Global and multiscale aspects of magnetospheric dynamics in local-linear filters
Global and multiscale aspects of magnetospheric dynamics in local-linear filters
复制标题
局部线性滤波器中磁层动力学的全局和多尺度方面
DOI：
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发表时间：
2002
期刊：
影响因子：
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通讯作者：
K. Papadopoulos
中科院分区：
文献类型：
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作者：
A. Ukhorskiy;M. Sitnov;A. Sharma;K. Papadopoulos
[1] 磁层动力学由全局和多尺度组成。与太阳风输入和磁层输出相关的局部线性滤波器（LLF）较早被用来预测全局动力学行为。在本文中，研究了全球和多尺度过程在磁层动力学预测中的相对作用。滤波器是使用 VBS 时间序列作为输入、AL 指数作为输出从重建的输入输出磁层相空间导出的。我们证明了 LLF 的传统公式可以分为对应于全局和多尺度成分的两部分。第一部分是零阶项，它是通过模型输出的相空间平均值获得的。这是类似于相变物理学中的平均场模型的特征，它产生全局相干分量的迭代预测。第二部分由滤波器的高阶项组成，它们非常不规则，因此不能用于动态预测。这种不规则行为代表了与早期使用 LLF 研究的低维动力学的背离。早期的预测研究混合了这两个组成部分。然而，通过分离这两个组件，预测过程被高度简化，并且实现了更长周期的预测。多尺度性质源于大范围尺度的扰动，并且具有类似于有色噪声的功率谱。当在预测过程中考虑这些扰动时，与平均场模型相比，迭代预测的精度提高了四倍。然而，过滤技术并没有提供在动态模型中正确包含多尺度方面的规定，并且可以通过统计方法来实现预测的进一步改进。这些结果对空间天气预报具有重要意义。
[1] The magnetospheric dynamics consists of global and multiscale components. The local-linear filters (LLFs) relating the solar wind input and the magnetospheric output have been used earlier to predict the global dynamical behavior. In this paper, the relative role of global and multiscale processes in the prediction of magnetospheric dynamics is studied. The filters are derived from the reconstructed input–output magnetospheric phase space using time series of VBS as the input and AL index as the output. We show that the conventional formula for the LLF can be broken into two parts corresponding to the global and multiscale constituents. The first part is the zeroth-order term, which is obtained by the phase space average of the model outputs. This is a feature similar to the mean-field model in phase transition physics, which yields iterative predictions of the global coherent component. The second part consists of the higher-order terms of the filter, which are highly irregular and thus cannot be used in dynamical prediction. This irregular behavior represents the departure from the low-dimensional dynamics underlying earlier studies using LLFs. The earlier prediction studies mixed these two components. However, by separating these two components, the prediction procedure is highly simplified and longer period predictions are achieved. The multiscale nature arises from the perturbations over a wide range of scales and has a power spectrum similar to that of colored noise. When these perturbations are taken into account in the prediction process, the iterative predictions yield a factor of four improvement in the accuracy compared to the mean-field model. However, the filter technique does not provide a prescription for correctly including the multiscale aspects in a dynamical model and further improvement in forecasting can be achieved by a statistical approach. These results have important implications for space weather forecasting.