Validation of the EGSIEM GRACE gravity fields using GNSS coordinate timeseries and in-situ ocean bottom pressure records

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Chen, Q.; Poropat, L.; Zhang, L.; Dobslaw, H.; Weigelt, M. et al.: Validation of the EGSIEM GRACE gravity fields using GNSS coordinate timeseries and in-situ ocean bottom pressure records. In: Remote Sensing 10 (2018), Nr. 12, 1976. DOI: https://doi.org/10.3390/rs10121976

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Over the 15 years of the Gravity Recovery and Climate Experiment (GRACE) mission, various data processing approaches were developed to derive time-series of global gravity fields based on sensor observations acquired from the two spacecrafts. In this paper, we compare GRACE-based mass anomalies provided by various processing groups against Global Navigation Satellite System (GNSS) station coordinate time-series and in-situ observations of ocean bottom pressure. In addition to the conventional GRACE-based global geopotential models from the main processing centers, we focus particularly on combined gravity field solutions generated within the Horizon2020 project European Gravity Service for Improved Emergency Management (EGSIEM). Although two validation techniques are fully independent from each other, it is demonstrated that they confirm each other to a large extent. Through the validation, we show that the EGSIEM combined long-term monthly solutions are comparable to CSR RL05 and ITSG2016, and better than the other three considered GRACE monthly solutions AIUB RL02, GFZ RL05a, and JPL RL05.1. Depending on the GNSS products, up to 25.6% mean Weighted Root-Mean-Square (WRMS) reduction is obtained when comparing GRACE to the ITRF2014 residuals over 236 GNSS stations. In addition, we also observe remarkable agreement at the annual period between GNSS and GRACE with up to 73% median WRMS reduction when comparing GRACE to the 312 EGSIEM-reprocessed GNSS time series. While the correspondence between GRACE and ocean bottom pressure data is overall much smaller due to lower signal to noise ratio over the oceans than over the continents, up to 50% agreement is found between them in some regions. The results fully confirm the conclusions found using GNSS.
License of this version: CC BY 4.0 Unported
Document Type: Article
Publishing status: publishedVersion
Issue Date: 2018
Appears in Collections:Fakultät für Bauingenieurwesen und Geodäsie

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pos. country downloads
total perc.
1 image of flag of Germany Germany 124 68.51%
2 image of flag of United States United States 27 14.92%
3 image of flag of China China 12 6.63%
4 image of flag of Poland Poland 2 1.10%
5 image of flag of Iran, Islamic Republic of Iran, Islamic Republic of 1 0.55%
6 image of flag of Ireland Ireland 1 0.55%
7 image of flag of Hong Kong Hong Kong 1 0.55%
8 image of flag of France France 1 0.55%
9 image of flag of Czech Republic Czech Republic 1 0.55%
10 image of flag of Bosnia and Herzegovina Bosnia and Herzegovina 1 0.55%
    other countries 10 5.52%

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