MOS guidance using a neural network for the rainfall forecast over India View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-05-13

AUTHORS

Ashok Kumar, Ch Sridevi, V R Durai, K K Singh, P Mukhopadhyay, N Chattopadhyay

ABSTRACT

In the present study, a model output statistics (MOS) guidance model was developed by using the neural network technique for a bias-corrected rainfall forecast. The model was developed over the Indian window (0–40∘N\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$40{^{\circ }}\hbox {N}$$\end{document} and 60–100∘E\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$100{^{\circ }}\hbox {E}$$\end{document}) by using the observed and global forecast system (GFS) T-1534 model output (up to 5 days) at a 0.125∘×0.125∘\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.125{^{\circ }} \times \,0.125{^{\circ }}$$\end{document} regular grid during the summer monsoon (June–September) 2016. The skill of the developed MOS model forecast against the observed 0.125∘×0.125∘\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.125{^{\circ }} \times 0.125{^{\circ }}$$\end{document} grid rainfall data is obtained for the summer monsoon (June–September) 2017. The skill of the MOS model rainfall forecast is found to show good improvement over the T-1534 model’s direct forecast over the Indian window. In general, the T-1534 model’s direct forecast shows high skill but the forecast obtained by using the MOS model shows better skill than the direct model’s forecast, although a major improvement is seen for the Day 1 forecast at the national level. So the skill of the bias-corrected rainfall forecast by using the MOS guidance and the T-1534 model output is high and has the potential of being used as an operational forecast over the Indian region. More... »

PAGES

130

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s12040-019-1149-y

DOI

http://dx.doi.org/10.1007/s12040-019-1149-y

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1114222015


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/2008/02", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Physical 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"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0201", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Astronomical and Space Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0403", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Geology", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0406", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Physical Geography and Environmental Geoscience", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "India Meteorological Department, 110 003, New Delhi, India", 
          "id": "http://www.grid.ac/institutes/grid.466772.6", 
          "name": [
            "India Meteorological Department, 110 003, New Delhi, India"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kumar", 
        "givenName": "Ashok", 
        "id": "sg:person.015665027055.25", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015665027055.25"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "India Meteorological Department, 110 003, New Delhi, India", 
          "id": "http://www.grid.ac/institutes/grid.466772.6", 
          "name": [
            "India Meteorological Department, 110 003, New Delhi, India"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Sridevi", 
        "givenName": "Ch", 
        "id": "sg:person.012733721336.83", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012733721336.83"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "India Meteorological Department, 110 003, New Delhi, India", 
          "id": "http://www.grid.ac/institutes/grid.466772.6", 
          "name": [
            "India Meteorological Department, 110 003, 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, 110 003, New Delhi, India", 
          "id": "http://www.grid.ac/institutes/grid.466772.6", 
          "name": [
            "India Meteorological Department, 110 003, New Delhi, India"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Singh", 
        "givenName": "K K", 
        "id": "sg:person.011166642362.25", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011166642362.25"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "India Institute of Tropical Meteorology, 411 017, Pune, India", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "India Institute of Tropical Meteorology, 411 017, Pune, India"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Mukhopadhyay", 
        "givenName": "P", 
        "id": "sg:person.07665477062.28", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07665477062.28"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "India Meteorological Department, 411 005, Pune, India", 
          "id": "http://www.grid.ac/institutes/grid.466772.6", 
          "name": [
            "India Meteorological Department, 411 005, Pune, India"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Chattopadhyay", 
        "givenName": "N", 
        "id": "sg:person.014171723724.50", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014171723724.50"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/s00382-013-1895-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027568127", 
          "https://doi.org/10.1007/s00382-013-1895-5"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-05-13", 
    "datePublishedReg": "2019-05-13", 
    "description": "In the present study, a model output statistics (MOS) guidance model was developed by using the neural network technique for a bias-corrected rainfall forecast. The model was developed over the Indian window (0\u201340\u2218N\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym}\n\t\t\t\t\\usepackage{amsfonts}\n\t\t\t\t\\usepackage{amssymb}\n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$40{^{\\circ }}\\hbox {N}$$\\end{document} and 60\u2013100\u2218E\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym}\n\t\t\t\t\\usepackage{amsfonts}\n\t\t\t\t\\usepackage{amssymb}\n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$100{^{\\circ }}\\hbox {E}$$\\end{document}) by using the observed and global forecast system (GFS) T-1534 model output (up to 5\u00a0days) at a 0.125\u2218\u00d70.125\u2218\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym}\n\t\t\t\t\\usepackage{amsfonts}\n\t\t\t\t\\usepackage{amssymb}\n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$0.125{^{\\circ }} \\times \\,0.125{^{\\circ }}$$\\end{document} regular grid during the summer monsoon (June\u2013September) 2016. The skill of the developed MOS model forecast against the observed 0.125\u2218\u00d70.125\u2218\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym}\n\t\t\t\t\\usepackage{amsfonts}\n\t\t\t\t\\usepackage{amssymb}\n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$0.125{^{\\circ }} \\times 0.125{^{\\circ }}$$\\end{document} grid rainfall data is obtained for the summer monsoon (June\u2013September) 2017. The skill of the MOS model rainfall forecast is found to show good improvement over the T-1534 model\u2019s direct forecast over the Indian window. In general, the T-1534 model\u2019s direct forecast shows high skill but the forecast obtained by using the MOS model shows better skill than the direct model\u2019s forecast, although a major improvement is seen for the Day 1 forecast at the national level. So the skill of the bias-corrected rainfall forecast by using the MOS guidance and the T-1534 model output is high and has the potential of being used as an operational forecast over the Indian region.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s12040-019-1149-y", 
    "inLanguage": "en", 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1136531", 
        "issn": [
          "2347-4327", 
          "0253-4126"
        ], 
        "name": "Journal of Earth System Science", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "5", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "128"
      }
    ], 
    "keywords": [
      "rainfall forecasts", 
      "model forecasts", 
      "MOS guidance", 
      "model output", 
      "direct forecasts", 
      "model rainfall forecast", 
      "day 1 forecast", 
      "rainfall data", 
      "operational forecasts", 
      "Indian region", 
      "better skills", 
      "high skill", 
      "forecasts", 
      "rainfall", 
      "regular grid", 
      "neural network technique", 
      "major improvements", 
      "India", 
      "model", 
      "region", 
      "network techniques", 
      "window", 
      "data", 
      "output", 
      "MOS model", 
      "grid", 
      "guidance model", 
      "skills", 
      "potential", 
      "study", 
      "present study", 
      "levels", 
      "network", 
      "technique", 
      "neural network", 
      "improvement", 
      "national level", 
      "guidance", 
      "better improvement", 
      "system T"
    ], 
    "name": "MOS guidance using a neural network for the rainfall forecast over India", 
    "pagination": "130", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1114222015"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s12040-019-1149-y"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s12040-019-1149-y", 
      "https://app.dimensions.ai/details/publication/pub.1114222015"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-05-10T10:25", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220509/entities/gbq_results/article/article_832.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s12040-019-1149-y"
  }
]
 

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/s12040-019-1149-y'

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/s12040-019-1149-y'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s12040-019-1149-y'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s12040-019-1149-y'


 

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

154 TRIPLES      22 PREDICATES      69 URIs      57 LITERALS      6 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s12040-019-1149-y schema:about anzsrc-for:02
2 anzsrc-for:0201
3 anzsrc-for:04
4 anzsrc-for:0403
5 anzsrc-for:0406
6 schema:author Na30b7bddc2a847e9b738f060bc1d4bda
7 schema:citation sg:pub.10.1007/s00382-013-1895-5
8 schema:datePublished 2019-05-13
9 schema:datePublishedReg 2019-05-13
10 schema:description In the present study, a model output statistics (MOS) guidance model was developed by using the neural network technique for a bias-corrected rainfall forecast. The model was developed over the Indian window (0–40∘N\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$40{^{\circ }}\hbox {N}$$\end{document} and 60–100∘E\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$100{^{\circ }}\hbox {E}$$\end{document}) by using the observed and global forecast system (GFS) T-1534 model output (up to 5 days) at a 0.125∘×0.125∘\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.125{^{\circ }} \times \,0.125{^{\circ }}$$\end{document} regular grid during the summer monsoon (June–September) 2016. The skill of the developed MOS model forecast against the observed 0.125∘×0.125∘\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.125{^{\circ }} \times 0.125{^{\circ }}$$\end{document} grid rainfall data is obtained for the summer monsoon (June–September) 2017. The skill of the MOS model rainfall forecast is found to show good improvement over the T-1534 model’s direct forecast over the Indian window. In general, the T-1534 model’s direct forecast shows high skill but the forecast obtained by using the MOS model shows better skill than the direct model’s forecast, although a major improvement is seen for the Day 1 forecast at the national level. So the skill of the bias-corrected rainfall forecast by using the MOS guidance and the T-1534 model output is high and has the potential of being used as an operational forecast over the Indian region.
11 schema:genre article
12 schema:inLanguage en
13 schema:isAccessibleForFree true
14 schema:isPartOf Nc21550a2b0c646bd8b80f7bbf7fc389f
15 Nef92f72135ae41098063db585bb98dee
16 sg:journal.1136531
17 schema:keywords India
18 Indian region
19 MOS guidance
20 MOS model
21 better improvement
22 better skills
23 data
24 day 1 forecast
25 direct forecasts
26 forecasts
27 grid
28 guidance
29 guidance model
30 high skill
31 improvement
32 levels
33 major improvements
34 model
35 model forecasts
36 model output
37 model rainfall forecast
38 national level
39 network
40 network techniques
41 neural network
42 neural network technique
43 operational forecasts
44 output
45 potential
46 present study
47 rainfall
48 rainfall data
49 rainfall forecasts
50 region
51 regular grid
52 skills
53 study
54 system T
55 technique
56 window
57 schema:name MOS guidance using a neural network for the rainfall forecast over India
58 schema:pagination 130
59 schema:productId N0d947793aaf44c8789b11d9d9475b1b8
60 Na4b57a83e9f647bfa1969545b31b55cb
61 schema:sameAs https://app.dimensions.ai/details/publication/pub.1114222015
62 https://doi.org/10.1007/s12040-019-1149-y
63 schema:sdDatePublished 2022-05-10T10:25
64 schema:sdLicense https://scigraph.springernature.com/explorer/license/
65 schema:sdPublisher N508c316fd14b43b3be42738c422b4cd0
66 schema:url https://doi.org/10.1007/s12040-019-1149-y
67 sgo:license sg:explorer/license/
68 sgo:sdDataset articles
69 rdf:type schema:ScholarlyArticle
70 N0d947793aaf44c8789b11d9d9475b1b8 schema:name dimensions_id
71 schema:value pub.1114222015
72 rdf:type schema:PropertyValue
73 N1fa542be949b4788b645a593ef657114 rdf:first sg:person.014171723724.50
74 rdf:rest rdf:nil
75 N508c316fd14b43b3be42738c422b4cd0 schema:name Springer Nature - SN SciGraph project
76 rdf:type schema:Organization
77 Na30b7bddc2a847e9b738f060bc1d4bda rdf:first sg:person.015665027055.25
78 rdf:rest Nbfcb163ade754e53b284da641bd8260e
79 Na4b57a83e9f647bfa1969545b31b55cb schema:name doi
80 schema:value 10.1007/s12040-019-1149-y
81 rdf:type schema:PropertyValue
82 Nb44a83dd89ff4d64a33d50aed120a50a rdf:first sg:person.011166642362.25
83 rdf:rest Nc4808c061d824c148445818e263f0f20
84 Nbfcb163ade754e53b284da641bd8260e rdf:first sg:person.012733721336.83
85 rdf:rest Nc95233c7507d4dcbace9147752693a92
86 Nc21550a2b0c646bd8b80f7bbf7fc389f schema:volumeNumber 128
87 rdf:type schema:PublicationVolume
88 Nc4808c061d824c148445818e263f0f20 rdf:first sg:person.07665477062.28
89 rdf:rest N1fa542be949b4788b645a593ef657114
90 Nc95233c7507d4dcbace9147752693a92 rdf:first sg:person.015364606563.26
91 rdf:rest Nb44a83dd89ff4d64a33d50aed120a50a
92 Nef92f72135ae41098063db585bb98dee schema:issueNumber 5
93 rdf:type schema:PublicationIssue
94 anzsrc-for:02 schema:inDefinedTermSet anzsrc-for:
95 schema:name Physical Sciences
96 rdf:type schema:DefinedTerm
97 anzsrc-for:0201 schema:inDefinedTermSet anzsrc-for:
98 schema:name Astronomical and Space Sciences
99 rdf:type schema:DefinedTerm
100 anzsrc-for:04 schema:inDefinedTermSet anzsrc-for:
101 schema:name Earth Sciences
102 rdf:type schema:DefinedTerm
103 anzsrc-for:0403 schema:inDefinedTermSet anzsrc-for:
104 schema:name Geology
105 rdf:type schema:DefinedTerm
106 anzsrc-for:0406 schema:inDefinedTermSet anzsrc-for:
107 schema:name Physical Geography and Environmental Geoscience
108 rdf:type schema:DefinedTerm
109 sg:journal.1136531 schema:issn 0253-4126
110 2347-4327
111 schema:name Journal of Earth System Science
112 schema:publisher Springer Nature
113 rdf:type schema:Periodical
114 sg:person.011166642362.25 schema:affiliation grid-institutes:grid.466772.6
115 schema:familyName Singh
116 schema:givenName K K
117 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011166642362.25
118 rdf:type schema:Person
119 sg:person.012733721336.83 schema:affiliation grid-institutes:grid.466772.6
120 schema:familyName Sridevi
121 schema:givenName Ch
122 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012733721336.83
123 rdf:type schema:Person
124 sg:person.014171723724.50 schema:affiliation grid-institutes:grid.466772.6
125 schema:familyName Chattopadhyay
126 schema:givenName N
127 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014171723724.50
128 rdf:type schema:Person
129 sg:person.015364606563.26 schema:affiliation grid-institutes:grid.466772.6
130 schema:familyName Durai
131 schema:givenName V R
132 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015364606563.26
133 rdf:type schema:Person
134 sg:person.015665027055.25 schema:affiliation grid-institutes:grid.466772.6
135 schema:familyName Kumar
136 schema:givenName Ashok
137 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015665027055.25
138 rdf:type schema:Person
139 sg:person.07665477062.28 schema:affiliation grid-institutes:None
140 schema:familyName Mukhopadhyay
141 schema:givenName P
142 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07665477062.28
143 rdf:type schema:Person
144 sg:pub.10.1007/s00382-013-1895-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027568127
145 https://doi.org/10.1007/s00382-013-1895-5
146 rdf:type schema:CreativeWork
147 grid-institutes:None schema:alternateName India Institute of Tropical Meteorology, 411 017, Pune, India
148 schema:name India Institute of Tropical Meteorology, 411 017, Pune, India
149 rdf:type schema:Organization
150 grid-institutes:grid.466772.6 schema:alternateName India Meteorological Department, 110 003, New Delhi, India
151 India Meteorological Department, 411 005, Pune, India
152 schema:name India Meteorological Department, 110 003, New Delhi, India
153 India Meteorological Department, 411 005, Pune, India
154 rdf:type schema:Organization
 




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


...