论文标题

比例分离的动态模式分解和电离层预测

Scale-Separated Dynamic Mode Decomposition and Ionospheric Forecasting

论文作者

Alford-Lago, Daniel J., Curtis, Christopher W., Ihler, Alexander T., Zawdie, Katherine A.

论文摘要

我们提出了一种使用电离层电子密度分布时间序列的模态分解来预测FOF2和HMF2参数的方法。我们的方法基于动态模式分解(DMD),该分解提供了一种单独测量的时空模式的方法。 DMD模型很容易更新,因为记录了新数据,并且不需要任何物理来告知动态。但是,在电离层轮廓的情况下,我们发现了广泛的振荡,其中包括高于昼夜频率。因此,我们使用小波分解提出了对DMD的非平凡扩展。我们将此方法称为比例分离的动态模式分解(SSDMD),因为小波在数据中的不同时间尺度上分离了分离的组件。我们表明,该方法提供了峰等离子体密度的稳定重建,可用于预测未来时间步骤的FOF2和HMF2状态。我们在涵盖高太阳活动和低纬度位置的数据集的数据集上证明了SSDMD方法。

We present a method for forecasting the foF2 and hmF2 parameters using modal decompositions of ionospheric electron density profile time series. Our method is based on the Dynamic Mode Decomposition (DMD), which provides a means of determining spatiotemporal modes from measurements alone. DMD models are easily updated as new data is recorded and do not require any physics to inform the dynamics. However, in the case of ionospheric profiles, we find a wide range of oscillations, including some far above the diurnal frequency. Therefore, we propose nontrivial extensions to DMD using wavelet decompositions. We call this method the Scale-Separated Dynamic Mode Decomposition (SSDMD) since the wavelets isolate fluctuations at different time scales in the data into separated components. We show that this method provides a stable reconstruction of the peak plasma density and can be used to predict the state of foF2 and hmF2 at future time steps. We demonstrate the SSDMD method on data sets covering periods of high and low solar activity as well as low, mid, and high latitude locations.

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