during active times. The cross polar cap potentials (ΦPC) from ML-AIM, the Weimer (2005, https://doi.org/10.1029/2004ja010884) model, and the Super Dual Auroral Radar Network (SuperDARN) data-assimilated potentials, are compared to the ones from 3204 polar crossings of the Defense Meteorological Satellite Program F17 satellite, showing better performance of ML-AIM than others. ML-AIM is unique and innovative because it predicts ionospheric responses to the time-varying solar wind and geomagnetic conditions, while the other traditional empirical models like Weimer (2005, https://doi.org/10.1029/2004ja010884) designed to provide a quasi-static ionospheric condition under quasi-steady solar wind/IMF conditions. Plans are underway to improve ML-AIM performance by including a fully ML network of models of aurora precipitation and ionospheric conductance, targeting its characterization of geomagnetically active times.
auroral electrodynamics↗
Maryland Space Weather UnderGround (SWUG) Educational Outreach Program for Solar Eclipse Study
Space Weather UnderGround (SWUG) is an educational outreach program that provides hands-on experiences in Science, Technology, Engineering, and Mathematics (STEM) to high school and undergraduate students. It has three ultimate goals: 1. Educating the future STEM workforce. 2. Building a cost-effective, research-capable ground 3. magnetometer array across the United States. Collecting geomagnetic field data at high spatial resolution for heliophysics research. SWUG students build, test, and deploy the Simple Aurora Monitor (SAM)-III kit, purchased by Reeve Engineers. The SAM-III kit is a fluxgate magnetometer that measures geomagnetic field changes at a resolution of 1 nT/sec. Dr. Charles Smith initiated the SWUG program at the University of New Hampshire, and it has since expanded to Alaska and Maryland. This presentation introduces the Maryland SWUG activities. The MD-SWUG program was started in the Fall of 2022 with the goal of deploying magnetometers to the solar eclipse sites in 2023 and 2024. Two student-built magnetometers were deployed to the Southwest Research Institute and the Los Alamos National Laboratory for the 2023 annular solar eclipse, with plans to deploy at least four magnetometers to the 2024 total solar eclipse sites, including the University of Texas at Dallas. During a solar eclipse, reduced solar irradiance weakens ionospheric currents and reduces geomagnetic fields by up to 30 nT at the eclipse sites. A recent study suggested that solar eclipses impact geomagnetic fields not only along the eclipse path but also at their magnetic conjugate locations. The SWUG program will help us understand this electrodynamic coupling between the conjugate locations during a solar eclipse by providing better data coverage along the solar eclipse path.
Hyunju Connor↗