Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/100672
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dc.contributorDepartment of Land Surveying and Geo-Informatics-
dc.creatorLi, Zen_US
dc.creatorChen, Wen_US
dc.creatorVan Dam, Ten_US
dc.creatorRebischung, Pen_US
dc.creatorAltamimi, Zen_US
dc.date.accessioned2023-08-11T03:12:32Z-
dc.date.available2023-08-11T03:12:32Z-
dc.identifier.issn0949-7714en_US
dc.identifier.urihttp://hdl.handle.net/10397/100672-
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.rights© Springer-Verlag GmbH Germany, part of Springer Nature 2020en_US
dc.rightsThis version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use(https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms), but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s00190-020-01370-yen_US
dc.subjectAtmospheric pressure loading (ATML)en_US
dc.subjectERA-Interimen_US
dc.subjectGNSS position time seriesen_US
dc.subjectMERRAen_US
dc.subjectMERRA-2en_US
dc.subjectNCEP reanalysisen_US
dc.titleComparative analysis of different atmospheric surface pressure models and their impacts on daily ITRF2014 GNSS residual time seriesen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume94en_US
dc.identifier.issue4en_US
dc.identifier.doi10.1007/s00190-020-01370-yen_US
dcterms.abstractTo remove atmospheric pressure loading (ATML) effect from GNSS coordinate time series, surface pressure (SP) models are required to predict the displacements. In this paper, we modeled the 3D ATML surface displacements using the latest MERRA-2 SP grids, together with four other products (NCEP-R-1, NCEP-R-2, ERA-Interim and MERRA) for 596 globally distributed GNSS stations, and compared them with ITRF2014 residual time series. The five sets of ATML displacements are highly consistent with each other, particularly for those stations far away from coasts, of which the lowest correlations in the Up component for all the four models w.r.t MERRA-2 become larger than 0.91. ERA-Interim-derived ATML displacement performs best in reducing scatter of the GNSS height for 90.3% of the stations (89.3% for NCEP-R-1, 89.1% for NCEP-R-2, 86.4% for MERRA and 85.1% for MERRA-2). We think that this may be possibly due to the 4D variational data assimilation method applied. Considering inland stations only, more than 96% exhibit WRMS reduction in the Up direction for all five models, with an average improvement of 3–4% compared with the original ITRF2014 residual time series before ATML correction. Most stations (> 67%) also exhibit horizontal WRMS reductions based on the five models, but of small magnitudes, with most improvements (> 76%) less than 5%. In particular, most stations in South America, South Africa, Oceania and the Southern Oceans show larger WRMS reductions with MERRA-2, while all other four SP datasets lead to larger WRMS reduction for the Up component than MERRA-2 in Europe. Through comparison of the daily pressure variation from the five SP models, we conclude that the bigger model differences in the SP-induced surface displacements and their impacts on the ITRF2014 residuals for coastal/island stations are mainly due to the IB correction based on the different land–sea masks. A unique high spatial resolution land–sea mask should be applied in the future, so that model differences would come from only SP grids. Further research is also required to compare the ATML effect in ice-covered and high mountainous regions, for example the Qinghai–Tibet Plateau in China, the Andes in South America, etc., where larger pressure differences between models tend to occur.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationJournal of geodesy, Apr. 2020, v. 94, no. 4, 42en_US
dcterms.isPartOfJournal of geodesyen_US
dcterms.issued2020-04-
dc.identifier.scopus2-s2.0-85082430893-
dc.identifier.eissn1432-1394en_US
dc.identifier.artn42en_US
dc.description.validate202305 bckw-
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumberLSGI-0111-
dc.description.fundingSourceRGCen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextNational Key Research and Development Program of China; National Science Foundation for Distinguished Young Scholars of Chinaen_US
dc.description.pubStatusPublisheden_US
dc.identifier.OPUS52662451-
dc.description.oaCategoryGreen (AAM)en_US
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