利用基于机器学习的场对齐电流模型计算高纬度电离层电动力学

IF 3.7 2区 地球科学
Space Weather Pub Date : 2024-04-10 DOI:10.1029/2023sw003683
V. Sai Gowtam, Hyunju Connor, Bharat S. R. Kunduri, Joachim Raeder, Karl M. Laundal, S. Tulasi Ram, Dogacan S. Ozturk, Donald Hampton, Shibaji Chakraborty, Charles Owolabi, Amy Keesee
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引用次数: 0

摘要

我们引入了一个新框架,称为基于机器学习(ML)的极光电离层电动力学模型(ML-AIM)。ML-AIM 利用 Kunduri 等人的场对齐电流 ML 模型(2020 年,https://doi.org/10.1029/2020JA027908)、Robinson 等人的极光电导模型(2020 年,https://doi.org/10.1029/2020JA028008)以及 Moen 和 Brekke 的太阳辐照度电导模型(1993 年,https://doi.org/10.1029/92gl02109)求解电流连续性方程。ML-AIM 的输入是太阳风等离子体、行星际磁场和地磁指数的 60 分钟时间历程,输出是电离层电动势、电场、Pedersen/Hall 电流和焦耳热。我们对2013年5月14日的弱地磁活动间隔和2017年9月7-8日的地磁暴进行了两次ML-AIM模拟。ML-AIM 模拟产生了物理上准确的电离层电势模式,例如双电池对流模式和活跃期电势增强。将ML-AIM、Weimer(2005年,https://doi.org/10.1029/2004ja010884)模型和超级双极光雷达网(SuperDARN)数据吸收的电位与国防气象卫星计划F17卫星3204次极地穿越的电位进行比较,发现ML-AIM的性能优于其他模型。ML-AIM 具有独特性和创新性,因为它预测电离层对时变太阳风和地磁条件的反 应,而 Weimer 等其他传统经验模型(2005 年,https://doi.org/10.1029/2004ja010884)旨在提供准稳 定太阳风/IMF 条件下的准静态电离层状况。目前正在计划改进 ML-AIM 的性能,将极光降水和电离层传导模型的全 ML 网络包括在内,以描述地磁活跃期的特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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