Spatio-temporal modelling of wind speed variations and extremes in the Caribbean and the Gulf of Mexico View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-02-20

AUTHORS

Igor Rychlik, Wengang Mao

ABSTRACT

The wind speed variability in the North Atlantic has been successfully modelled using a spatio-temporal transformed Gaussian field. However, this type of model does not correctly describe the extreme wind speeds attributed to tropical storms and hurricanes. In this study, the transformed Gaussian model is further developed to include the occurrence of severe storms. In this new model, random components are added to the transformed Gaussian field to model rare events with extreme wind speeds. The resulting random field is locally stationary and homogeneous. The localized dependence structure is described by time- and space-dependent parameters. The parameters have a natural physical interpretation. To exemplify its application, the model is fitted to the ECMWF ERA-Interim reanalysis data set. The model is applied to compute long-term wind speed distributions and return values, e.g., 100- or 1000-year extreme wind speeds, and to simulate random wind speed time series at a fixed location or spatio-temporal wind fields around that location. More... »

PAGES

921-944

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00704-018-2411-y

DOI

http://dx.doi.org/10.1007/s00704-018-2411-y

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1101139843


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/2008/04", 
        "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": "Department of Mathematical Sciences, Chalmers University of Technology, SE-41296, Gothenburg, Sweden", 
          "id": "http://www.grid.ac/institutes/grid.5371.0", 
          "name": [
            "Department of Mathematical Sciences, Chalmers University of Technology, SE-41296, Gothenburg, Sweden"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Rychlik", 
        "givenName": "Igor", 
        "id": "sg:person.015546022606.95", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015546022606.95"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Mechanics and Maritime Sciences, Chalmers University of Technology, SE-41296, Gothenburg, Sweden", 
          "id": "http://www.grid.ac/institutes/grid.5371.0", 
          "name": [
            "Department of Mechanics and Maritime Sciences, Chalmers University of Technology, SE-41296, Gothenburg, Sweden"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Mao", 
        "givenName": "Wengang", 
        "id": "sg:person.011623526636.51", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011623526636.51"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/s10687-010-0119-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000439726", 
          "https://doi.org/10.1007/s10687-010-0119-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4471-3675-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001789312", 
          "https://doi.org/10.1007/978-1-4471-3675-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10687-015-0227-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050364861", 
          "https://doi.org/10.1007/s10687-015-0227-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10687-010-0117-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000929413", 
          "https://doi.org/10.1007/s10687-010-0117-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4612-0173-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001840015", 
          "https://doi.org/10.1007/978-1-4612-0173-1"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2018-02-20", 
    "datePublishedReg": "2018-02-20", 
    "description": "The wind speed variability in the North Atlantic has been successfully modelled using a spatio-temporal transformed Gaussian field. However, this type of model does not correctly describe the extreme wind speeds attributed to tropical storms and hurricanes. In this study, the transformed Gaussian model is further developed to include the occurrence of severe storms. In this new model, random components are added to the transformed Gaussian field to model rare events with extreme wind speeds. The resulting random field is locally stationary and homogeneous. The localized dependence structure is described by time- and space-dependent parameters. The parameters have a natural physical interpretation. To exemplify its application, the model is fitted to the ECMWF ERA-Interim reanalysis data set. The model is applied to compute long-term wind speed distributions and return values, e.g., 100- or 1000-year extreme wind speeds, and to simulate random wind speed time series at a fixed location or spatio-temporal wind fields around that location.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s00704-018-2411-y", 
    "inLanguage": "en", 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.7111462", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1086664", 
        "issn": [
          "0177-798X", 
          "1434-4483"
        ], 
        "name": "Theoretical and Applied Climatology", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "3-4", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "135"
      }
    ], 
    "keywords": [
      "extreme wind speeds", 
      "wind speed", 
      "ECMWF ERA-Interim reanalysis data", 
      "ERA-Interim reanalysis data", 
      "wind speed variability", 
      "Gulf of Mexico", 
      "reanalysis data", 
      "North Atlantic", 
      "tropical storms", 
      "long-term wind speed distributions", 
      "severe storms", 
      "wind field", 
      "spatio-temporal modelling", 
      "speed variability", 
      "time series", 
      "wind speed distribution", 
      "storms", 
      "wind speed variations", 
      "Atlantic", 
      "types of models", 
      "speed distribution", 
      "Gulf", 
      "hurricanes", 
      "extremes", 
      "variability", 
      "speed variation", 
      "location", 
      "wind speed time series", 
      "random component", 
      "Mexico", 
      "Caribbean", 
      "dependence structure", 
      "events", 
      "speed time series", 
      "modelling", 
      "occurrence", 
      "model", 
      "variation", 
      "new model", 
      "physical interpretation", 
      "interpretation", 
      "field", 
      "space-dependent parameters", 
      "Gaussian model", 
      "speed", 
      "distribution", 
      "rare event", 
      "data", 
      "series", 
      "parameters", 
      "natural physical interpretation", 
      "components", 
      "values", 
      "Gaussian field", 
      "time", 
      "types", 
      "structure", 
      "study", 
      "random fields", 
      "applications"
    ], 
    "name": "Spatio-temporal modelling of wind speed variations and extremes in the Caribbean and the Gulf of Mexico", 
    "pagination": "921-944", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1101139843"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s00704-018-2411-y"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s00704-018-2411-y", 
      "https://app.dimensions.ai/details/publication/pub.1101139843"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-06-01T22:19", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220601/entities/gbq_results/article/article_789.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s00704-018-2411-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/s00704-018-2411-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/s00704-018-2411-y'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00704-018-2411-y'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00704-018-2411-y'


 

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

149 TRIPLES      22 PREDICATES      90 URIs      77 LITERALS      6 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s00704-018-2411-y schema:about anzsrc-for:04
2 anzsrc-for:0401
3 schema:author N015593fc155c482585aed29e8b85b1b0
4 schema:citation sg:pub.10.1007/978-1-4471-3675-0
5 sg:pub.10.1007/978-1-4612-0173-1
6 sg:pub.10.1007/s10687-010-0117-3
7 sg:pub.10.1007/s10687-010-0119-1
8 sg:pub.10.1007/s10687-015-0227-z
9 schema:datePublished 2018-02-20
10 schema:datePublishedReg 2018-02-20
11 schema:description The wind speed variability in the North Atlantic has been successfully modelled using a spatio-temporal transformed Gaussian field. However, this type of model does not correctly describe the extreme wind speeds attributed to tropical storms and hurricanes. In this study, the transformed Gaussian model is further developed to include the occurrence of severe storms. In this new model, random components are added to the transformed Gaussian field to model rare events with extreme wind speeds. The resulting random field is locally stationary and homogeneous. The localized dependence structure is described by time- and space-dependent parameters. The parameters have a natural physical interpretation. To exemplify its application, the model is fitted to the ECMWF ERA-Interim reanalysis data set. The model is applied to compute long-term wind speed distributions and return values, e.g., 100- or 1000-year extreme wind speeds, and to simulate random wind speed time series at a fixed location or spatio-temporal wind fields around that location.
12 schema:genre article
13 schema:inLanguage en
14 schema:isAccessibleForFree true
15 schema:isPartOf N235c2bf4b3b54bf49036b6eb11cbedb2
16 Nc6b10b65506f4476b02f54134cce614f
17 sg:journal.1086664
18 schema:keywords Atlantic
19 Caribbean
20 ECMWF ERA-Interim reanalysis data
21 ERA-Interim reanalysis data
22 Gaussian field
23 Gaussian model
24 Gulf
25 Gulf of Mexico
26 Mexico
27 North Atlantic
28 applications
29 components
30 data
31 dependence structure
32 distribution
33 events
34 extreme wind speeds
35 extremes
36 field
37 hurricanes
38 interpretation
39 location
40 long-term wind speed distributions
41 model
42 modelling
43 natural physical interpretation
44 new model
45 occurrence
46 parameters
47 physical interpretation
48 random component
49 random fields
50 rare event
51 reanalysis data
52 series
53 severe storms
54 space-dependent parameters
55 spatio-temporal modelling
56 speed
57 speed distribution
58 speed time series
59 speed variability
60 speed variation
61 storms
62 structure
63 study
64 time
65 time series
66 tropical storms
67 types
68 types of models
69 values
70 variability
71 variation
72 wind field
73 wind speed
74 wind speed distribution
75 wind speed time series
76 wind speed variability
77 wind speed variations
78 schema:name Spatio-temporal modelling of wind speed variations and extremes in the Caribbean and the Gulf of Mexico
79 schema:pagination 921-944
80 schema:productId N56a065897a23474f83bc6375246c5951
81 N7dcab87a69b84cf38f7c551f952198cb
82 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101139843
83 https://doi.org/10.1007/s00704-018-2411-y
84 schema:sdDatePublished 2022-06-01T22:19
85 schema:sdLicense https://scigraph.springernature.com/explorer/license/
86 schema:sdPublisher N8b68f4ede75345e5ba5f4a824e35194b
87 schema:url https://doi.org/10.1007/s00704-018-2411-y
88 sgo:license sg:explorer/license/
89 sgo:sdDataset articles
90 rdf:type schema:ScholarlyArticle
91 N015593fc155c482585aed29e8b85b1b0 rdf:first sg:person.015546022606.95
92 rdf:rest N43a2ce4fca2f4b09838ca1eab02a48a1
93 N235c2bf4b3b54bf49036b6eb11cbedb2 schema:volumeNumber 135
94 rdf:type schema:PublicationVolume
95 N43a2ce4fca2f4b09838ca1eab02a48a1 rdf:first sg:person.011623526636.51
96 rdf:rest rdf:nil
97 N56a065897a23474f83bc6375246c5951 schema:name doi
98 schema:value 10.1007/s00704-018-2411-y
99 rdf:type schema:PropertyValue
100 N7dcab87a69b84cf38f7c551f952198cb schema:name dimensions_id
101 schema:value pub.1101139843
102 rdf:type schema:PropertyValue
103 N8b68f4ede75345e5ba5f4a824e35194b schema:name Springer Nature - SN SciGraph project
104 rdf:type schema:Organization
105 Nc6b10b65506f4476b02f54134cce614f schema:issueNumber 3-4
106 rdf:type schema:PublicationIssue
107 anzsrc-for:04 schema:inDefinedTermSet anzsrc-for:
108 schema:name Earth Sciences
109 rdf:type schema:DefinedTerm
110 anzsrc-for:0401 schema:inDefinedTermSet anzsrc-for:
111 schema:name Atmospheric Sciences
112 rdf:type schema:DefinedTerm
113 sg:grant.7111462 http://pending.schema.org/fundedItem sg:pub.10.1007/s00704-018-2411-y
114 rdf:type schema:MonetaryGrant
115 sg:journal.1086664 schema:issn 0177-798X
116 1434-4483
117 schema:name Theoretical and Applied Climatology
118 schema:publisher Springer Nature
119 rdf:type schema:Periodical
120 sg:person.011623526636.51 schema:affiliation grid-institutes:grid.5371.0
121 schema:familyName Mao
122 schema:givenName Wengang
123 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011623526636.51
124 rdf:type schema:Person
125 sg:person.015546022606.95 schema:affiliation grid-institutes:grid.5371.0
126 schema:familyName Rychlik
127 schema:givenName Igor
128 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015546022606.95
129 rdf:type schema:Person
130 sg:pub.10.1007/978-1-4471-3675-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001789312
131 https://doi.org/10.1007/978-1-4471-3675-0
132 rdf:type schema:CreativeWork
133 sg:pub.10.1007/978-1-4612-0173-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001840015
134 https://doi.org/10.1007/978-1-4612-0173-1
135 rdf:type schema:CreativeWork
136 sg:pub.10.1007/s10687-010-0117-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000929413
137 https://doi.org/10.1007/s10687-010-0117-3
138 rdf:type schema:CreativeWork
139 sg:pub.10.1007/s10687-010-0119-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000439726
140 https://doi.org/10.1007/s10687-010-0119-1
141 rdf:type schema:CreativeWork
142 sg:pub.10.1007/s10687-015-0227-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1050364861
143 https://doi.org/10.1007/s10687-015-0227-z
144 rdf:type schema:CreativeWork
145 grid-institutes:grid.5371.0 schema:alternateName Department of Mathematical Sciences, Chalmers University of Technology, SE-41296, Gothenburg, Sweden
146 Department of Mechanics and Maritime Sciences, Chalmers University of Technology, SE-41296, Gothenburg, Sweden
147 schema:name Department of Mathematical Sciences, Chalmers University of Technology, SE-41296, Gothenburg, Sweden
148 Department of Mechanics and Maritime Sciences, Chalmers University of Technology, SE-41296, Gothenburg, Sweden
149 rdf:type schema:Organization
 




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


...