A method to identify aperiodic disturbances in the ionosphere
A method to identify aperiodic disturbances in the ionosphere
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DOI：
10.5194/angeo-32-563-2014
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发表时间：
2014-05
影响因子：
1.9
通讯作者：
Jing Song Wang;Z. Chen;Chunming Huang
中科院分区：
文献类型：
--
作者：
Jing Song Wang;Z. Chen;Chunming Huang
抽象的。本文将电离层F2层临界频率的变化分解为周期分量和非周期分量。后者包括地球物理对电离层的影响和随机噪声造成的干扰。谱白化法(SWM)是一种用于统计估计和/或检测的信号处理技术，用于识别电离层中的非周期分量。这里采用的白化算法用于将观测数据序列的傅里叶变换除以实包络函数。结果，周期性成分被抑制，而非周期性成分成为主要贡献者。应用到基于含有人为(因此可控)干扰的电离层观测的显著模拟周期特征的合成数据集，以验证SWM用于识别非周期分量。虽然经过后处理后，随机噪声有所增强，但仍然可以清楚地识别出人为干扰。SWM随后被应用于真实的电离层观测。它被发现比通常使用的月中位数方法更敏感地识别地磁效应。此外，SWM检测到的干扰在所有时间尺度上都用高斯型概率密度函数来表征，这进一步简化了统计分析，并表明无论时间尺度如何，都可以比较这样识别的干扰。
Abstract. In this paper, variations in the ionospheric F2 layer's critical frequency are decomposed into their periodic and aperiodic components. The latter include disturbances caused both by geophysical impacts on the ionosphere and random noise. The spectral whitening method (SWM), a signal-processing technique used in statistical estimation and/or detection, was used to identify aperiodic components in the ionosphere. The whitening algorithm adopted herein is used to divide the Fourier transform of the observed data series by a real envelope function. As a result, periodic components are suppressed and aperiodic components emerge as the dominant contributors. Application to a synthetic data set based on significant simulated periodic features of ionospheric observations containing artificial (and, hence, controllable) disturbances was used to validate the SWM for identification of aperiodic components. Although the random noise was somewhat enhanced by post-processing, the artificial disturbances could still be clearly identified. The SWM was then applied to real ionospheric observations. It was found to be more sensitive than the often-used monthly median method to identify geomagnetic effects. In addition, disturbances detected by the SWM were characterized by a Gaussian-type probability density function over all timescales, which further simplifies statistical analysis and suggests that the disturbances thus identified can be compared regardless of timescale.