Ontology type: schema:ScholarlyArticle
2013-03
AUTHORSJarir Saleh, Xiaopeng Li, Yan Ming Wang, Daniel R. Roman, Dru A. Smith
ABSTRACTAre the National Geodetic Survey’s surface gravity data sufficient for supporting the computation of a 1 cm-accurate geoid? This paper attempts to answer this question by deriving a few measures of accuracy for this data and estimating their effects on the US geoid. We use a data set which comprises million gravity observations collected in 1,489 surveys. Comparisons to GRACE-derived gravity and geoid are made to estimate the long-wavelength errors. Crossover analysis and -nearest neighbor predictions are used for estimating local gravity biases and high-frequency gravity errors, and the corresponding geoid biases and high-frequency geoid errors are evaluated. Results indicate that 244 of all 1,489 surface gravity surveys have significant biases mGal, with geoid implications that reach 20 cm. Some of the biased surveys are large enough in horizontal extent to be reliably corrected by satellite-derived gravity models, but many others are not. In addition, the results suggest that the data are contaminated by high-frequency errors with an RMS of mGal. This causes high-frequency geoid errors of a few centimeters in and to the west of the Rocky Mountains and in the Appalachians and a few millimeters or less everywhere else. Finally, long-wavelength () surface gravity errors on the sub-mGal level but with large horizontal extent are found. All of the south and southeast of the USA is biased by +0.3 to +0.8 mGal and the Rocky Mountains by to mGal. These small but extensive gravity errors lead to long-wavelength geoid errors that reach 60 cm in the interior of the USA. More... »
PAGES203-221
http://scigraph.springernature.com/pub.10.1007/s00190-012-0589-9
DOIhttp://dx.doi.org/10.1007/s00190-012-0589-9
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1026491938
JSON-LD is the canonical representation for SciGraph data.
TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT
[
{
"@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json",
"about": [
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0909",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Geomatic Engineering",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/09",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Engineering",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "Earth Resources Technology (United States)",
"id": "https://www.grid.ac/institutes/grid.486861.5",
"name": [
"Earth Resources Technology, 1315 East-West Hwy, 20910, Silver Spring, MD, USA"
],
"type": "Organization"
},
"familyName": "Saleh",
"givenName": "Jarir",
"id": "sg:person.011760546617.79",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011760546617.79"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Earth Resources Technology (United States)",
"id": "https://www.grid.ac/institutes/grid.486861.5",
"name": [
"Earth Resources Technology, 1315 East-West Hwy, 20910, Silver Spring, MD, USA"
],
"type": "Organization"
},
"familyName": "Li",
"givenName": "Xiaopeng",
"id": "sg:person.013071233601.93",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013071233601.93"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "National Geodetic Survey",
"id": "https://www.grid.ac/institutes/grid.410486.f",
"name": [
"National Geodetic Survey, 1315 East-West Hwy, 20910, Silver Spring, MD, USA"
],
"type": "Organization"
},
"familyName": "Wang",
"givenName": "Yan Ming",
"id": "sg:person.011537241004.41",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011537241004.41"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "National Geodetic Survey",
"id": "https://www.grid.ac/institutes/grid.410486.f",
"name": [
"National Geodetic Survey, 1315 East-West Hwy, 20910, Silver Spring, MD, USA"
],
"type": "Organization"
},
"familyName": "Roman",
"givenName": "Daniel R.",
"id": "sg:person.012560102507.28",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012560102507.28"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "National Geodetic Survey",
"id": "https://www.grid.ac/institutes/grid.410486.f",
"name": [
"National Geodetic Survey, 1315 East-West Hwy, 20910, Silver Spring, MD, USA"
],
"type": "Organization"
},
"familyName": "Smith",
"givenName": "Dru A.",
"id": "sg:person.016654516201.56",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016654516201.56"
],
"type": "Person"
}
],
"citation": [
{
"id": "sg:pub.10.1007/s001900100200",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1000191258",
"https://doi.org/10.1007/s001900100200"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/3-540-27432-4_42",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1003851092",
"https://doi.org/10.1007/3-540-27432-4_42"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s001900050080",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1009669842",
"https://doi.org/10.1007/s001900050080"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/s1464-1895(98)00007-6",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1011726135"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s001900050239",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1012134722",
"https://doi.org/10.1007/s001900050239"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1111/j.1365-246x.2008.03924.x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1013123221"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1029/jb093ib01p00393",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1017944246"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00190-011-0506-7",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1018357342",
"https://doi.org/10.1007/s00190-011-0506-7"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1029/2011jb008916",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1019715144"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-540-85426-5_21",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1021594064",
"https://doi.org/10.1007/978-3-540-85426-5_21"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-540-85426-5_21",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1021594064",
"https://doi.org/10.1007/978-3-540-85426-5_21"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00190-009-0355-9",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1023077557",
"https://doi.org/10.1007/s00190-009-0355-9"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00190-009-0355-9",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1023077557",
"https://doi.org/10.1007/s00190-009-0355-9"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00190-009-0355-9",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1023077557",
"https://doi.org/10.1007/s00190-009-0355-9"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.2478/v10156-010-0016-1",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1024677199"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1111/j.1365-2478.2011.01017.x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1028658099"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.14358/pers.72.3.237",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1031223963"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1111/j.1365-246x.1969.tb00264.x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1034254458"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s001900000129",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1037381833",
"https://doi.org/10.1007/s001900000129"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/bf02530617",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1041434815",
"https://doi.org/10.1007/bf02530617"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00190-010-0422-2",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1044093144",
"https://doi.org/10.1007/s00190-010-0422-2"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1126/science.220.4598.671",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1062526985"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.21236/ada150788",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1091744048"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1017/cbo9780511804120",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1098678499"
],
"type": "CreativeWork"
}
],
"datePublished": "2013-03",
"datePublishedReg": "2013-03-01",
"description": "Are the National Geodetic Survey\u2019s surface gravity data sufficient for supporting the computation of a 1 cm-accurate geoid? This paper attempts to answer this question by deriving a few measures of accuracy for this data and estimating their effects on the US geoid. We use a data set which comprises million gravity observations collected in 1,489 surveys. Comparisons to GRACE-derived gravity and geoid are made to estimate the long-wavelength errors. Crossover analysis and -nearest neighbor predictions are used for estimating local gravity biases and high-frequency gravity errors, and the corresponding geoid biases and high-frequency geoid errors are evaluated. Results indicate that 244 of all 1,489 surface gravity surveys have significant biases mGal, with geoid implications that reach 20 cm. Some of the biased surveys are large enough in horizontal extent to be reliably corrected by satellite-derived gravity models, but many others are not. In addition, the results suggest that the data are contaminated by high-frequency errors with an RMS of mGal. This causes high-frequency geoid errors of a few centimeters in and to the west of the Rocky Mountains and in the Appalachians and a few millimeters or less everywhere else. Finally, long-wavelength () surface gravity errors on the sub-mGal level but with large horizontal extent are found. All of the south and southeast of the USA is biased by +0.3 to +0.8 mGal and the Rocky Mountains by to mGal. These small but extensive gravity errors lead to long-wavelength geoid errors that reach 60 cm in the interior of the USA.",
"genre": "research_article",
"id": "sg:pub.10.1007/s00190-012-0589-9",
"inLanguage": [
"en"
],
"isAccessibleForFree": false,
"isPartOf": [
{
"id": "sg:journal.1052480",
"issn": [
"0949-7714",
"1432-1394"
],
"name": "Journal of Geodesy",
"type": "Periodical"
},
{
"issueNumber": "3",
"type": "PublicationIssue"
},
{
"type": "PublicationVolume",
"volumeNumber": "87"
}
],
"name": "Error analysis of the NGS\u2019 surface gravity database",
"pagination": "203-221",
"productId": [
{
"name": "readcube_id",
"type": "PropertyValue",
"value": [
"7e3586f5f8aded44cde1e519d25480c65afd9a311446f16febb3fdc1e2cd2cf2"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/s00190-012-0589-9"
]
},
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1026491938"
]
}
],
"sameAs": [
"https://doi.org/10.1007/s00190-012-0589-9",
"https://app.dimensions.ai/details/publication/pub.1026491938"
],
"sdDataset": "articles",
"sdDatePublished": "2019-04-10T18:20",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8675_00000513.jsonl",
"type": "ScholarlyArticle",
"url": "http://link.springer.com/10.1007%2Fs00190-012-0589-9"
}
]Download the RDF metadata as: json-ld nt turtle xml License info
JSON-LD is a popular format for linked data which is fully compatible with JSON.
curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s00190-012-0589-9'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s00190-012-0589-9'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00190-012-0589-9'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00190-012-0589-9'
This table displays all metadata directly associated to this object as RDF triples.
165 TRIPLES
21 PREDICATES
48 URIs
19 LITERALS
7 BLANK NODES