Analysis of temporal–longitudinal–latitudinal characteristics in the global ionosphere based on tensor rank-1 decomposition

Abstract

Combining analyses of spatial and temporal characteristics of the ionosphere is of great significance for scientific research and engineering applications. Tensor decomposition is performed to explore the temporal-longitudinallatitudinal characteristics in the ionosphere. Three-dimensional tensors are established based on the time series of ionospheric vertical total electron content maps obtained from the Centre for Orbit Determination in Europe.To obtain large-scale characteristics of the ionosphere, rank-1 decomposition is used to obtain U (1) , U (2) , and U (3) , which are the resulting vectors for the time, longitude, and latitude modes, respectively. Our initial finding is that the correspondence between the frequency spectrum of U (1) and solar variation indicates that rank-1 decomposition primarily describes large-scale temporal variations in the global ionosphere caused by the Sun. Furthermore, the time lags between the maxima of the ionospheric U (2) and solar irradiation range from 1 to 3.7 h without seasonal dependence. The differences in time lags may indicate different interactions between processes in the magnetosphere-ionosphere-thermosphere system. Based on the dataset displayed in the geomagnetic coordinates, the position of the barycenter of U (3) provides evidence for north-south asymmetry (NSA) in the large-scale ionospheric variations. The daily variation in such asymmetry indicates the influences of solar ionization. The diurnal geomagnetic coordinate variations in U (3) show that the large-scale EIA (equatorial ionization anomaly) variations during the day and night have similar characteristics. Considering the influences of geomagnetic disturbance on ionospheric behavior, we select the geomagnetic quiet GIMs to construct the ionospheric tensor. The results indicate that the geomagnetic disturbances have little effect on large-scale ionospheric characteristics.

The Tensor‐based Feature Analysis of Spatiotemporal Field Data With Heterogeneity

“…In the tensor‐based geoscience application, the tensor structure is first utilized to integrate the spatial and temporal information in a unified framework. Then, with the capability of the tensor decomposition for feature extraction, the nonlinear signal extraction (Lu et al, ), feature‐based compressed storage (Yuan et al, ), and dimensionality reduction (Gao et al, ), can be achieved by absorbing the prominent spatiotemporal features and removing the redundancy. All these tensor‐based spatiotemporal analyses show the state‐of‐the‐art performance for spatiotemporal data (Leibovici, ).…”