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首页> 外文期刊>Journal of geodynamics >Gravity reduction with three-dimensional atmospheric pressure data for precise ground gravity measurements
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Gravity reduction with three-dimensional atmospheric pressure data for precise ground gravity measurements

机译：利用三维大气压数据进行重力降低，以实现精确的地面重力测量

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The redistribution of air masses induces gravity variations (atmospheric pressure effect) up to about 20 μgal. These variations are disturbing signals in gravity records and they must be removed very carefully for detecting weak gravity signals. In the past, different methods have been developed for modelling of the atmospheric pressure effect. These methods use local or two-dimensional (2D) surface atmospheric pressure data and a standard height-dependent air density distribution. The atmospheric pressure effect is consisting of the elastic deformation and attraction term. The deformation term can be well modelled with 2D surface atmospheric pressure data, for instance with the Green's function method. For modelling of the attraction term, three-dimensional (3D) data are required. Results with 2D data are insufficient. From European Centre for Medium-Range Weather Forecasts (ECMWF) 3D atmospheric pressure data are now available. The ECMWF data used here are characterised by a spacing of Δφ and Δλ = 0.5°, 60 pressure levels up to a height of 60 km and an interval of 6 h. These data are used for modelling of the atmospheric attraction term. Two attraction models have been developed based on the point mass attraction of air segments and the gravity potential of the air masses. The modelling shows a surface pressure-independent part of gravity variations induced by mass redistribution of the atmosphere in the order of some μgal. This part can only be determined by using 3D atmospheric pressure data. It has been calculated for the Vienna Superconducting Gravimeter site. From this follows that the gravity reduction can be improved by applying the 3D atmospheric attraction model for analysing long-periodic tidal waves including the polar tide. The same improvement is expected for reduction of long-term absolute gravity measurements or comparison of gravity measurements at different seasonal times. By using 3D atmospheric pressure data, the gravity correction can be improved up to some μgal.
机译：空气质量的重新分布会引起重力变化（大气压效应），最大可达约20μgal。这些变化是重力记录中的干扰信号，必须非常小心地将其除去以检测微弱的重力信号。过去，已经开发了用于模拟大气压力效应的不同方法。这些方法使用局部或二维（2D）表面大气压力数据以及标准的高度相关的空气密度分布。大气压效应由弹性变形和吸引力项组成。变形项可以用二维表面大气压数据很好地建模，例如格林函数方法。对于吸引项的建模，需要三维（3D）数据。具有2D数据的结果不足。欧洲中距离天气预报中心（ECMWF）现在提供3D大气压力数据。此处使用的ECMWF数据的特征是间距Δφ和Δλ= 0.5°，60个压力水平（最高60 km的高度）和6 h的间隔。这些数据用于模拟大气吸引项。基于空气段的点质量吸引和空气团的重力势，已经开发了两个吸引模型。该模型显示了重力变化的表面压力无关部分，该变化由大气的质量重新分布引起，约为几微加仑。这部分只能通过使用3D大气压数据来确定。它是针对维也纳超导重力仪站点计算的。由此得出结论，可以通过应用3D大气吸引模型来分析包括极性潮汐在内的长周期潮汐来改善重力。对于减少长期绝对重力测量值或比较不同季节时间的重力测量值，预期会有相同的改进。通过使用3D大气压数据，可以将重力校正提高到几微加仑。

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