Geophysical research: article

Аnnotation: Modern gravimetric measurements allow us to study both global and local features of the internal structure of the Earth, to obtain more and more reliable estimates of elastic modulus and quality factors the mantle. It is important to study the resonant effects of the outer and inner cores, as well as waves of large periods. In this connection, correct and reliable interpretation of gravimetric measurement data is especially important. For this, in particular, it is necessary to be able to accurately calculate the atmospheric gravimetric effect, the total amplitude of which at various observation points can reach 5-10 mGal. In this regard, this paper presents a method for calculating the atmospheric gravimetric loading effect and direct Newtonian atmospheric attraction. All calculations are performed in the approximation of a simple layer. The calculation of atmospheric effects is carried out by decomposing the surface atmospheric pressure in spherical functions up to 360 degrees. As the initial data, the values of the surface atmospheric pressure of the modern European reanalysis ERA5 were applied. The load effect was calculated using the load Love numbers, calculated by the author of the present work for the case of an inelastic rotating self-gravitating ellipsoidal Earth [Spiridonov, 2017; Spiridonov, Vinogradova, 2018]. Estimates are given for the relative errors of the calculations arising both as a result of the replacement of the real thickness of the atmosphere by a simple layer and as a result of the finiteness of the degree of decomposition of atmospheric pressure into spherical functions. For this, the errors of the corresponding integrals for the standard atmosphere were analyzed, and a comparative analysis of decomposition maps of the surface atmospheric pressure up to 180, 360 and 720 orders was carried out. The total relative error of the calculations was 0.01 %. Numerical estimates of the absolute values of the atmospheric gravimetric effect, as well as possible ranges of variation of the regression coefficients depending on weather and local conditions, are obtained.