Short note: the experimental geopotential model XGM2016

As a precursor study for the upcoming combined Earth Gravitational Model 2020 (EGM2020), the Experimental Gravity Field Model XGM2016, parameterized as a spherical harmonic series up to degree and order 719, is computed. XGM2016 shares the same combination methodology as its predecessor model GOCO05...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of geodesy 2018-04, Vol.92 (4), p.443-451
Hauptverfasser: Pail, R., Fecher, T., Barnes, D., Factor, J. F., Holmes, S. A., Gruber, T., Zingerle, P.
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 451
container_issue 4
container_start_page 443
container_title Journal of geodesy
container_volume 92
creator Pail, R.
Fecher, T.
Barnes, D.
Factor, J. F.
Holmes, S. A.
Gruber, T.
Zingerle, P.
description As a precursor study for the upcoming combined Earth Gravitational Model 2020 (EGM2020), the Experimental Gravity Field Model XGM2016, parameterized as a spherical harmonic series up to degree and order 719, is computed. XGM2016 shares the same combination methodology as its predecessor model GOCO05c (Fecher et al. in Surv Geophys 38(3): 571–590, 2017 . doi: 10.1007/s10712-016-9406-y ). The main difference between these models is that XGM2016 is supported by an improved terrestrial data set of 15 ′ × 15 ′ gravity anomaly area-means provided by the United States National Geospatial-Intelligence Agency (NGA), resulting in significant upgrades compared to existing combined gravity field models, especially in continental areas such as South America, Africa, parts of Asia, and Antarctica. A combination strategy of relative regional weighting provides for improved performance in near-coastal ocean regions, including regions where the altimetric data are mostly unchanged from previous models. Comparing cumulative height anomalies, from both EGM2008 and XGM2016 at degree/order 719, yields differences of 26 cm in Africa and 40 cm in South America. These differences result from including additional information of satellite data, as well as from the improved ground data in these regions. XGM2016 also yields a smoother Mean Dynamic Topography with significantly reduced artifacts, which indicates an improved modeling of the ocean areas.
doi_str_mv 10.1007/s00190-017-1070-6
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2015464480</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2015464480</sourcerecordid><originalsourceid>FETCH-LOGICAL-c382t-be93ae1e503ab98e577df9423b6ff4bfdbefc98340a406cab3b38474c8db3d703</originalsourceid><addsrcrecordid>eNp1kE9LxDAQxYMouK5-AG8Fz9FJkyaNN1l0FVY8qOAtJO1k_7Db1iQL-u3NUsGTp2GY35uZ9wi5ZHDNANRNBGAaKDBFGSig8ohMmOAlZVyLYzIBLTRViolTchbjJtOqquWEqNdVH1LR9Qlvi7TCAr8GDOsddsluiyX2Q550aZ2bXd_itviYP5fA5Dk58XYb8eK3Tsn7w_3b7JEuXuZPs7sFbXhdJupQc4sMK-DW6RorpVqvRcmd9F443zr0ja65ACtANtZxx2uhRFO3jrcK-JRcjXuH0H_uMSaz6fehyydNfqMSUoj6QLGRakIfY0BvhuzBhm_DwBzyMWM-Jts2h3yMzJpy1MTMdksMf5v_F_0AClFm9g</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2015464480</pqid></control><display><type>article</type><title>Short note: the experimental geopotential model XGM2016</title><source>Springer Nature - Complete Springer Journals</source><creator>Pail, R. ; Fecher, T. ; Barnes, D. ; Factor, J. F. ; Holmes, S. A. ; Gruber, T. ; Zingerle, P.</creator><creatorcontrib>Pail, R. ; Fecher, T. ; Barnes, D. ; Factor, J. F. ; Holmes, S. A. ; Gruber, T. ; Zingerle, P.</creatorcontrib><description>As a precursor study for the upcoming combined Earth Gravitational Model 2020 (EGM2020), the Experimental Gravity Field Model XGM2016, parameterized as a spherical harmonic series up to degree and order 719, is computed. XGM2016 shares the same combination methodology as its predecessor model GOCO05c (Fecher et al. in Surv Geophys 38(3): 571–590, 2017 . doi: 10.1007/s10712-016-9406-y ). The main difference between these models is that XGM2016 is supported by an improved terrestrial data set of 15 ′ × 15 ′ gravity anomaly area-means provided by the United States National Geospatial-Intelligence Agency (NGA), resulting in significant upgrades compared to existing combined gravity field models, especially in continental areas such as South America, Africa, parts of Asia, and Antarctica. A combination strategy of relative regional weighting provides for improved performance in near-coastal ocean regions, including regions where the altimetric data are mostly unchanged from previous models. Comparing cumulative height anomalies, from both EGM2008 and XGM2016 at degree/order 719, yields differences of 26 cm in Africa and 40 cm in South America. These differences result from including additional information of satellite data, as well as from the improved ground data in these regions. XGM2016 also yields a smoother Mean Dynamic Topography with significantly reduced artifacts, which indicates an improved modeling of the ocean areas.</description><identifier>ISSN: 0949-7714</identifier><identifier>EISSN: 1432-1394</identifier><identifier>DOI: 10.1007/s00190-017-1070-6</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Anomalies ; Data ; Dynamic height ; Dynamic topography ; Earth ; Earth and Environmental Science ; Earth Sciences ; Geodetics ; Geophysics/Geodesy ; Gravitational fields ; Gravity ; Gravity field ; Height anomalies ; Modelling ; Oceans ; Regions ; Satellite data ; Satellites ; Short Note ; Slope ; Topography ; Topography (geology)</subject><ispartof>Journal of geodesy, 2018-04, Vol.92 (4), p.443-451</ispartof><rights>Springer-Verlag GmbH Germany 2017</rights><rights>Journal of Geodesy is a copyright of Springer, (2017). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c382t-be93ae1e503ab98e577df9423b6ff4bfdbefc98340a406cab3b38474c8db3d703</citedby><cites>FETCH-LOGICAL-c382t-be93ae1e503ab98e577df9423b6ff4bfdbefc98340a406cab3b38474c8db3d703</cites><orcidid>0000-0002-4364-4012</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00190-017-1070-6$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00190-017-1070-6$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Pail, R.</creatorcontrib><creatorcontrib>Fecher, T.</creatorcontrib><creatorcontrib>Barnes, D.</creatorcontrib><creatorcontrib>Factor, J. F.</creatorcontrib><creatorcontrib>Holmes, S. A.</creatorcontrib><creatorcontrib>Gruber, T.</creatorcontrib><creatorcontrib>Zingerle, P.</creatorcontrib><title>Short note: the experimental geopotential model XGM2016</title><title>Journal of geodesy</title><addtitle>J Geod</addtitle><description>As a precursor study for the upcoming combined Earth Gravitational Model 2020 (EGM2020), the Experimental Gravity Field Model XGM2016, parameterized as a spherical harmonic series up to degree and order 719, is computed. XGM2016 shares the same combination methodology as its predecessor model GOCO05c (Fecher et al. in Surv Geophys 38(3): 571–590, 2017 . doi: 10.1007/s10712-016-9406-y ). The main difference between these models is that XGM2016 is supported by an improved terrestrial data set of 15 ′ × 15 ′ gravity anomaly area-means provided by the United States National Geospatial-Intelligence Agency (NGA), resulting in significant upgrades compared to existing combined gravity field models, especially in continental areas such as South America, Africa, parts of Asia, and Antarctica. A combination strategy of relative regional weighting provides for improved performance in near-coastal ocean regions, including regions where the altimetric data are mostly unchanged from previous models. Comparing cumulative height anomalies, from both EGM2008 and XGM2016 at degree/order 719, yields differences of 26 cm in Africa and 40 cm in South America. These differences result from including additional information of satellite data, as well as from the improved ground data in these regions. XGM2016 also yields a smoother Mean Dynamic Topography with significantly reduced artifacts, which indicates an improved modeling of the ocean areas.</description><subject>Anomalies</subject><subject>Data</subject><subject>Dynamic height</subject><subject>Dynamic topography</subject><subject>Earth</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Geodetics</subject><subject>Geophysics/Geodesy</subject><subject>Gravitational fields</subject><subject>Gravity</subject><subject>Gravity field</subject><subject>Height anomalies</subject><subject>Modelling</subject><subject>Oceans</subject><subject>Regions</subject><subject>Satellite data</subject><subject>Satellites</subject><subject>Short Note</subject><subject>Slope</subject><subject>Topography</subject><subject>Topography (geology)</subject><issn>0949-7714</issn><issn>1432-1394</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp1kE9LxDAQxYMouK5-AG8Fz9FJkyaNN1l0FVY8qOAtJO1k_7Db1iQL-u3NUsGTp2GY35uZ9wi5ZHDNANRNBGAaKDBFGSig8ohMmOAlZVyLYzIBLTRViolTchbjJtOqquWEqNdVH1LR9Qlvi7TCAr8GDOsddsluiyX2Q550aZ2bXd_itviYP5fA5Dk58XYb8eK3Tsn7w_3b7JEuXuZPs7sFbXhdJupQc4sMK-DW6RorpVqvRcmd9F443zr0ja65ACtANtZxx2uhRFO3jrcK-JRcjXuH0H_uMSaz6fehyydNfqMSUoj6QLGRakIfY0BvhuzBhm_DwBzyMWM-Jts2h3yMzJpy1MTMdksMf5v_F_0AClFm9g</recordid><startdate>20180401</startdate><enddate>20180401</enddate><creator>Pail, R.</creator><creator>Fecher, T.</creator><creator>Barnes, D.</creator><creator>Factor, J. F.</creator><creator>Holmes, S. A.</creator><creator>Gruber, T.</creator><creator>Zingerle, P.</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7TG</scope><scope>7TN</scope><scope>7XB</scope><scope>88I</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>GNUQQ</scope><scope>H96</scope><scope>HCIFZ</scope><scope>KL.</scope><scope>L.G</scope><scope>M2P</scope><scope>PCBAR</scope><scope>PHGZM</scope><scope>PHGZT</scope><scope>PKEHL</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><orcidid>https://orcid.org/0000-0002-4364-4012</orcidid></search><sort><creationdate>20180401</creationdate><title>Short note: the experimental geopotential model XGM2016</title><author>Pail, R. ; Fecher, T. ; Barnes, D. ; Factor, J. F. ; Holmes, S. A. ; Gruber, T. ; Zingerle, P.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c382t-be93ae1e503ab98e577df9423b6ff4bfdbefc98340a406cab3b38474c8db3d703</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Anomalies</topic><topic>Data</topic><topic>Dynamic height</topic><topic>Dynamic topography</topic><topic>Earth</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Geodetics</topic><topic>Geophysics/Geodesy</topic><topic>Gravitational fields</topic><topic>Gravity</topic><topic>Gravity field</topic><topic>Height anomalies</topic><topic>Modelling</topic><topic>Oceans</topic><topic>Regions</topic><topic>Satellite data</topic><topic>Satellites</topic><topic>Short Note</topic><topic>Slope</topic><topic>Topography</topic><topic>Topography (geology)</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Pail, R.</creatorcontrib><creatorcontrib>Fecher, T.</creatorcontrib><creatorcontrib>Barnes, D.</creatorcontrib><creatorcontrib>Factor, J. F.</creatorcontrib><creatorcontrib>Holmes, S. A.</creatorcontrib><creatorcontrib>Gruber, T.</creatorcontrib><creatorcontrib>Zingerle, P.</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Meteorological &amp; Geoastrophysical Abstracts</collection><collection>Oceanic Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>Earth, Atmospheric &amp; Aquatic Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>ProQuest Central Student</collection><collection>Aquatic Science &amp; Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy &amp; Non-Living Resources</collection><collection>SciTech Premium Collection</collection><collection>Meteorological &amp; Geoastrophysical Abstracts - Academic</collection><collection>Aquatic Science &amp; Fisheries Abstracts (ASFA) Professional</collection><collection>Science Database</collection><collection>Earth, Atmospheric &amp; Aquatic Science Database</collection><collection>ProQuest Central (New)</collection><collection>ProQuest One Academic (New)</collection><collection>ProQuest One Academic Middle East (New)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><jtitle>Journal of geodesy</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Pail, R.</au><au>Fecher, T.</au><au>Barnes, D.</au><au>Factor, J. F.</au><au>Holmes, S. A.</au><au>Gruber, T.</au><au>Zingerle, P.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Short note: the experimental geopotential model XGM2016</atitle><jtitle>Journal of geodesy</jtitle><stitle>J Geod</stitle><date>2018-04-01</date><risdate>2018</risdate><volume>92</volume><issue>4</issue><spage>443</spage><epage>451</epage><pages>443-451</pages><issn>0949-7714</issn><eissn>1432-1394</eissn><abstract>As a precursor study for the upcoming combined Earth Gravitational Model 2020 (EGM2020), the Experimental Gravity Field Model XGM2016, parameterized as a spherical harmonic series up to degree and order 719, is computed. XGM2016 shares the same combination methodology as its predecessor model GOCO05c (Fecher et al. in Surv Geophys 38(3): 571–590, 2017 . doi: 10.1007/s10712-016-9406-y ). The main difference between these models is that XGM2016 is supported by an improved terrestrial data set of 15 ′ × 15 ′ gravity anomaly area-means provided by the United States National Geospatial-Intelligence Agency (NGA), resulting in significant upgrades compared to existing combined gravity field models, especially in continental areas such as South America, Africa, parts of Asia, and Antarctica. A combination strategy of relative regional weighting provides for improved performance in near-coastal ocean regions, including regions where the altimetric data are mostly unchanged from previous models. Comparing cumulative height anomalies, from both EGM2008 and XGM2016 at degree/order 719, yields differences of 26 cm in Africa and 40 cm in South America. These differences result from including additional information of satellite data, as well as from the improved ground data in these regions. XGM2016 also yields a smoother Mean Dynamic Topography with significantly reduced artifacts, which indicates an improved modeling of the ocean areas.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s00190-017-1070-6</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0002-4364-4012</orcidid></addata></record>
fulltext fulltext
identifier ISSN: 0949-7714
ispartof Journal of geodesy, 2018-04, Vol.92 (4), p.443-451
issn 0949-7714
1432-1394
language eng
recordid cdi_proquest_journals_2015464480
source Springer Nature - Complete Springer Journals
subjects Anomalies
Data
Dynamic height
Dynamic topography
Earth
Earth and Environmental Science
Earth Sciences
Geodetics
Geophysics/Geodesy
Gravitational fields
Gravity
Gravity field
Height anomalies
Modelling
Oceans
Regions
Satellite data
Satellites
Short Note
Slope
Topography
Topography (geology)
title Short note: the experimental geopotential model XGM2016
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-19T21%3A34%3A33IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Short%20note:%20the%20experimental%20geopotential%20model%20XGM2016&rft.jtitle=Journal%20of%20geodesy&rft.au=Pail,%20R.&rft.date=2018-04-01&rft.volume=92&rft.issue=4&rft.spage=443&rft.epage=451&rft.pages=443-451&rft.issn=0949-7714&rft.eissn=1432-1394&rft_id=info:doi/10.1007/s00190-017-1070-6&rft_dat=%3Cproquest_cross%3E2015464480%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2015464480&rft_id=info:pmid/&rfr_iscdi=true