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Solving the problem of determinating moon gravitational tides in the electrical field of the atmosphere boundary layer

Keywords:

L.V. Grunskaya, V.V. Isakevich, E. V. Alekseevich, L.T. Sushkova, A.A. Zakirov, D.V. Rubai


Spectral analysis of time series of the electrical field of the atmosphere boundary layer doesn, t allow to single out for sure influence of moon tides on the electrical field, which resulted in necessity of carrying out investigations of the signal structure in spectral and time ranges with the help of the developed programm and analytical system on the base of the method of the latent vectors of the correlative matrixes of time series. There has been developed a programm and analytical system for investigating the signal structure in spectral and time ranges, called by geophysical processes. The main purpose in developing such system is investigation of the signal structure in spectral and time ranges called by geophysical processes in the electrical field of the atmosphere boundary layer. Using the method of the spectral analysis of the latent vectors in many years, time series of the electrical field vertical component showed its efficiency for exposing periodical components with relative power contribution up to 10-4. Novelty of the developed method is in the effect that not the initial time series itself but latent vectors, which it is divided up, receives spectral analysis, which allows to use spectral analysis for the separate time series non-correlative components, connected with the definite physical processes. Exposing frequencies, coinsiding with the frequencies of the tides, the total frequency of coincidence of the amplitude spectrum maxima of the closen latent vectors of the electrical and geomagnetical fields with the frequencies of tides was 99 per cent. There has been suggested and confirmed the hypothesis about the advantage of the spectral analysis of the latent vectors of the time series covariance matrix over the direct spectral analysis of the time series on exposing periodical components of the time series.
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