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基于紫外极光图像的极光电集流指数模型

肖晖 田馨沁

肖晖, 田馨沁. 基于紫外极光图像的极光电集流指数模型[J]. 空间科学学报, 2023, 43(3): 434-445. doi: 10.11728/cjss2023.03.2022-0033
引用本文: 肖晖, 田馨沁. 基于紫外极光图像的极光电集流指数模型[J]. 空间科学学报, 2023, 43(3): 434-445. doi: 10.11728/cjss2023.03.2022-0033
XIAO Hui, TIAN Xinqin. Modeling of Auroral Electrojet Index with Ultraviolet Aurora Image (in Chinese). Chinese Journal of Space Science, 2023, 43(3): 434-445 doi: 10.11728/cjss2023.03.2022-0033
Citation: XIAO Hui, TIAN Xinqin. Modeling of Auroral Electrojet Index with Ultraviolet Aurora Image (in Chinese). Chinese Journal of Space Science, 2023, 43(3): 434-445 doi: 10.11728/cjss2023.03.2022-0033

基于紫外极光图像的极光电集流指数模型

doi: 10.11728/cjss2023.03.2022-0033
基金项目: 国家自然科学基金项目(41471381)和江苏省自然科学基金项目面上项目(BK20171410)共同资助
详细信息
    作者简介:

    肖晖:E-mail:xiaohui@njxzc.edu.cn

  • 中图分类号: P352

Modeling of Auroral Electrojet Index with Ultraviolet Aurora Image

  • 摘要: 极光电集流指数AE是描述地磁亚暴强弱的重要指标,且与极区磁层扰动及极光粒子沉降过程密切相关。因此,建立更加准确的极光电集流指数模型对空间天气的研究具有重要意义。利用1997年POLAR卫星紫外极光图像数据探究了紫外极光图像中极光强度IAP的空间分布与AE指数在不同季节的相关性,并在此基础上提出了基于紫外极光图像的AE指数模型。以网格化方法提取极光强度空间分布特征,采用广义回归神经网络,通过相关系数法和方差选择法构建Cor-GRNN和Var-GRNN两种AE指数模型,并针对冬至月份、夏至月份、分点月份3个季节分别进行训练。研究结果表明,AE指数与IAP具有相似的半年变化趋势,其相关性在不同季节差异较大。相比于太阳风驱动下的AE指数神经网络预测模型,基于极光图像的AE指数模型在ERMSR2标准上均优于其他模型,其中归一化ERMS小于0.1,模型对于AE指数变化的可解释度提升了10%左右。

     

  • 图  1  地磁台站在磁坐标系下的位置分布

    Figure  1.  Location distribution of geomagnetic stations in the magnetic coordinate system

    图  2  1997年AEAUALIAP指数30天滑动平均的年变化曲线

    Figure  2.  Annual change curves of 30 day moving average of AE, AU, AL and IAP indexes in 1997

    图  3  1997年D(上)、J(中)、E(下)月份极光强度与AE指数的相关性以及极光强度的累积分布。(a)~(c) 为AEIAP的散点图和线性关系拟合,(d)~(f) 为极光强度的累积分布(色标表示极光强度,取值0~20 × 104 cm–2·s–1

    Figure  3.  Correlation between the total auroral intensity and AE index in D, J and E months in 1997 and the cumulative distribution of auroral intensity. In (a) ~ (c) Scatter plot and linear relationship fitting of AE and IAP. In (d) ~ (f) cumulative distribution of auroral intensity (The color bar represents the aurora power, with values ranging from 0 to 20 × 104 cm–2·s–1)

    图  4  一级磁扰动期间AEAUAL随UT的变化曲线(a),极光强度磁经度分布的变化(b),极光强度经纬度分布的变化(c)

    Figure  4.  During first-order disturbance: (a) variation curves of AE, AU, and AL with UT; (b) variation of magnetic longitude distribution of auroral intensity; (c) variation of latitudinal longitude distribution of auroral intensity

    图  6  三级磁扰动期间 AEAUAL随UT的变化曲线(a),极光强度磁经度分布的变化(b),极光强度经纬度分布的变化(c)

    Figure  6.  During third-order disturbance: (a) variation curves of AE, AU, and AL with UT; (b) variation of magnetic longitude distribution of auroral intensity; (c) variation of latitudinal longitude distribution of auroral intensity

    图  5  二级磁扰动期间 AEAUAL随UT的变化曲线 (a),极光强度磁经度分布的变化 (b),极光强度经纬度分布的变化 (c)

    Figure  5.  During second-order disturbance: (a) variation curves of AE, AU, and AL with UT; (b) variation of magnetic longitude distribution of auroral intensity; (c) variation of latitudinal longitude distribution of auroral intensity

    图  7  紫外极光图像网格化特征提取过程

    Figure  7.  Grid feature extraction process of ultraviolet aurora image

    图  8  GRNN网络结构

    Figure  8.  GRNN network structure

    图  9  AE指数预测模型流程

    Figure  9.  AE index prediction model flow chart

    图  10  D,J,E这三组数据集中极光强度网格化特征与AE指数的相关系数

    Figure  10.  Correlation coefficient between grid characteristics of auroral intensity and AE index

    图  11  D月份两种模型AE指数预测结果与真实值曲线

    Figure  11.  Curve between AE index prediction and reference of two models in D months

    表  1  两种预测模型在D,J,E测试集上的预测性能比较

    Table  1.   Comparison of prediction performance of two prediction models on D, J, E test sets

    DatasetModelERMS(Normalized)VAR$ {R}^{2} $
    DCor-GRNN0.08050.25240.9555
    Var-GRNN0.08310.26850.9526
    JCor-GRNN0.09890.31640.9296
    Var-GRNN0.09180.32820.9420
    ECor-GRNN0.10190.28790.9463
    Var-GRNN0.10960.33310.9378
     加粗字体表示两种模型中更好的性能数据。
    下载: 导出CSV

    表  2  D月份亚暴发生期间AE指数预测模型性能

    Table  2.   Performance of AE index prediction model during substorm occurrence in D months

    ModelERMS /nTERMS(Normalized)VAR$ {R}^{2} $
    Cor-GRNN106.68570.10340.47990.9179
    Var-GRNN119.17280.11230.56660.9032
    下载: 导出CSV

    表  3  本文模型与其他AE指数预测模型性能对比

    Table  3.   Performance comparison between this model and other AE index prediction models

    ModelERMS(Normalized)VAR$ {R}^{2} $
    Cor-GRNN0.09370.28500.9438
    Var-GRNN0.09480.31040.9441
    ELM0.10790.28540.8351
    Random forest0.12460.38080.7800
    FFBP0.11050.29920.8271
    SVM0.13070.41840.7583
     加粗数据表示性能最优。
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-07-15
  • 录用日期:  2023-01-04
  • 修回日期:  2023-01-09
  • 网络出版日期:  2023-01-12

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