A neural network atmospheric model for hybrid coupled modelling View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2001-03

AUTHORS

Y. Tang, W. W. Hsieh, B. Tang, K. Haines

ABSTRACT

The possibility of using a nonlinear empirical atmospheric model for hybrid coupled atmosphere-ocean modelling has been examined by using a neural network (NN) model for predicting the contemporaneous wind stress field from the upper ocean state. Upper ocean heat content (HC) from a 6-layer ocean model was a better predictor of the wind stress than the (observed or modelled) sea surface temperature (SST). Our results showed that the NN model generally had slightly better skills in predicting the contemporaneous wind stress than the linear regression (LR) model in the off-equatorial tropical Pacific and in the eastern equatorial Pacific. When the wind stresses from the NN and LR models were used to drive the ocean model, slightly better SST skills were found in the off-equatorial tropical Pacific and in the eastern equatorial Pacific when the NN winds were used instead of the LR winds. Better skills for the model HC were found in the western and central equatorial Pacific when the NN winds were used instead of the LR winds. Why NN failed to show more significant improvement over LR in the equatorial Pacific for the wind stress and SST is probably because the relationship between the surface ocean and the atmosphere in the equatorial Pacific over the seasonal time scale is almost linear. More... »

PAGES

445-455

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s003820000119

DOI

http://dx.doi.org/10.1007/s003820000119

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1009215357


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

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/2008/0405", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Oceanography", 
        "type": "DefinedTerm"
      }, 
      {
        "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"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "University of British Columbia", 
          "id": "https://www.grid.ac/institutes/grid.17091.3e", 
          "name": [
            "Oceanography/EOS, University of British Columbia, Vancouver, B.C., Canada V6T 1Z4, CA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Tang", 
        "givenName": "Y.", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of British Columbia", 
          "id": "https://www.grid.ac/institutes/grid.17091.3e", 
          "name": [
            "Oceanography/EOS, University of British Columbia, Vancouver, B.C., Canada V6T 1Z4, CA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Hsieh", 
        "givenName": "W. W.", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of British Columbia", 
          "id": "https://www.grid.ac/institutes/grid.17091.3e", 
          "name": [
            "Oceanography/EOS, University of British Columbia, Vancouver, B.C., Canada V6T 1Z4, CA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Tang", 
        "givenName": "B.", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Edinburgh", 
          "id": "https://www.grid.ac/institutes/grid.4305.2", 
          "name": [
            "Department of Meteorology, University of Edinburgh, The King's Buildings, Edinburgh EH9\u20093JZ, UK, GB"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Haines", 
        "givenName": "K.", 
        "id": "sg:person.01052227550.65", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01052227550.65"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2001-03", 
    "datePublishedReg": "2001-03-01", 
    "description": "The possibility of using a nonlinear empirical atmospheric model for hybrid coupled atmosphere-ocean modelling has been examined by using a neural network (NN) model for predicting the contemporaneous wind stress field from the upper ocean state. Upper ocean heat content (HC) from a 6-layer ocean model was a better predictor of the wind stress than the (observed or modelled) sea surface temperature (SST). Our results showed that the NN model generally had slightly better skills in predicting the contemporaneous wind stress than the linear regression (LR) model in the off-equatorial tropical Pacific and in the eastern equatorial Pacific. When the wind stresses from the NN and LR models were used to drive the ocean model, slightly better SST skills were found in the off-equatorial tropical Pacific and in the eastern equatorial Pacific when the NN winds were used instead of the LR winds. Better skills for the model HC were found in the western and central equatorial Pacific when the NN winds were used instead of the LR winds. Why NN failed to show more significant improvement over LR in the equatorial Pacific for the wind stress and SST is probably because the relationship between the surface ocean and the atmosphere in the equatorial Pacific over the seasonal time scale is almost linear.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s003820000119", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1049631", 
        "issn": [
          "0930-7575", 
          "1432-0894"
        ], 
        "name": "Climate Dynamics", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "5-6", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "17"
      }
    ], 
    "name": "A neural network atmospheric model for hybrid coupled modelling", 
    "pagination": "445-455", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "2be6618aefaed1ddb92b70129e98648362654cd4d2092280b1fe9c9fae2ec037"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s003820000119"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1009215357"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s003820000119", 
      "https://app.dimensions.ai/details/publication/pub.1009215357"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T15:00", 
    "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_8663_00000510.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007%2Fs003820000119"
  }
]
 

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

HOW TO GET THIS DATA PROGRAMMATICALLY:

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/s003820000119'

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/s003820000119'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s003820000119'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s003820000119'


 

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

82 TRIPLES      20 PREDICATES      27 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s003820000119 schema:about anzsrc-for:04
2 anzsrc-for:0405
3 schema:author N1280dfb7028c42c39220ee6c19deb1f2
4 schema:datePublished 2001-03
5 schema:datePublishedReg 2001-03-01
6 schema:description The possibility of using a nonlinear empirical atmospheric model for hybrid coupled atmosphere-ocean modelling has been examined by using a neural network (NN) model for predicting the contemporaneous wind stress field from the upper ocean state. Upper ocean heat content (HC) from a 6-layer ocean model was a better predictor of the wind stress than the (observed or modelled) sea surface temperature (SST). Our results showed that the NN model generally had slightly better skills in predicting the contemporaneous wind stress than the linear regression (LR) model in the off-equatorial tropical Pacific and in the eastern equatorial Pacific. When the wind stresses from the NN and LR models were used to drive the ocean model, slightly better SST skills were found in the off-equatorial tropical Pacific and in the eastern equatorial Pacific when the NN winds were used instead of the LR winds. Better skills for the model HC were found in the western and central equatorial Pacific when the NN winds were used instead of the LR winds. Why NN failed to show more significant improvement over LR in the equatorial Pacific for the wind stress and SST is probably because the relationship between the surface ocean and the atmosphere in the equatorial Pacific over the seasonal time scale is almost linear.
7 schema:genre research_article
8 schema:inLanguage en
9 schema:isAccessibleForFree false
10 schema:isPartOf N06cabefad65f46f4b0236664bd42af15
11 N7910a1b4f2944b578647e63038882b55
12 sg:journal.1049631
13 schema:name A neural network atmospheric model for hybrid coupled modelling
14 schema:pagination 445-455
15 schema:productId N22151acc78b44013a64d224733c4ad3b
16 N4fdca29b4150486c9717623a8e54f19b
17 N6e54260bc6b344a9ad105322d6d18995
18 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009215357
19 https://doi.org/10.1007/s003820000119
20 schema:sdDatePublished 2019-04-10T15:00
21 schema:sdLicense https://scigraph.springernature.com/explorer/license/
22 schema:sdPublisher Ncec022fc00c1491791b4bb4b6de4fbac
23 schema:url http://link.springer.com/10.1007%2Fs003820000119
24 sgo:license sg:explorer/license/
25 sgo:sdDataset articles
26 rdf:type schema:ScholarlyArticle
27 N06cabefad65f46f4b0236664bd42af15 schema:volumeNumber 17
28 rdf:type schema:PublicationVolume
29 N1280dfb7028c42c39220ee6c19deb1f2 rdf:first Ne7c8253d585a43478cd9b01ecd524f38
30 rdf:rest N8d0b33ec78cc42068f7c3c06d1ae5dbb
31 N183b09c7d46b4c9db871bfede5878716 rdf:first sg:person.01052227550.65
32 rdf:rest rdf:nil
33 N1b1fca0d72b94b228d710f1df0d23ec3 rdf:first N2c505ac84e9f406fb2de40529c0de777
34 rdf:rest N183b09c7d46b4c9db871bfede5878716
35 N22151acc78b44013a64d224733c4ad3b schema:name dimensions_id
36 schema:value pub.1009215357
37 rdf:type schema:PropertyValue
38 N228c106652684b4fb36275b5f20245fe schema:affiliation https://www.grid.ac/institutes/grid.17091.3e
39 schema:familyName Hsieh
40 schema:givenName W. W.
41 rdf:type schema:Person
42 N2c505ac84e9f406fb2de40529c0de777 schema:affiliation https://www.grid.ac/institutes/grid.17091.3e
43 schema:familyName Tang
44 schema:givenName B.
45 rdf:type schema:Person
46 N4fdca29b4150486c9717623a8e54f19b schema:name readcube_id
47 schema:value 2be6618aefaed1ddb92b70129e98648362654cd4d2092280b1fe9c9fae2ec037
48 rdf:type schema:PropertyValue
49 N6e54260bc6b344a9ad105322d6d18995 schema:name doi
50 schema:value 10.1007/s003820000119
51 rdf:type schema:PropertyValue
52 N7910a1b4f2944b578647e63038882b55 schema:issueNumber 5-6
53 rdf:type schema:PublicationIssue
54 N8d0b33ec78cc42068f7c3c06d1ae5dbb rdf:first N228c106652684b4fb36275b5f20245fe
55 rdf:rest N1b1fca0d72b94b228d710f1df0d23ec3
56 Ncec022fc00c1491791b4bb4b6de4fbac schema:name Springer Nature - SN SciGraph project
57 rdf:type schema:Organization
58 Ne7c8253d585a43478cd9b01ecd524f38 schema:affiliation https://www.grid.ac/institutes/grid.17091.3e
59 schema:familyName Tang
60 schema:givenName Y.
61 rdf:type schema:Person
62 anzsrc-for:04 schema:inDefinedTermSet anzsrc-for:
63 schema:name Earth Sciences
64 rdf:type schema:DefinedTerm
65 anzsrc-for:0405 schema:inDefinedTermSet anzsrc-for:
66 schema:name Oceanography
67 rdf:type schema:DefinedTerm
68 sg:journal.1049631 schema:issn 0930-7575
69 1432-0894
70 schema:name Climate Dynamics
71 rdf:type schema:Periodical
72 sg:person.01052227550.65 schema:affiliation https://www.grid.ac/institutes/grid.4305.2
73 schema:familyName Haines
74 schema:givenName K.
75 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01052227550.65
76 rdf:type schema:Person
77 https://www.grid.ac/institutes/grid.17091.3e schema:alternateName University of British Columbia
78 schema:name Oceanography/EOS, University of British Columbia, Vancouver, B.C., Canada V6T 1Z4, CA
79 rdf:type schema:Organization
80 https://www.grid.ac/institutes/grid.4305.2 schema:alternateName University of Edinburgh
81 schema:name Department of Meteorology, University of Edinburgh, The King's Buildings, Edinburgh EH9 3JZ, UK, GB
82 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...