G015-03
Atmospheric pressure loading in GPS positions: A comparison of data products and processing methods for the contiguous U.S. and Alaska

Monday, 14 December 2020: 16:08
Virtual
Hilary R Martens1, Donald F Argus2, Cody Norberg3, Geoffrey Blewitt4, Thomas Herring5, Angelyn W Moore6, William C Hammond4, Corne Kreemer7 and Yehuda Bock8, (1)University of Montana, Geosciences, Missoula, MT, United States, (2)Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States, (3)University of Montana, Missoula, United States, (4)Nevada Geodetic Laboratory, Nevada Bureau of Mines and Geology, University of Nevada - Reno, Reno, NV, United States, (5)Massachusetts Institute of Technology, Department of Earth, Atmospheric and Planetary Sciences, Cambridge, MA, United States, (6)NASA Jet Propulsion Laboratory, Pasadena, CA, United States, (7)University of Nevada Reno, Nevada Bureau of Mines and Geology, Reno, NV, United States, (8)Scripps Institution of Oceanography, Institute of Geophysics and Planetary Physics, La Jolla, CA, United States
Recorded Presentation
Abstract:
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. We compare five GPS data products produced by three processing centers: the Nevada Geodetic Laboratory (NGL), Jet Propulsion Laboratory (JPL), and UNAVCO Consortium. We find that 5 to 30 per cent of the scatter in residual GPS time series (commonly considered noise) can be explained by atmospheric loading in the contiguous U.S. 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): median 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 amplitudes of seasonal oscillation. Newer products exhibit lower total scatter and stronger linear correlations between pairs of time series than older products, suggesting improved precision. Network-coherent differences in estimates of seasonal deformation reveal reference-frame inconsistencies between data products. As a next step, we extend our analysis to two sets of MEaSUREs time series, consisting of (1) positions estimated by Scripps using the GAMIT software and (2) a combination of the Scripps-GAMIT and JPL-GIPSY series.
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