Prediction of Indian summer monsoon in short to medium range time scale with high resolution global forecast system (GFS) T574 ... View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2014-03

AUTHORS

V. R. Durai, S. K. Roy Bhowmik

ABSTRACT

Performance of national centers for environmental prediction based global forecast system (GFS) T574/L64 and GFS T382/L64 over Indian region has been evaluated for the summer monsoon season of 2011. The real-time model outputs are generated daily at India Meteorological Department, New Delhi for the forecasts up to 7 days. Verification of rainfall forecasts has been carried out against observed rainfall analysis. Performance of the model is also examined in terms of lower tropospheric wind circulation, vertical structure of specific humidity and precipitable water content. Case study of a monsoon depression is also illustrated. Results obtained show that, in general, both the GFS T382 and T574 forecasts are skillful to capture climatologically heavy rainfall regions. However, the accuracy in prediction of location and magnitude of rainfall fluctuates considerably. The verification results, at the spatial scale of 50 km resolution, in a regional spatial scale and country as a whole, in terms of continuous skill score, time series and categorical statistics, have demonstrated superiority of GFS T574 against T382 over Indian region. Both the model shows bias of lower tropospheric drying and upper tropospheric moistening. A bias of anti-cyclonic circulation in the lower tropospheric level lay over the central India, where rainfall as well as precipitable water content shows negative bias. Considerable differences between GFS T574 and T382 are noticed in the structure of model bias in terms of lower tropospheric wind circulation, vertical structure of specific humidity and precipitable water contents. The magnitude of error for these parameters increases with forecast lead time in both GFS T574 and T382. The results documented are expected to be useful to the forecasters, monsoon researchers and modeling community. More... »

PAGES

1527-1551

Journal

TITLE

Climate Dynamics

ISSUE

5-6

VOLUME

42

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00382-013-1895-5

DOI

http://dx.doi.org/10.1007/s00382-013-1895-5

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1027568127


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/2008/0401", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Atmospheric Sciences", 
        "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": "India Meteorological Department", 
          "id": "https://www.grid.ac/institutes/grid.466772.6", 
          "name": [
            "India Meteorological Department, 110003, New Delhi, India"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Durai", 
        "givenName": "V. R.", 
        "id": "sg:person.015364606563.26", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015364606563.26"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "India Meteorological Department", 
          "id": "https://www.grid.ac/institutes/grid.466772.6", 
          "name": [
            "India Meteorological Department, 110003, New Delhi, India"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Roy Bhowmik", 
        "givenName": "S. K.", 
        "id": "sg:person.016161404425.25", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016161404425.25"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1175/1520-0493(1992)120<1747:tnmcss>2.0.co;2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002851820"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/1520-0434(1989)004<0335:dotngd>2.0.co;2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005273922"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/1520-0477(1990)071<1410:gnwpat>2.0.co;2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006212940"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/2010mwr3456.1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008487725"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/1520-0434(2001)016<0697:eeonal>2.0.co;2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010695190"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/mwr3090.1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011920286"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/1520-0493(1997)125<0252:acsocp>2.0.co;2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012149301"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/1520-0434(1991)006<0425:rciitg>2.0.co;2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017206664"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/1520-0434(1990)005<0576:osmosi>2.0.co;2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018227500"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/1520-0434(2000)015<0103:voqpff>2.0.co;2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021763854"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/1520-0434(1990)005<0570:tcsiaa>2.0.co;2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029356469"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/1520-0434(2000)015<0257:oiotfc>2.0.co;2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029458286"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/2010bams3001.1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032678557"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/97jd00237", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036390887"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/2009waf2222201.1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045188535"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/mwr3264.1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047265064"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/1520-0493(1995)123<3331:aecppf>2.0.co;2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047831838"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/2008jamc1843.1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048563088"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2014-03", 
    "datePublishedReg": "2014-03-01", 
    "description": "Performance of national centers for environmental prediction based global forecast system (GFS) T574/L64 and GFS T382/L64 over Indian region has been evaluated for the summer monsoon season of 2011. The real-time model outputs are generated daily at India Meteorological Department, New Delhi for the forecasts up to 7 days. Verification of rainfall forecasts has been carried out against observed rainfall analysis. Performance of the model is also examined in terms of lower tropospheric wind circulation, vertical structure of specific humidity and precipitable water content. Case study of a monsoon depression is also illustrated. Results obtained show that, in general, both the GFS T382 and T574 forecasts are skillful to capture climatologically heavy rainfall regions. However, the accuracy in prediction of location and magnitude of rainfall fluctuates considerably. The verification results, at the spatial scale of 50 km resolution, in a regional spatial scale and country as a whole, in terms of continuous skill score, time series and categorical statistics, have demonstrated superiority of GFS T574 against T382 over Indian region. Both the model shows bias of lower tropospheric drying and upper tropospheric moistening. A bias of anti-cyclonic circulation in the lower tropospheric level lay over the central India, where rainfall as well as precipitable water content shows negative bias. Considerable differences between GFS T574 and T382 are noticed in the structure of model bias in terms of lower tropospheric wind circulation, vertical structure of specific humidity and precipitable water contents. The magnitude of error for these parameters increases with forecast lead time in both GFS T574 and T382. The results documented are expected to be useful to the forecasters, monsoon researchers and modeling community.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s00382-013-1895-5", 
    "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": "42"
      }
    ], 
    "name": "Prediction of Indian summer monsoon in short to medium range time scale with high resolution global forecast system (GFS) T574 and T382", 
    "pagination": "1527-1551", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "83539afd2f07e6df4facff9f8cddbbdf7de1fbf8f5bf2a40ec0273c48477305e"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s00382-013-1895-5"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1027568127"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s00382-013-1895-5", 
      "https://app.dimensions.ai/details/publication/pub.1027568127"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T19:09", 
    "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_8678_00000513.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007%2Fs00382-013-1895-5"
  }
]
 

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/s00382-013-1895-5'

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/s00382-013-1895-5'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00382-013-1895-5'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00382-013-1895-5'


 

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

122 TRIPLES      21 PREDICATES      45 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s00382-013-1895-5 schema:about anzsrc-for:04
2 anzsrc-for:0401
3 schema:author N5f166e2a82a74301a7df93a3202679ad
4 schema:citation https://doi.org/10.1029/97jd00237
5 https://doi.org/10.1175/1520-0434(1989)004<0335:dotngd>2.0.co;2
6 https://doi.org/10.1175/1520-0434(1990)005<0570:tcsiaa>2.0.co;2
7 https://doi.org/10.1175/1520-0434(1990)005<0576:osmosi>2.0.co;2
8 https://doi.org/10.1175/1520-0434(1991)006<0425:rciitg>2.0.co;2
9 https://doi.org/10.1175/1520-0434(2000)015<0103:voqpff>2.0.co;2
10 https://doi.org/10.1175/1520-0434(2000)015<0257:oiotfc>2.0.co;2
11 https://doi.org/10.1175/1520-0434(2001)016<0697:eeonal>2.0.co;2
12 https://doi.org/10.1175/1520-0477(1990)071<1410:gnwpat>2.0.co;2
13 https://doi.org/10.1175/1520-0493(1992)120<1747:tnmcss>2.0.co;2
14 https://doi.org/10.1175/1520-0493(1995)123<3331:aecppf>2.0.co;2
15 https://doi.org/10.1175/1520-0493(1997)125<0252:acsocp>2.0.co;2
16 https://doi.org/10.1175/2008jamc1843.1
17 https://doi.org/10.1175/2009waf2222201.1
18 https://doi.org/10.1175/2010bams3001.1
19 https://doi.org/10.1175/2010mwr3456.1
20 https://doi.org/10.1175/mwr3090.1
21 https://doi.org/10.1175/mwr3264.1
22 schema:datePublished 2014-03
23 schema:datePublishedReg 2014-03-01
24 schema:description Performance of national centers for environmental prediction based global forecast system (GFS) T574/L64 and GFS T382/L64 over Indian region has been evaluated for the summer monsoon season of 2011. The real-time model outputs are generated daily at India Meteorological Department, New Delhi for the forecasts up to 7 days. Verification of rainfall forecasts has been carried out against observed rainfall analysis. Performance of the model is also examined in terms of lower tropospheric wind circulation, vertical structure of specific humidity and precipitable water content. Case study of a monsoon depression is also illustrated. Results obtained show that, in general, both the GFS T382 and T574 forecasts are skillful to capture climatologically heavy rainfall regions. However, the accuracy in prediction of location and magnitude of rainfall fluctuates considerably. The verification results, at the spatial scale of 50 km resolution, in a regional spatial scale and country as a whole, in terms of continuous skill score, time series and categorical statistics, have demonstrated superiority of GFS T574 against T382 over Indian region. Both the model shows bias of lower tropospheric drying and upper tropospheric moistening. A bias of anti-cyclonic circulation in the lower tropospheric level lay over the central India, where rainfall as well as precipitable water content shows negative bias. Considerable differences between GFS T574 and T382 are noticed in the structure of model bias in terms of lower tropospheric wind circulation, vertical structure of specific humidity and precipitable water contents. The magnitude of error for these parameters increases with forecast lead time in both GFS T574 and T382. The results documented are expected to be useful to the forecasters, monsoon researchers and modeling community.
25 schema:genre research_article
26 schema:inLanguage en
27 schema:isAccessibleForFree false
28 schema:isPartOf N7f621d156c6f46cc9a8db01d70a700b5
29 Nc0fa8f0e42f84b78a2474f37aea2fbcf
30 sg:journal.1049631
31 schema:name Prediction of Indian summer monsoon in short to medium range time scale with high resolution global forecast system (GFS) T574 and T382
32 schema:pagination 1527-1551
33 schema:productId N2349f667529343d089e55d31c19aa531
34 N32ddbdac38ad4672a61352e14c919db9
35 Ndab0b3ac7d674d329cbff52091bb0cb5
36 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027568127
37 https://doi.org/10.1007/s00382-013-1895-5
38 schema:sdDatePublished 2019-04-10T19:09
39 schema:sdLicense https://scigraph.springernature.com/explorer/license/
40 schema:sdPublisher Nff584ba36f234956b88bc80c734c2061
41 schema:url http://link.springer.com/10.1007%2Fs00382-013-1895-5
42 sgo:license sg:explorer/license/
43 sgo:sdDataset articles
44 rdf:type schema:ScholarlyArticle
45 N2349f667529343d089e55d31c19aa531 schema:name dimensions_id
46 schema:value pub.1027568127
47 rdf:type schema:PropertyValue
48 N32ddbdac38ad4672a61352e14c919db9 schema:name doi
49 schema:value 10.1007/s00382-013-1895-5
50 rdf:type schema:PropertyValue
51 N4968c9d1d40e45fba8654d4eaeefeb80 rdf:first sg:person.016161404425.25
52 rdf:rest rdf:nil
53 N5f166e2a82a74301a7df93a3202679ad rdf:first sg:person.015364606563.26
54 rdf:rest N4968c9d1d40e45fba8654d4eaeefeb80
55 N7f621d156c6f46cc9a8db01d70a700b5 schema:issueNumber 5-6
56 rdf:type schema:PublicationIssue
57 Nc0fa8f0e42f84b78a2474f37aea2fbcf schema:volumeNumber 42
58 rdf:type schema:PublicationVolume
59 Ndab0b3ac7d674d329cbff52091bb0cb5 schema:name readcube_id
60 schema:value 83539afd2f07e6df4facff9f8cddbbdf7de1fbf8f5bf2a40ec0273c48477305e
61 rdf:type schema:PropertyValue
62 Nff584ba36f234956b88bc80c734c2061 schema:name Springer Nature - SN SciGraph project
63 rdf:type schema:Organization
64 anzsrc-for:04 schema:inDefinedTermSet anzsrc-for:
65 schema:name Earth Sciences
66 rdf:type schema:DefinedTerm
67 anzsrc-for:0401 schema:inDefinedTermSet anzsrc-for:
68 schema:name Atmospheric Sciences
69 rdf:type schema:DefinedTerm
70 sg:journal.1049631 schema:issn 0930-7575
71 1432-0894
72 schema:name Climate Dynamics
73 rdf:type schema:Periodical
74 sg:person.015364606563.26 schema:affiliation https://www.grid.ac/institutes/grid.466772.6
75 schema:familyName Durai
76 schema:givenName V. R.
77 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015364606563.26
78 rdf:type schema:Person
79 sg:person.016161404425.25 schema:affiliation https://www.grid.ac/institutes/grid.466772.6
80 schema:familyName Roy Bhowmik
81 schema:givenName S. K.
82 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016161404425.25
83 rdf:type schema:Person
84 https://doi.org/10.1029/97jd00237 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036390887
85 rdf:type schema:CreativeWork
86 https://doi.org/10.1175/1520-0434(1989)004<0335:dotngd>2.0.co;2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005273922
87 rdf:type schema:CreativeWork
88 https://doi.org/10.1175/1520-0434(1990)005<0570:tcsiaa>2.0.co;2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029356469
89 rdf:type schema:CreativeWork
90 https://doi.org/10.1175/1520-0434(1990)005<0576:osmosi>2.0.co;2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018227500
91 rdf:type schema:CreativeWork
92 https://doi.org/10.1175/1520-0434(1991)006<0425:rciitg>2.0.co;2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017206664
93 rdf:type schema:CreativeWork
94 https://doi.org/10.1175/1520-0434(2000)015<0103:voqpff>2.0.co;2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021763854
95 rdf:type schema:CreativeWork
96 https://doi.org/10.1175/1520-0434(2000)015<0257:oiotfc>2.0.co;2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029458286
97 rdf:type schema:CreativeWork
98 https://doi.org/10.1175/1520-0434(2001)016<0697:eeonal>2.0.co;2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010695190
99 rdf:type schema:CreativeWork
100 https://doi.org/10.1175/1520-0477(1990)071<1410:gnwpat>2.0.co;2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006212940
101 rdf:type schema:CreativeWork
102 https://doi.org/10.1175/1520-0493(1992)120<1747:tnmcss>2.0.co;2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002851820
103 rdf:type schema:CreativeWork
104 https://doi.org/10.1175/1520-0493(1995)123<3331:aecppf>2.0.co;2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047831838
105 rdf:type schema:CreativeWork
106 https://doi.org/10.1175/1520-0493(1997)125<0252:acsocp>2.0.co;2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012149301
107 rdf:type schema:CreativeWork
108 https://doi.org/10.1175/2008jamc1843.1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048563088
109 rdf:type schema:CreativeWork
110 https://doi.org/10.1175/2009waf2222201.1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045188535
111 rdf:type schema:CreativeWork
112 https://doi.org/10.1175/2010bams3001.1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032678557
113 rdf:type schema:CreativeWork
114 https://doi.org/10.1175/2010mwr3456.1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008487725
115 rdf:type schema:CreativeWork
116 https://doi.org/10.1175/mwr3090.1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011920286
117 rdf:type schema:CreativeWork
118 https://doi.org/10.1175/mwr3264.1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047265064
119 rdf:type schema:CreativeWork
120 https://www.grid.ac/institutes/grid.466772.6 schema:alternateName India Meteorological Department
121 schema:name India Meteorological Department, 110003, New Delhi, India
122 rdf:type schema:Organization
 




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


...