Detecting inhomogeneity in daily climate series using wavelet analysis View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2008-03

AUTHORS

Zhongwei Yan, Phil D. Jones

ABSTRACT

A wavelet method was applied to detect inhomogeneities in daily meteorological series, data which are being increasingly applied in studies of climate extremes. The wavelet method has been applied to a few well-established long-term daily temperature series back to the 18th century, which have been “homogenized” with conventional approaches. Various types of problems remaining in the series were revealed with the wavelet method. Their influences on analyses of change in climate extremes are discussed. The results have importance for understanding issues in conventional climate data processing and for development of improved methods of homogenization in order to improve analysis of climate extremes based on daily data. More... »

PAGES

157-163

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00376-008-0157-7

DOI

http://dx.doi.org/10.1007/s00376-008-0157-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1021381645


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": "Institute of Atmospheric Physics", 
          "id": "https://www.grid.ac/institutes/grid.424023.3", 
          "name": [
            "Laboratory of Regional Climate and Environment for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, 100029, Beijing, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Yan", 
        "givenName": "Zhongwei", 
        "id": "sg:person.011271135136.00", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011271135136.00"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of East Anglia", 
          "id": "https://www.grid.ac/institutes/grid.8273.e", 
          "name": [
            "Climatic Research Unit, University of East Anglia, NR4 7TJ, Norwich, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Jones", 
        "givenName": "Phil D.", 
        "id": "sg:person.0721646017.58", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0721646017.58"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1175/1520-0442(2003)016<3665:tiiodt>2.0.co;2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001138623"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/jcli3855.1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001491842"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1023/a:1005468316392", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002364713", 
          "https://doi.org/10.1023/a:1005468316392"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1023/a:1014983229213", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003606376", 
          "https://doi.org/10.1023/a:1014983229213"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/1520-0477(1995)076<2391:csduwt>2.0.co;2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005593907"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/2000jd900300", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012740545"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/2000jd900705", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013269189"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/1520-0442(1998)011<2200:citeot>2.0.co;2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017927689"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00704-005-0156-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019729988", 
          "https://doi.org/10.1007/s00704-005-0156-x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00704-005-0156-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019729988", 
          "https://doi.org/10.1007/s00704-005-0156-x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1023/a:1014923027396", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024495971", 
          "https://doi.org/10.1023/a:1014923027396"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/joc.945", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029731980"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1023/a:1014902904197", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035451665", 
          "https://doi.org/10.1023/a:1014902904197"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-94-015-9265-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040258456", 
          "https://doi.org/10.1007/978-94-015-9265-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-94-015-9265-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040258456", 
          "https://doi.org/10.1007/978-94-015-9265-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1023/a:1014939413284", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040677594", 
          "https://doi.org/10.1023/a:1014939413284"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/1520-0442(2002)015<1322:hodtoc>2.0.co;2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041690778"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/joc.3370120402", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044655337"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf02919312", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045140840", 
          "https://doi.org/10.1007/bf02919312"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf02919312", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045140840", 
          "https://doi.org/10.1007/bf02919312"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1023/a:1014918808741", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046986388", 
          "https://doi.org/10.1023/a:1014918808741"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s007040200016", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048170738", 
          "https://doi.org/10.1007/s007040200016"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/030913339902300305", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1063817505"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/030913339902300305", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1063817505"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3354/cr019193", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1071159442"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2008-03", 
    "datePublishedReg": "2008-03-01", 
    "description": "A wavelet method was applied to detect inhomogeneities in daily meteorological series, data which are being increasingly applied in studies of climate extremes. The wavelet method has been applied to a few well-established long-term daily temperature series back to the 18th century, which have been \u201chomogenized\u201d with conventional approaches. Various types of problems remaining in the series were revealed with the wavelet method. Their influences on analyses of change in climate extremes are discussed. The results have importance for understanding issues in conventional climate data processing and for development of improved methods of homogenization in order to improve analysis of climate extremes based on daily data.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s00376-008-0157-7", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1135901", 
        "issn": [
          "0256-1530", 
          "1861-9533"
        ], 
        "name": "Advances in Atmospheric Sciences", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "2", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "25"
      }
    ], 
    "name": "Detecting inhomogeneity in daily climate series using wavelet analysis", 
    "pagination": "157-163", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "65df83a9e76503127b980afaf9e0f8b8bff78c39bdb159bb862dbce7aba4ed5b"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s00376-008-0157-7"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1021381645"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s00376-008-0157-7", 
      "https://app.dimensions.ai/details/publication/pub.1021381645"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T14:30", 
    "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/v90/0000000373_0000000373/records_13090_00000000.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007/s00376-008-0157-7"
  }
]
 

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/s00376-008-0157-7'

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/s00376-008-0157-7'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00376-008-0157-7'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00376-008-0157-7'


 

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

144 TRIPLES      21 PREDICATES      48 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s00376-008-0157-7 schema:about anzsrc-for:04
2 anzsrc-for:0401
3 schema:author N5e33e9b7cf744251b17194befe019a36
4 schema:citation sg:pub.10.1007/978-94-015-9265-9
5 sg:pub.10.1007/bf02919312
6 sg:pub.10.1007/s00704-005-0156-x
7 sg:pub.10.1007/s007040200016
8 sg:pub.10.1023/a:1005468316392
9 sg:pub.10.1023/a:1014902904197
10 sg:pub.10.1023/a:1014918808741
11 sg:pub.10.1023/a:1014923027396
12 sg:pub.10.1023/a:1014939413284
13 sg:pub.10.1023/a:1014983229213
14 https://doi.org/10.1002/joc.3370120402
15 https://doi.org/10.1002/joc.945
16 https://doi.org/10.1029/2000jd900300
17 https://doi.org/10.1029/2000jd900705
18 https://doi.org/10.1175/1520-0442(1998)011<2200:citeot>2.0.co;2
19 https://doi.org/10.1175/1520-0442(2002)015<1322:hodtoc>2.0.co;2
20 https://doi.org/10.1175/1520-0442(2003)016<3665:tiiodt>2.0.co;2
21 https://doi.org/10.1175/1520-0477(1995)076<2391:csduwt>2.0.co;2
22 https://doi.org/10.1175/jcli3855.1
23 https://doi.org/10.1177/030913339902300305
24 https://doi.org/10.3354/cr019193
25 schema:datePublished 2008-03
26 schema:datePublishedReg 2008-03-01
27 schema:description A wavelet method was applied to detect inhomogeneities in daily meteorological series, data which are being increasingly applied in studies of climate extremes. The wavelet method has been applied to a few well-established long-term daily temperature series back to the 18th century, which have been “homogenized” with conventional approaches. Various types of problems remaining in the series were revealed with the wavelet method. Their influences on analyses of change in climate extremes are discussed. The results have importance for understanding issues in conventional climate data processing and for development of improved methods of homogenization in order to improve analysis of climate extremes based on daily data.
28 schema:genre research_article
29 schema:inLanguage en
30 schema:isAccessibleForFree false
31 schema:isPartOf N1c879e7da28949a39e1780bcbfc30133
32 Ne40080ba4b554f47af94388d1df6e4d8
33 sg:journal.1135901
34 schema:name Detecting inhomogeneity in daily climate series using wavelet analysis
35 schema:pagination 157-163
36 schema:productId Nadfd3c50ef954daa93e883b86321a404
37 Nedd9de1468644499b48b277665150ce4
38 Nf305b91b3a734ec0b7ad304dc3646ed6
39 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021381645
40 https://doi.org/10.1007/s00376-008-0157-7
41 schema:sdDatePublished 2019-04-11T14:30
42 schema:sdLicense https://scigraph.springernature.com/explorer/license/
43 schema:sdPublisher N787ec7c1577744399e2d76dd56a1801c
44 schema:url http://link.springer.com/10.1007/s00376-008-0157-7
45 sgo:license sg:explorer/license/
46 sgo:sdDataset articles
47 rdf:type schema:ScholarlyArticle
48 N1c879e7da28949a39e1780bcbfc30133 schema:issueNumber 2
49 rdf:type schema:PublicationIssue
50 N224fc723ee884899853ba21341ed9a91 rdf:first sg:person.0721646017.58
51 rdf:rest rdf:nil
52 N5e33e9b7cf744251b17194befe019a36 rdf:first sg:person.011271135136.00
53 rdf:rest N224fc723ee884899853ba21341ed9a91
54 N787ec7c1577744399e2d76dd56a1801c schema:name Springer Nature - SN SciGraph project
55 rdf:type schema:Organization
56 Nadfd3c50ef954daa93e883b86321a404 schema:name readcube_id
57 schema:value 65df83a9e76503127b980afaf9e0f8b8bff78c39bdb159bb862dbce7aba4ed5b
58 rdf:type schema:PropertyValue
59 Ne40080ba4b554f47af94388d1df6e4d8 schema:volumeNumber 25
60 rdf:type schema:PublicationVolume
61 Nedd9de1468644499b48b277665150ce4 schema:name dimensions_id
62 schema:value pub.1021381645
63 rdf:type schema:PropertyValue
64 Nf305b91b3a734ec0b7ad304dc3646ed6 schema:name doi
65 schema:value 10.1007/s00376-008-0157-7
66 rdf:type schema:PropertyValue
67 anzsrc-for:04 schema:inDefinedTermSet anzsrc-for:
68 schema:name Earth Sciences
69 rdf:type schema:DefinedTerm
70 anzsrc-for:0401 schema:inDefinedTermSet anzsrc-for:
71 schema:name Atmospheric Sciences
72 rdf:type schema:DefinedTerm
73 sg:journal.1135901 schema:issn 0256-1530
74 1861-9533
75 schema:name Advances in Atmospheric Sciences
76 rdf:type schema:Periodical
77 sg:person.011271135136.00 schema:affiliation https://www.grid.ac/institutes/grid.424023.3
78 schema:familyName Yan
79 schema:givenName Zhongwei
80 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011271135136.00
81 rdf:type schema:Person
82 sg:person.0721646017.58 schema:affiliation https://www.grid.ac/institutes/grid.8273.e
83 schema:familyName Jones
84 schema:givenName Phil D.
85 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0721646017.58
86 rdf:type schema:Person
87 sg:pub.10.1007/978-94-015-9265-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040258456
88 https://doi.org/10.1007/978-94-015-9265-9
89 rdf:type schema:CreativeWork
90 sg:pub.10.1007/bf02919312 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045140840
91 https://doi.org/10.1007/bf02919312
92 rdf:type schema:CreativeWork
93 sg:pub.10.1007/s00704-005-0156-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1019729988
94 https://doi.org/10.1007/s00704-005-0156-x
95 rdf:type schema:CreativeWork
96 sg:pub.10.1007/s007040200016 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048170738
97 https://doi.org/10.1007/s007040200016
98 rdf:type schema:CreativeWork
99 sg:pub.10.1023/a:1005468316392 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002364713
100 https://doi.org/10.1023/a:1005468316392
101 rdf:type schema:CreativeWork
102 sg:pub.10.1023/a:1014902904197 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035451665
103 https://doi.org/10.1023/a:1014902904197
104 rdf:type schema:CreativeWork
105 sg:pub.10.1023/a:1014918808741 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046986388
106 https://doi.org/10.1023/a:1014918808741
107 rdf:type schema:CreativeWork
108 sg:pub.10.1023/a:1014923027396 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024495971
109 https://doi.org/10.1023/a:1014923027396
110 rdf:type schema:CreativeWork
111 sg:pub.10.1023/a:1014939413284 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040677594
112 https://doi.org/10.1023/a:1014939413284
113 rdf:type schema:CreativeWork
114 sg:pub.10.1023/a:1014983229213 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003606376
115 https://doi.org/10.1023/a:1014983229213
116 rdf:type schema:CreativeWork
117 https://doi.org/10.1002/joc.3370120402 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044655337
118 rdf:type schema:CreativeWork
119 https://doi.org/10.1002/joc.945 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029731980
120 rdf:type schema:CreativeWork
121 https://doi.org/10.1029/2000jd900300 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012740545
122 rdf:type schema:CreativeWork
123 https://doi.org/10.1029/2000jd900705 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013269189
124 rdf:type schema:CreativeWork
125 https://doi.org/10.1175/1520-0442(1998)011<2200:citeot>2.0.co;2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017927689
126 rdf:type schema:CreativeWork
127 https://doi.org/10.1175/1520-0442(2002)015<1322:hodtoc>2.0.co;2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041690778
128 rdf:type schema:CreativeWork
129 https://doi.org/10.1175/1520-0442(2003)016<3665:tiiodt>2.0.co;2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001138623
130 rdf:type schema:CreativeWork
131 https://doi.org/10.1175/1520-0477(1995)076<2391:csduwt>2.0.co;2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005593907
132 rdf:type schema:CreativeWork
133 https://doi.org/10.1175/jcli3855.1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001491842
134 rdf:type schema:CreativeWork
135 https://doi.org/10.1177/030913339902300305 schema:sameAs https://app.dimensions.ai/details/publication/pub.1063817505
136 rdf:type schema:CreativeWork
137 https://doi.org/10.3354/cr019193 schema:sameAs https://app.dimensions.ai/details/publication/pub.1071159442
138 rdf:type schema:CreativeWork
139 https://www.grid.ac/institutes/grid.424023.3 schema:alternateName Institute of Atmospheric Physics
140 schema:name Laboratory of Regional Climate and Environment for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, 100029, Beijing, China
141 rdf:type schema:Organization
142 https://www.grid.ac/institutes/grid.8273.e schema:alternateName University of East Anglia
143 schema:name Climatic Research Unit, University of East Anglia, NR4 7TJ, Norwich, UK
144 rdf:type schema:Organization
 




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


...