Synchronization of geomagnetic and ionospheric disturbances over Kazan station

Abstract

Abstract. The phenomena which accompany synchronization of night-time ionospheric and geomagnetic disturbances in an ULF range with periods 35-50 min near the mid-latitude station Kazan during a global magnetically quiet period have been analyzed. The comparison between dynamic spectra and wavelet patterns of these disturbances has revealed that spectral features of simultaneous disturbances of the F2-layer critical frequency and H, D, Z geomagnetic field components are similar. By studying spectral features of the F2-layer critical frequency over Kazan and disturbances of the H and D geomagnetic field components at magnetic stations which differ from Kazan station in longitude and latitude, we have established that the disturbances considered belong to the class of fast magnetosonic waves. The analysis of solar wind parameters, interplanetary magnetic field (IMF), and values of the auroral index AL in the period under study has shown that this event is associated with IMF B z component disturbances and occurs during substorm development.

Neural Network Recovery of Gaps in Geomagnetic Field Records

“…where is T  the target (actually registered) value for comparison with the output for the  -th example in the input sequence, O  is the value of the  -th output of the ANN for the  -th example of the input sequence, T is the average of all the target values of the ANN output, N is the number of points of the target process. Thus, the recovery efficiency is understood as the unit reduced by the value of the average relative variation, which in turn is the ratio of the standard error to the variance of the target process [12,14].…”

“…In this example, a study was conducted to find the optimal ANN architecture to obtain the smallest recovery error. It has been established that with an increase in the number of neurons in the hidden layer, the quality of recovery increases [14]. This is especially true for restoring recordings at the Alma-Ata magnetic station at one Kakioka station.…”

“…For this purpose, a recurrent ANN with backward propagation of error was used, which has a feedback loop coming from a hidden layer (Elman ANN). Our experience with the application of the ANN technique for geophysical tasks [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] suggests the appropriateness of using this particular version of the ANN. The architecture of this ANN is presented in Fig.…”

Classification of Solar Plasma Flows by Analyzing Out- and Intra-Magnetospheric Magnetohydrodynamic Oscillations During Magnetic Storms

“…The Data processing was performed using wavelet transform and subsequent post-processing of wavelet patterns results. The fourth order Daubechies function was chosen as a base wavelet in numerical experiments [6][7][8][9][10][11]. Scale coefficients of wavelet transform were considered in the range from 4 to 15, which corresponds to the range of the studied oscillation periods from 120 s to 450 s. The excess spectral information presented in the case of using the wavelet transform technique is reduced by plotting only the positions of the spectrum maxima on the chart (Fig.…”

Studying the relationship of localization parameters of solar sources of magnetic clouds with their characteristics and substorm activity

“…Unlike CME, MC can have a huge amount of magnetic field energy practically without mechanical momentum [Kilpua et al, 2012]. Because of extremely low content of particles, MC is weakly exposed to solar gravity and can increase the specific magnetic field energy due to compression when interacting with other dense solar wind structures [Barkhatova et al, 2017;Manakova et al, 2016]. Analysis of MC and long-term forecast of its geomagnetic effectiveness are hampered by the fact that an isolated MC is rare and its source is difficult to detect against the background of the disturbed solar corona or photosphere.…”