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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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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 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.

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Acknowledgements

We are grateful to Paul Ries for determining satellite orbits and clocks in JPL Repro 3.0, the basis of JPL’s and NGL’s latest solutions analyzed herein. Michael Heflin contributed to determining JPL’s GPS solutions and creating the new GipsyX software. We gratefully acknowledge the insightful feedback from two anonymous reviewers, reviewer Adrian Borsa, and the associate editor, which strengthened the manuscript. Part of this research is performed at Jet Propulsion Laboratory, California Institute of Technology, under contract with NASA.

Funding

UNAVCO products used here are based on services provided by the GAGE Facility, operated by UNAVCO, Inc., with support from the National Science Foundation (NSF) and the National Aeronautics and Space Administration (NASA) under NSF Cooperative Agreement EAR-1724794. This research is funded in part by a NASA GNSS Remote Sensing Science Team Grant (NNH14ZDA001N-GNSS) and a NASA Earth Surface and Interior Grant (NNH18ZDA001N-ESI). Research at the University of Montana was additionally funded in part by NASA Earth Surface and Interior Grant 80NSCC19K0361 and NASA Project NNX15AK40A through the Montana Space Grant Consortium. Research at University of Nevada, Reno, was funded by NASA Earth Surface and Interior Grant 80NSSC19K1044. GB, WCH, and CK also received support from NASA Project NNX16AK89G.

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Authors and Affiliations

  1. Department of Geosciences, University of Montana, Missoula, USA

    Hilary R. Martens

  2. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, USA

    Donald F. Argus & Angelyn W. Moore

  3. Department of Physics and Astronomy, University of Montana, Missoula, USA

    Cody Norberg

  4. Nevada Geodetic Laboratory, Nevada Bureau of Mines and Geology, University of Nevada, Reno, USA

    Geoffrey Blewitt, William C. Hammond & Corné Kreemer

  5. Department of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, USA

    Thomas A. Herring

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  1. Hilary R. Martens
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  2. Donald F. Argus
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  3. Cody Norberg
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  4. Geoffrey Blewitt
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  6. Angelyn W. Moore
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  8. Corné Kreemer
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Contributions

H.R.M. and D.F.A. conceptualized and designed the study; H.R.M., D.F.A., and C.N. performed the formal analysis and investigation; G.B., W.C.H., and C.K. developed GPS data products and provided processing information from the Nevada Geodetic Laboratory; T.A.H. developed GPS data products and provided processing information from UNAVCO; and A.W.M. developed GPS data products and provided processing information from the Jet Propulsion Laboratory. All authors contributed to the interpretation of data and modeling results. H.R.M. wrote the first draft of the manuscript; all authors commented on this version of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Hilary R. Martens.

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Availability of data and material

GPS position series are available from the Nevada Geodetic Laboratory at http://geodesy.unr.edu/gps_timeseries/, from the Jet Propulsion Laboratory (post-point series) at https://sideshow.jpl.nasa.gov/pub/JPL_GPS_Timeseries/, and from UNAVCO at https://www.unavco.org/data/gps-gnss/derived-products/derived-products.html. Elastic displacements produced by changes in non-tidal atmospheric loading (as well as non-tidal oceanic and hydrologic loading) are available from GFZ German Research Centre for Geosciences at https://www.gfz-potsdam.de/en/esmdata/loading and at ftp://esmdata.gfz-potsdam.de/LOADING. The LoadDef software for modeling load-induced deformation is available from Martens et al. (2019).

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Martens, H.R., Argus, D.F., Norberg, C. et al. Atmospheric pressure loading in GPS positions: dependency on GPS processing methods and effect on assessment of seasonal deformation in the contiguous USA and Alaska. J Geod 94, 115 (2020). https://doi.org/10.1007/s00190-020-01445-w

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