Statistical modeling of daily maximum surface ozone concentrations View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2010-08

AUTHORS

A. M. Zvyagintsev, I. B. Belikov, N. F. Elanskii, G. Kakadzhanova, I. N. Kuznetsova, O. A. Tarasova, I. Yu. Shalygina

ABSTRACT

A statistical model of the daily maximum surface ozone concentrations is suggested based on correlations with its predictors. Among the predictors are the temperature; relative humidity; mean wind speed in the planetary boundary layer; concentrations of other trace gases; and the “meteorological pollution potential,” which can characterize adverse (for atmospheric dispersion) meteorological conditions. The statistical model is suitable for surface ozone forecasting; it uses current meteorological parameters, as well as their forecasted values. The most significant predictors of the surface ozone in the Moscow region are the meteorological pollution potential and anomalies (deviations from the norms) of the temperature, relative humidity, and surface ozone on the previous day. The model was tested using the data obtained for the Moscow region and some German stations. Such a model is better than the “climate” and “inertial” models and can ensure a determination coefficient of the surface ozone anomalies of about 50%. More... »

PAGES

284-292

Identifiers

URI

http://scigraph.springernature.com/pub.10.1134/s102485601004007x

DOI

http://dx.doi.org/10.1134/s102485601004007x

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1017042308


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": "Roshydromet", 
          "id": "https://www.grid.ac/institutes/grid.433404.4", 
          "name": [
            "Central Aerological Observatory, Pervomaiskaya ul. 3, 141700, Dolgoprudnyi, Moscow region, Russia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zvyagintsev", 
        "givenName": "A. M.", 
        "id": "sg:person.07503617640.61", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07503617640.61"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Russian Academy of Sciences", 
          "id": "https://www.grid.ac/institutes/grid.4886.2", 
          "name": [
            "Obukhov Institute of Atmospheric Physics, Russian Academy of Sciences, Pyzhevskii per. 3, 109017, Moscow, Russia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Belikov", 
        "givenName": "I. B.", 
        "id": "sg:person.015274652731.85", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015274652731.85"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Russian Academy of Sciences", 
          "id": "https://www.grid.ac/institutes/grid.4886.2", 
          "name": [
            "Obukhov Institute of Atmospheric Physics, Russian Academy of Sciences, Pyzhevskii per. 3, 109017, Moscow, Russia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Elanskii", 
        "givenName": "N. F.", 
        "id": "sg:person.010233634375.18", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010233634375.18"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Roshydromet", 
          "id": "https://www.grid.ac/institutes/grid.433404.4", 
          "name": [
            "Central Aerological Observatory, Pervomaiskaya ul. 3, 141700, Dolgoprudnyi, Moscow region, Russia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kakadzhanova", 
        "givenName": "G.", 
        "id": "sg:person.011553230457.54", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011553230457.54"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "Hydrometeorological Center of Russia, B. Predtechenskii per. 9-13, 123242, Moscow, Russia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kuznetsova", 
        "givenName": "I. N.", 
        "id": "sg:person.015455205355.88", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015455205355.88"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Roshydromet", 
          "id": "https://www.grid.ac/institutes/grid.433404.4", 
          "name": [
            "Central Aerological Observatory, Pervomaiskaya ul. 3, 141700, Dolgoprudnyi, Moscow region, Russia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Tarasova", 
        "givenName": "O. A.", 
        "id": "sg:person.012512461216.79", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012512461216.79"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "Hydrometeorological Center of Russia, B. Predtechenskii per. 9-13, 123242, Moscow, Russia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Shalygina", 
        "givenName": "I. Yu.", 
        "id": "sg:person.016013153025.09", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016013153025.09"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.5194/acp-3-941-2003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000026913"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.5194/acp-3-941-2003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000026913"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0960-1686(91)90262-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006002441"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0960-1686(91)90262-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006002441"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/1520-0450(1982)021<1662:aopmvt>2.0.co;2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006785933"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s1352-2310(00)00466-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020810688"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.5194/acpd-5-9003-2005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025296214"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.5194/acpd-5-9003-2005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025296214"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s1352-2310(98)00345-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036039504"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/10473289.1996.10467439", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039033496"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.5194/acp-9-2695-2009", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042878050"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.atmosenv.2004.09.070", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044234765"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.atmosenv.2006.07.039", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052748143"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2010-08", 
    "datePublishedReg": "2010-08-01", 
    "description": "A statistical model of the daily maximum surface ozone concentrations is suggested based on correlations with its predictors. Among the predictors are the temperature; relative humidity; mean wind speed in the planetary boundary layer; concentrations of other trace gases; and the \u201cmeteorological pollution potential,\u201d which can characterize adverse (for atmospheric dispersion) meteorological conditions. The statistical model is suitable for surface ozone forecasting; it uses current meteorological parameters, as well as their forecasted values. The most significant predictors of the surface ozone in the Moscow region are the meteorological pollution potential and anomalies (deviations from the norms) of the temperature, relative humidity, and surface ozone on the previous day. The model was tested using the data obtained for the Moscow region and some German stations. Such a model is better than the \u201cclimate\u201d and \u201cinertial\u201d models and can ensure a determination coefficient of the surface ozone anomalies of about 50%.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1134/s102485601004007x", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1136100", 
        "issn": [
          "1024-8560", 
          "2070-0393"
        ], 
        "name": "Atmospheric and Oceanic Optics", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "4", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "23"
      }
    ], 
    "name": "Statistical modeling of daily maximum surface ozone concentrations", 
    "pagination": "284-292", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "513245a7e31eaf472bca76ee30472e3d023da3e3d9e4caa6e03623947a058736"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1134/s102485601004007x"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1017042308"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1134/s102485601004007x", 
      "https://app.dimensions.ai/details/publication/pub.1017042308"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T10:49", 
    "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/v90/0000000350_0000000350/records_77557_00000000.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1134/S102485601004007X"
  }
]
 

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.10.1134/s102485601004007x'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1134/s102485601004007x'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1134/s102485601004007x'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1134/s102485601004007x'


 

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

140 TRIPLES      21 PREDICATES      37 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1134/s102485601004007x schema:about anzsrc-for:04
2 anzsrc-for:0401
3 schema:author N95cd09b6b8af476fb34dc75cda76b712
4 schema:citation https://doi.org/10.1016/0960-1686(91)90262-6
5 https://doi.org/10.1016/j.atmosenv.2004.09.070
6 https://doi.org/10.1016/j.atmosenv.2006.07.039
7 https://doi.org/10.1016/s1352-2310(00)00466-0
8 https://doi.org/10.1016/s1352-2310(98)00345-8
9 https://doi.org/10.1080/10473289.1996.10467439
10 https://doi.org/10.1175/1520-0450(1982)021<1662:aopmvt>2.0.co;2
11 https://doi.org/10.5194/acp-3-941-2003
12 https://doi.org/10.5194/acp-9-2695-2009
13 https://doi.org/10.5194/acpd-5-9003-2005
14 schema:datePublished 2010-08
15 schema:datePublishedReg 2010-08-01
16 schema:description A statistical model of the daily maximum surface ozone concentrations is suggested based on correlations with its predictors. Among the predictors are the temperature; relative humidity; mean wind speed in the planetary boundary layer; concentrations of other trace gases; and the “meteorological pollution potential,” which can characterize adverse (for atmospheric dispersion) meteorological conditions. The statistical model is suitable for surface ozone forecasting; it uses current meteorological parameters, as well as their forecasted values. The most significant predictors of the surface ozone in the Moscow region are the meteorological pollution potential and anomalies (deviations from the norms) of the temperature, relative humidity, and surface ozone on the previous day. The model was tested using the data obtained for the Moscow region and some German stations. Such a model is better than the “climate” and “inertial” models and can ensure a determination coefficient of the surface ozone anomalies of about 50%.
17 schema:genre research_article
18 schema:inLanguage en
19 schema:isAccessibleForFree false
20 schema:isPartOf N6ca40cd661734a489f2265ddc3608eff
21 Ned4d51a05ba64a45ac6abf309768f69a
22 sg:journal.1136100
23 schema:name Statistical modeling of daily maximum surface ozone concentrations
24 schema:pagination 284-292
25 schema:productId N2cfad0cabf38420e97f2256cffb1d1ad
26 N4f53f74de5ad418fa196180127f81561
27 Nfc25096cee5b4f7baadeba07831deb3f
28 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017042308
29 https://doi.org/10.1134/s102485601004007x
30 schema:sdDatePublished 2019-04-11T10:49
31 schema:sdLicense https://scigraph.springernature.com/explorer/license/
32 schema:sdPublisher Nb502f314a35f4ccb8322b7d2a37f042e
33 schema:url http://link.springer.com/10.1134/S102485601004007X
34 sgo:license sg:explorer/license/
35 sgo:sdDataset articles
36 rdf:type schema:ScholarlyArticle
37 N0854f8f017194016aae79914954f0045 rdf:first sg:person.012512461216.79
38 rdf:rest Nbb29ecd2bf38481ab97619f394a33757
39 N2cfad0cabf38420e97f2256cffb1d1ad schema:name dimensions_id
40 schema:value pub.1017042308
41 rdf:type schema:PropertyValue
42 N3943f640b3184d97a5ea83f534f7dbb6 rdf:first sg:person.015455205355.88
43 rdf:rest N0854f8f017194016aae79914954f0045
44 N4f53f74de5ad418fa196180127f81561 schema:name readcube_id
45 schema:value 513245a7e31eaf472bca76ee30472e3d023da3e3d9e4caa6e03623947a058736
46 rdf:type schema:PropertyValue
47 N57a1a2883fc8469683cdf5d487920dbe schema:name Hydrometeorological Center of Russia, B. Predtechenskii per. 9-13, 123242, Moscow, Russia
48 rdf:type schema:Organization
49 N5e9e41f4df104764b7997bb6b951b144 rdf:first sg:person.010233634375.18
50 rdf:rest N94f55fc3921045bf9dee5b6a21eb6293
51 N6bc21702808145c49330eca99a8c055d rdf:first sg:person.015274652731.85
52 rdf:rest N5e9e41f4df104764b7997bb6b951b144
53 N6ca40cd661734a489f2265ddc3608eff schema:volumeNumber 23
54 rdf:type schema:PublicationVolume
55 N94f55fc3921045bf9dee5b6a21eb6293 rdf:first sg:person.011553230457.54
56 rdf:rest N3943f640b3184d97a5ea83f534f7dbb6
57 N95cd09b6b8af476fb34dc75cda76b712 rdf:first sg:person.07503617640.61
58 rdf:rest N6bc21702808145c49330eca99a8c055d
59 Nb502f314a35f4ccb8322b7d2a37f042e schema:name Springer Nature - SN SciGraph project
60 rdf:type schema:Organization
61 Nbb29ecd2bf38481ab97619f394a33757 rdf:first sg:person.016013153025.09
62 rdf:rest rdf:nil
63 Ne9510e57d2114cd49de4573c87e4cf9e schema:name Hydrometeorological Center of Russia, B. Predtechenskii per. 9-13, 123242, Moscow, Russia
64 rdf:type schema:Organization
65 Ned4d51a05ba64a45ac6abf309768f69a schema:issueNumber 4
66 rdf:type schema:PublicationIssue
67 Nfc25096cee5b4f7baadeba07831deb3f schema:name doi
68 schema:value 10.1134/s102485601004007x
69 rdf:type schema:PropertyValue
70 anzsrc-for:04 schema:inDefinedTermSet anzsrc-for:
71 schema:name Earth Sciences
72 rdf:type schema:DefinedTerm
73 anzsrc-for:0401 schema:inDefinedTermSet anzsrc-for:
74 schema:name Atmospheric Sciences
75 rdf:type schema:DefinedTerm
76 sg:journal.1136100 schema:issn 1024-8560
77 2070-0393
78 schema:name Atmospheric and Oceanic Optics
79 rdf:type schema:Periodical
80 sg:person.010233634375.18 schema:affiliation https://www.grid.ac/institutes/grid.4886.2
81 schema:familyName Elanskii
82 schema:givenName N. F.
83 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010233634375.18
84 rdf:type schema:Person
85 sg:person.011553230457.54 schema:affiliation https://www.grid.ac/institutes/grid.433404.4
86 schema:familyName Kakadzhanova
87 schema:givenName G.
88 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011553230457.54
89 rdf:type schema:Person
90 sg:person.012512461216.79 schema:affiliation https://www.grid.ac/institutes/grid.433404.4
91 schema:familyName Tarasova
92 schema:givenName O. A.
93 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012512461216.79
94 rdf:type schema:Person
95 sg:person.015274652731.85 schema:affiliation https://www.grid.ac/institutes/grid.4886.2
96 schema:familyName Belikov
97 schema:givenName I. B.
98 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015274652731.85
99 rdf:type schema:Person
100 sg:person.015455205355.88 schema:affiliation N57a1a2883fc8469683cdf5d487920dbe
101 schema:familyName Kuznetsova
102 schema:givenName I. N.
103 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015455205355.88
104 rdf:type schema:Person
105 sg:person.016013153025.09 schema:affiliation Ne9510e57d2114cd49de4573c87e4cf9e
106 schema:familyName Shalygina
107 schema:givenName I. Yu.
108 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016013153025.09
109 rdf:type schema:Person
110 sg:person.07503617640.61 schema:affiliation https://www.grid.ac/institutes/grid.433404.4
111 schema:familyName Zvyagintsev
112 schema:givenName A. M.
113 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07503617640.61
114 rdf:type schema:Person
115 https://doi.org/10.1016/0960-1686(91)90262-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006002441
116 rdf:type schema:CreativeWork
117 https://doi.org/10.1016/j.atmosenv.2004.09.070 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044234765
118 rdf:type schema:CreativeWork
119 https://doi.org/10.1016/j.atmosenv.2006.07.039 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052748143
120 rdf:type schema:CreativeWork
121 https://doi.org/10.1016/s1352-2310(00)00466-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020810688
122 rdf:type schema:CreativeWork
123 https://doi.org/10.1016/s1352-2310(98)00345-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036039504
124 rdf:type schema:CreativeWork
125 https://doi.org/10.1080/10473289.1996.10467439 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039033496
126 rdf:type schema:CreativeWork
127 https://doi.org/10.1175/1520-0450(1982)021<1662:aopmvt>2.0.co;2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006785933
128 rdf:type schema:CreativeWork
129 https://doi.org/10.5194/acp-3-941-2003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000026913
130 rdf:type schema:CreativeWork
131 https://doi.org/10.5194/acp-9-2695-2009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042878050
132 rdf:type schema:CreativeWork
133 https://doi.org/10.5194/acpd-5-9003-2005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025296214
134 rdf:type schema:CreativeWork
135 https://www.grid.ac/institutes/grid.433404.4 schema:alternateName Roshydromet
136 schema:name Central Aerological Observatory, Pervomaiskaya ul. 3, 141700, Dolgoprudnyi, Moscow region, Russia
137 rdf:type schema:Organization
138 https://www.grid.ac/institutes/grid.4886.2 schema:alternateName Russian Academy of Sciences
139 schema:name Obukhov Institute of Atmospheric Physics, Russian Academy of Sciences, Pyzhevskii per. 3, 109017, Moscow, Russia
140 rdf:type schema:Organization
 




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


...