Study of the atmospheric pressure loading signal in very long baseline interferometry observations
Study of the atmospheric pressure loading signal in very long baseline interferometry observations
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DOI：
10.1029/2003jb002500
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发表时间：
2004-03-10
影响因子：
3.9
通讯作者：
Boy, JP
中科院分区：
文献类型：
--
作者：
Petrov, L;Boy, JP
大气环流引起的气团重新分布导致地壳载荷变形，垂直分量可高达 20 毫米，水平分量可高达 3 毫米。严格计算由压力载荷引起的场地位移需要了解整个地球表面的表面压力场。提出了使用国家环境预测数值天气模型中心的 6 小时压力场和 Ponte 和 Ray [2002] 大气潮汐模型来计算感兴趣的大地测量点的三维位移的程序。我们调查了可能的误差源，发现压力加载时间序列的误差低于 15% 的水平。我们通过使用 1980 年至 2002 年 350 万个超长基线干涉测量观测数据集估计压力加载时间序列的导纳因子来验证我们的模型。所有站点的平均导纳因子垂直位移为 0.95 +/- 0.02，水平位移为 1.00 +/- 0.07。首次检测到大气压力载荷引起的水平位移。这些导纳因子接近于统一，使我们能够得出这样的结论：平均而言，我们的模型在数量上与模型误差预算内的观察结果一致。同时我们发现该模型对于靠近海岸或山区的几个站点并不准确。我们的结论是，我们的模型适用于空间大地测量观测的常规数据缩减。
Redistribution of air masses due to atmospheric circulation causes loading deformation of the Earth's crust, which can be as large as 20 mm for the vertical component and 3 mm for horizontal components. Rigorous computation of site displacements caused by pressure loading requires knowledge of the surface pressure field over the entire Earth surface. A procedure for computing three-dimensional displacements of geodetic sites of interest using a 6 hourly pressure field from the National Centers for Environmental Prediction numerical weather models and the Ponte and Ray [2002] model of atmospheric tides is presented. We investigated possible error sources and found that the errors of our pressure loading time series are below the 15% level. We validated our model by estimating the admittance factors of the pressure loading time series using a data set of 3.5 million very long baseline interferometry observations from 1980 to 2002. The admittance factors averaged over all sites are 0.95 +/- 0.02 for the vertical displacement and 1.00 +/- 0.07 for the horizontal displacements. For the first time, horizontal displacements caused by atmospheric pressure loading have been detected. The closeness of these admittance factors to unity allows us to conclude that on average, our model quantitatively agrees with the observations within the error budget of the model. At the same time we found that the model is not accurate for several stations that are near a coast or in mountain regions. We conclude that our model is suitable for routine data reduction of space geodesy observations.