相关论文: Principal component analysis of geomagnetic activi…

In this paper, we study two sets of local geomagnetic indices from 26 stations using the principal component (PC) and the independent component (IC) analysis methods. We demonstrate that the annually averaged indices can be accurately…

Principal component analysis is performed on Birkeland or field-aligned current (FAC) measurements from the Active Magnetosphere and Planetary Electrodynamics Response Experiment. Principal component analysis (PCA) identifies the patterns…

In this paper, we analyze the applicability of the principal component analysis (PCA) as a tool to extract the Sq variation of the geomagnetic field. We tested different geomagnetic field components and used data measured at different…

Principal Components Analysis (PCA) and Independent Component Analysis (ICA) are used to identify global patterns in solar and space data. PCA seeks orthogonal modes of the two-point correlation matrix constructed from a data set. It…

Solar wind-magnetosphere coupling, its causes and consequences have been studied for the last several decades. However, the assessment of continuously changing behaviour of the sun, plasma and field flows in the interplanetary space and…

Local scaling and singularity properties of solar wind and geomagnetic time series were analysed using H\"{o}lder exponents $\alpha$. It was shown that in analysed cases due to multifractality of fluctuations $\alpha$ changes from point to…

We decompose the monthly aa-index of cycles 10-23 using principal component analysis (PCA). We show that the first component (PC1) is related to solar cycle, and accounts for 41.5 % of the variance of the data. The second component (PC2) is…

This paper describes a methodology (or treatment) to establish a representative signal of the global magnetic diurnal variation based on a spatial distribution in both longitude and latitude of a set of magnetic stations as well as their…

Using Principal Component Analysis (PCA), the nodal injection and line flow patterns in a network model of a future highly renewable European electricity system are investigated. It is shown that the number of principal components needed to…

The Principal Component Analysis (PCA) is a data dimensionality reduction technique well-suited for processing data from sensor networks. It can be applied to tasks like compression, event detection, and event recognition. This technique is…

Mursula et al. [2011] (MTL11) suggest that there is a 22-year variation in solar wind activity that coupled with the variation in heliographic latitude of the Earth during the year, gives rise to an apparent semiannual variation of…

Functional data typically contains amplitude and phase variation. In many data situations, phase variation is treated as a nuisance effect and is removed during preprocessing, although it may contain valuable information. In this note, we…

Long-term geomagnetic activity presented by the aa index has been used to show that the heliospheric magnetic field has more than doubled during the last 100 years. However, serious concern has been raised on the long-term consistency of…

Principal Component Analysis (PCA) is a well-known multivariate technique used to decorrelate a set of vectors. PCA has been extensively applied in the past to the classification of stellar and galaxy spectra. Here we apply PCA to the…

Here we present the results of the comparative analysis of the regular daily variations of the geomagnetic field, Sq and SD, obtained from the observations with two methods: (1) using the so-called geomagnetically quiet day (QD) to…

The solar magnetic activity consists of two periodic components: the main cycle with a period of 11 yr and a shorter cycle with a period of ~2 yrs. The origin of this second periodicity is still not well understood. We use almost 15 yrs of…

The relationship between moderate and extremely high levels of geomagnetic activity, represented by the Kp index (2- to 5+ and 6- to 9), and solar wind conditions during southward IMF intervals was revealed utilizing a newly developed…

Predicting geomagnetic conditions based on in-situ solar wind observations allows us to evade disasters caused by large electromagnetic disturbances originating from the Sun to save lives and protect economic activity. In this study, we…

We use Principal Component Analysis (PCA) to study the gas dynamics in numerical simulations of typical MCs. Our simulations account for the non-isothermal nature of the gas and include a simplified treatment of the time-dependent gas…

We present geomagnetic storms (GSs) selected from three solar cycles, spanning the years 1995 to 2022. We studied the development of the main phase of storms within disturbance storm time (Dst) amplitudes ranging from Dst =-64 nT to Dst=-…