利用机器学习为赤道至中纬度全球夜间电离层等离子体不规则现象建模

IF 3.7 2区 地球科学
Space Weather Pub Date : 2024-02-29 DOI:10.1029/2023sw003754
Ephrem Beshir Seba, Giovanni Lapenta
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引用次数: 0

摘要

本研究的重点是在全球范围内模拟夜间顶部电离层等离子体不规则现象(PI)的特征。我们利用随机森林(RF)和一维卷积神经网络(1D-CNN)模型,结合来自 Swarm A、B 和 C 卫星的数据、来自 OMNIWeb 数据中心的空间气象数据以及带状和经向风模型数据。我们的目标是使用根据 12 个空间天气和电离层参数训练的多层 1D-CNN 模型模拟每月全球 PI 特征。此外,我们还研究了对预测全球夜间 PI 特性最有影响的输入参数。我们的研究结果表明,非春分月份在美洲-非洲纵向扇面上表现出最高的赤道 PI 幅值,这与预期的春分月份较高的瑞雷-泰勒不稳定性增长率相反。冬季显示出最强烈和最广泛的垂直和水平分布赤道 PI 模式。我们还观察到地磁纬度上的双峰和纵向变化的波状不规则结构,尤其是在 5 月和 8 月,主要是在 9 月。此外,在不同季节还观测到南北半球不对称的 PI。通过射频参数重要性分析方法,我们确定与时间、地理和磁干扰有关的因素在预测全球 PI 变率中起着至关重要的作用。这些发现强调了这些变量在控制大西洋扇区观测到的最强 PI 特征方面的重要作用,这在 PI 研究中引起了广泛关注。所采用的 1D-CNN 模型显示出卓越的预测能力,在所有月份和卫星的全球 PI 特性方面显示出 0.98 的强相关性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modeling Equatorial to Mid-Latitudinal Global Night Time Ionospheric Plasma Irregularities Using Machine Learning
This study focuses on modeling the characteristics of nighttime topside Ionospheric Plasma Irregularities (PI) on a global scale. We utilize Random Forest (RF) and a one-dimensional Convolutional Neural Network (1D-CNN) model, incorporating data from the Swarm A, B, and C satellites, space weather data from the OMNIWeb data center, as well as zonal and meridional wind model data. Our objective is to simulate monthly global PI characteristics using a multilayer 1D-CNN model trained on 12 space weather and ionospheric parameters. In addition, we investigate the most influential input parameters for predicting global nighttime PI characteristics. Our findings indicate that non-equinox months exhibit the highest equatorial PI magnitude over the American-African longitudinal sector, contrary to the expected higher Rayleigh-Taylor instability growth rate during equinox months. Winter months display the most intense and widespread vertically and horizontally distributed equatorial PI patterns. We also observe double peaks across geomagnetic latitudes and longitudinally varying wavelike irregularity structures, particularly in May, August, and predominantly in September. Furthermore, north-south hemispherical asymmetry in PI observed across different seasons. Through the RF parameter importance analysis method, we determine that temporal, geographical, and magnetic disturbance-related factors play a crucial role in predicting global PI variabilities. These findings emphasize the significance of these variables in controlling the strongest PI characteristics observed in the Atlantic sector, which has garnered considerable attention in PI research. The employed 1D-CNN model demonstrates exceptional predictive capabilities, exhibiting a strong correlation of 0.98 for global PI characteristics across all months and satellites.
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