Atmospheric pressure loading in GPS positions: dependency on GPS processing methods and effect on assessment of seasonal deformation in the contiguous USA and Alaska
Atmospheric pressure loading in GPS positions: dependency on GPS processing methods and effect on assessment of seasonal deformation in the contiguous USA and Alaska
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DOI：
10.1007/s00190-020-01445-w
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发表时间：
2020-11
影响因子：
4.4
通讯作者：
H. Martens;D. Argus;C. Norberg;G. Blewitt;T. Herring;A. Moore;W. Hammond;C. Kreemer
中科院分区：
文献类型：
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作者：
H. Martens;D. Argus;C. Norberg;G. Blewitt;T. Herring;A. Moore;W. Hammond;C. Kreemer
全球定位系统彻底改变了监测地球系统过程，包括地球水循环的能力。几个分析中心处理GPS数据，以每日时间分辨率估计地面天线位置。处理策略的差异可能导致坐标位置估计的不一致，从而影响与大气和水文质量负荷变化相关的地壳位移分析。在这里，我们比较了五个GPS数据产品的三个处理中心：内华达州大地测量实验室，喷气推进实验室，和UNAVCO财团。我们发现，5%至30%的分散在残留的GPS时间序列（通常被认为是噪音）可以解释在美国和阿拉斯加的邻近大气负荷，但数据产品的百分比变化很大。使用高分辨率对流层模型（例如，ECMWF）在校正大气载荷后表现出比使用恒定或缓慢变化的对流层模型估计的位置显著更低的散射（例如，GPT 2w）。数据产品还显示出季节变形的差异（通常在很大程度上归因于水文质量负荷的波动）：估计的季节振幅的中值矢量差异在垂直分量中为0.4-1.0 mm，在水平分量中为0.1-0.3 mm，或季节振荡平均振幅的约10-40%。较新的产品表现出较低的总散射和较强的相关性比旧的产品。季节性变形估计的网络一致性差异揭示了数据产品之间的参考框架不一致。我们还交叉检查了两个独立的大气压力加载模型：ESMGFZ和LoadDef。
The Global Positioning System (GPS) has revolutionized the ability to monitor Earth-system processes, including Earth’s water cycle. Several analysis centers process GPS data to estimate ground-antenna positions at daily temporal resolution. Differences in processing strategies can lead to inconsistencies in coordinate-position estimates and therefore influence the analysis of crustal displacement associated with variations in atmospheric and hydrologic mass loading. Here, we compare five GPS data products produced by three processing centers: the Nevada Geodetic Laboratory, Jet Propulsion Laboratory, and UNAVCO Consortium. We find that 5 to 30% of the scatter in residual GPS time series (commonly considered noise) can be explained by atmospheric loading in the contiguous USA and Alaska, but that the percentages vary widely by data product. Positions derived using high-resolution troposphere models (e.g., ECMWF) exhibit significantly lower scatter after correcting for atmospheric loading than positions estimated using constant or slowly varying troposphere models (e.g., GPT2w). The data products also exhibit differences in seasonal deformation (commonly attributed, in large part, to fluctuations in hydrologic mass loading): median vector differences in estimated seasonal amplitude range from 0.4–1.0 mm in the vertical component and 0.1–0.3 mm in the horizontal components, or about 10–40% of the mean amplitudes of seasonal oscillation. Newer products exhibit lower total scatter and stronger correlations than older products. Network-coherent differences in estimates of seasonal deformation reveal reference-frame inconsistencies between data products. We also cross-check two independent models of atmospheric pressure loading: ESMGFZ and LoadDef.