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/2008/04", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Earth Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0401", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Atmospheric Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Centre for Oceans, Rivers, Atmosphere and Land Science, Indian Institute of Technology, Kharagpur, 721302, Kharagpur, West Bengal, India", 
          "id": "http://www.grid.ac/institutes/grid.429017.9", 
          "name": [
            "National Centre for Sustainable Coastal Management, Ministry of Environment, Forest and Climate Change, Anna University Campus, 600025, Chennai, India", 
            "Centre for Oceans, Rivers, Atmosphere and Land Science, Indian Institute of Technology, Kharagpur, 721302, Kharagpur, West Bengal, India"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Singh", 
        "givenName": "K. S.", 
        "id": "sg:person.07745337075.57", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07745337075.57"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Centre for Oceans, Rivers, Atmosphere and Land Science, Indian Institute of Technology, Kharagpur, 721302, Kharagpur, West Bengal, India", 
          "id": "http://www.grid.ac/institutes/grid.429017.9", 
          "name": [
            "Centre for Oceans, Rivers, Atmosphere and Land Science, Indian Institute of Technology, Kharagpur, 721302, Kharagpur, West Bengal, India"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Mandal", 
        "givenName": "M.", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Ocean Engineering and Naval Architecture, Indian Institute of Technology, Kharagpur, 721302, Kharagpur, West Bengal, India", 
          "id": "http://www.grid.ac/institutes/grid.429017.9", 
          "name": [
            "Ocean Engineering and Naval Architecture, Indian Institute of Technology, Kharagpur, 721302, Kharagpur, West Bengal, India"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Bhaskaran", 
        "givenName": "Prasad K.", 
        "id": "sg:person.015360732105.04", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015360732105.04"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/s11069-009-9397-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014139426", 
          "https://doi.org/10.1007/s11069-009-9397-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00703-013-0276-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023177777", 
          "https://doi.org/10.1007/s00703-013-0276-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00703-011-0127-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004348801", 
          "https://doi.org/10.1007/s00703-011-0127-y"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00703-011-0161-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048776705", 
          "https://doi.org/10.1007/s00703-011-0161-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00024-015-1206-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008379460", 
          "https://doi.org/10.1007/s00024-015-1206-7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00703-010-0102-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007563908", 
          "https://doi.org/10.1007/s00703-010-0102-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11069-012-0364-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013635534", 
          "https://doi.org/10.1007/s11069-012-0364-5"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11069-010-9700-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002073427", 
          "https://doi.org/10.1007/s11069-010-9700-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00024-011-0379-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014864475", 
          "https://doi.org/10.1007/s00024-011-0379-y"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2017-09-15", 
    "datePublishedReg": "2017-09-15", 
    "description": "This study attempts to investigate the impact of assimilation of satellite radiances and its role to improve the model initial condition and forecast of Bay of Bengal cyclone \u2018Sidr\u2019 by using the weather research and forecasting model and its three-dimensional variational data assimilation system. Results signify that the assimilation of high-resolution satellite radiances of advanced microwave sounding unit B (AMSU-B) data at peak channels has led to significant improvement in the initial fields of the storm structure. It improved the initial condition of moisture profile more significantly than the temperature profile, when radiances are assimilated into the model. In addition, the assimilation of AMSU-B showed a more positive impact on the prediction of the track and intensity of the storm than the assimilation of radiances of advanced microwave sounding unit A (AMSU-A) and high-resolution infra-red sounder (HIRS). The assimilation exercise with all observations (NCEP PREBUFR, AMSU-A, AMSU-B, HIRS, microwave humidity sounder, and atmospheric infra-red sounder) indicate that the track errors are reduced by about 46, 62, 90, and 86%, respectively, at 24, 48, 72, and 96\u00a0h forecasts compared to the experiment considering without data assimilation. The landfall, intensity, and structure of storm are well captured when all observations are assimilated into the model. Overall, it is concluded that assimilation of radiances is beneficial for the analysis and forecast of the storm. The results suggest that assimilating of both NCEP PREBUFR and radiance observations into the mesoscale model improves the initial condition and forecast of the storm.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s00703-017-0552-7", 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1271293", 
        "issn": [
          "0177-7971", 
          "1436-5065"
        ], 
        "name": "Meteorology and Atmospheric Physics", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "131"
      }
    ], 
    "keywords": [
      "assimilation of radiances", 
      "satellite radiances", 
      "data assimilation", 
      "advanced microwave", 
      "three-dimensional variational data assimilation system", 
      "variational data assimilation system", 
      "impact of assimilation", 
      "data assimilation system", 
      "radiance data assimilation", 
      "model initial conditions", 
      "Bengal cyclones", 
      "Weather Research", 
      "storm structure", 
      "assimilation exercise", 
      "assimilation system", 
      "h forecasts", 
      "radiance observations", 
      "mesoscale model", 
      "modelling system", 
      "initial conditions", 
      "moisture profiles", 
      "track error", 
      "unit A", 
      "storms", 
      "forecasting model", 
      "radiance", 
      "initial field", 
      "assimilation", 
      "forecasts", 
      "Sidr", 
      "B data", 
      "temperature profiles", 
      "AMSU", 
      "cyclones", 
      "landfall", 
      "sounder", 
      "Bay", 
      "impact", 
      "peak channel", 
      "intensity", 
      "track", 
      "model", 
      "profile", 
      "conditions", 
      "prediction", 
      "data", 
      "channels", 
      "microwave", 
      "error", 
      "structure", 
      "field", 
      "results", 
      "system", 
      "prediction performance", 
      "analysis", 
      "positive impact", 
      "experiments", 
      "study", 
      "addition", 
      "role", 
      "significant improvement", 
      "research", 
      "performance", 
      "improvement", 
      "observations", 
      "exercise"
    ], 
    "name": "Impact of radiance data assimilation on the prediction performance of cyclonic storm SIDR using WRF-3DVAR modelling system", 
    "pagination": "11-28", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1091778101"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s00703-017-0552-7"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s00703-017-0552-7", 
      "https://app.dimensions.ai/details/publication/pub.1091778101"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-08-04T17:06", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220804/entities/gbq_results/article/article_744.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s00703-017-0552-7"
  }
]

Download the RDF metadata as:  json-ld nt turtle xml License info

This table displays all metadata directly associated to this object as RDF triples.

175 TRIPLES      21 PREDICATES      99 URIs      82 LITERALS      6 BLANK NODES