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    Colorado geoid computation experiment: overview and summary

    86781.pdf (3.621Mb)
    Access Status
    Open access
    Authors
    Wang, Y.M.
    Sánchez, L.
    Ågren, J.
    Huang, J.
    Forsberg, R.
    Abd-Elmotaal, H.A.
    Ahlgren, K.
    Barzaghi, R.
    Bašić, T.
    Carrion, D.
    Claessens, Sten
    Erol, B.
    Erol, S.
    Filmer, Mick
    Grigoriadis, V.N.
    Isik, M.S.
    Jiang, T.
    Koç, Ö.
    Krcmaric, J.
    Li, X.
    Liu, Q.
    Matsuo, K.
    Natsiopoulos, D.A.
    Novák, P.
    Pail, R.
    Pitoňák, M.
    Schmidt, M.
    Varga, M.
    Vergos, G.S.
    Véronneau, M.
    Willberg, M.
    Zingerle, P.
    Date
    2021
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Wang, Y.M. and Sánchez, L. and Ågren, J. and Huang, J. and Forsberg, R. and Abd-Elmotaal, H.A. and Ahlgren, K. et al. 2021. Colorado geoid computation experiment: overview and summary. Journal of Geodesy. 95 (12): Article No. 127.
    Source Title
    Journal of Geodesy
    DOI
    10.1007/s00190-021-01567-9
    ISSN
    0949-7714
    Faculty
    Faculty of Science and Engineering
    School
    School of Earth and Planetary Sciences (EPS)
    Remarks

    This is a post-peer-review, pre-copyedit version of an article published in Journal of Geodesy. The final authenticated version is available online at: https://doi.org/10.1007/s00190-021-01567-9

    URI
    http://hdl.handle.net/20.500.11937/86957
    Collection
    • Curtin Research Publications
    Abstract

    The primary objective of the 1-cm geoid experiment in Colorado (USA) is to compare the numerous geoid computation methods used by different groups around the world. This is intended to lay the foundations for tuning computation methods to achieve the sought after 1-cm accuracy, and also evaluate how this accuracy may be robustly assessed. In this experiment, (quasi)geoid models were computed using the same input data provided by the US National Geodetic Survey (NGS), but using different methodologies. The rugged mountainous study area (730 km × 560 km) in Colorado was chosen so as to accentuate any differences between the methodologies, and to take advantage of newly collected GPS/leveling data of the Geoid Slope Validation Survey 2017 (GSVS17) which are now available to be used as an accurate and independent test dataset. Fourteen groups from fourteen countries submitted a gravimetric geoid and a quasigeoid model in a 1′× 1′ grid for the study area, as well as geoid heights, height anomalies, and geopotential values at the 223 GSVS17 marks. This paper concentrates on the quasigeoid model comparison and evaluation, while the geopotential value investigations are presented as a separate paper (Sánchez et al. in J Geodesy 95(3):1. https://doi.org/10.1007/s00190-021-01481-0, 2021). Three comparisons are performed: the area comparison to show the model precision, the comparison with the GSVS17 data to estimate the relative accuracy of the models, and the differential quasigeoid (slope) comparison with GSVS17 to assess the relative accuracy of the height anomalies at different baseline lengths. The results show that the precision of the 1′ × 1′ models over the complete area is about 2 cm, while the accuracy estimates along the GSVS17 profile range from 1.2 cm to 3.4 cm. Considering that the GSVS17 does not pass the roughest terrain, we estimate that the quasigeoid can be computed with an accuracy of ~ 2 cm in Colorado. The slope comparisons show that RMS values of the differences vary from 2 to 8 cm in all baseline lengths. Although the 2-cm precision and 2-cm relative accuracy have been estimated in such a rugged region, the experiment has not reached the 1-cm accuracy goal. At this point, the different accuracy estimates are not a proof of the superiority of one methodology over another because the model precision and accuracy of the GSVS17-derived height anomalies are at a similar level. It appears that the differences are not primarily caused by differences in theory, but that they originate mostly from numerical computations and/or data processing techniques. Consequently, recommendations to improve the model precision toward the 1-cm accuracy are also given in this paper.

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