Impacts of cloud and turbulence schemes on integrated water vapor: comparison between model predictions and GPS measurements View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2001-06

AUTHORS

G. Lenderink, E. van Meijgaard

ABSTRACT

Summary Structures in atmospheric Integrated Water Vapor (IWV) have been studied for the three successive cyclones, Kerstin, Liane and Monika, which controlled the meteorological conditions in the Baltic Sea catchment region in the period from 28 August to 5 September 1995 (part of the PIDCAP observational campaign defined within BALTEX). Several model predictions of these cyclones have been performed with a regional atmospheric general circulation model (RACMO). The impact of two different versions of the model physics package (standard ECHAM4 and a revised version with modifications in the cloud and turbulence scheme) has been investigated. Model predicted IWV has been evaluated with GPS station data from several stations in Sweden and Finland. For the most strongly developed cyclone Monika, the revised scheme generates more pronounced IWV structures, with well defined bands of high and low values of IWV curving into the center of the cyclone. In particular, the shape of the minima are in better agreement with the GPS station data, and the consistency between two subsequent model forecasts is also larger with the revised physics package. For the weaker systems, Kerstin and Liane, results from both model versions are very similar. More... »

PAGES

131-144

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s007030170022

DOI

http://dx.doi.org/10.1007/s007030170022

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1011521493


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": "Royal Netherlands Meteorological Institute (KNMI), De Bilt, The Netherlands, NL", 
          "id": "http://www.grid.ac/institutes/grid.8653.8", 
          "name": [
            "Royal Netherlands Meteorological Institute (KNMI), De Bilt, The Netherlands, NL"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lenderink", 
        "givenName": "G.", 
        "id": "sg:person.010547300007.01", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010547300007.01"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Royal Netherlands Meteorological Institute (KNMI), De Bilt, The Netherlands, NL", 
          "id": "http://www.grid.ac/institutes/grid.8653.8", 
          "name": [
            "Royal Netherlands Meteorological Institute (KNMI), De Bilt, The Netherlands, NL"
          ], 
          "type": "Organization"
        }, 
        "familyName": "van Meijgaard", 
        "givenName": "E.", 
        "id": "sg:person.016410121107.52", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016410121107.52"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2001-06", 
    "datePublishedReg": "2001-06-01", 
    "description": "Summary Structures in atmospheric Integrated Water Vapor (IWV) have been studied for the three successive cyclones, Kerstin, Liane and Monika, which controlled the meteorological conditions in the Baltic Sea catchment region in the period from 28 August to 5 September 1995 (part of the PIDCAP observational campaign defined within BALTEX). Several model predictions of these cyclones have been performed with a regional atmospheric general circulation model (RACMO). The impact of two different versions of the model physics package (standard ECHAM4 and a revised version with modifications in the cloud and turbulence scheme) has been investigated. Model predicted IWV has been evaluated with GPS station data from several stations in Sweden and Finland. For the most strongly developed cyclone Monika, the revised scheme generates more pronounced IWV structures, with well defined bands of high and low values of IWV curving into the center of the cyclone. In particular, the shape of the minima are in better agreement with the GPS station data, and the consistency between two subsequent model forecasts is also larger with the revised physics package. For the weaker systems, Kerstin and Liane, results from both model versions are very similar.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s007030170022", 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1271293", 
        "issn": [
          "0177-7971", 
          "1436-5065"
        ], 
        "name": "Meteorology and Atmospheric Physics", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "77"
      }
    ], 
    "keywords": [
      "integrated water vapor", 
      "GPS station data", 
      "station data", 
      "atmospheric general circulation model", 
      "physics package", 
      "water vapor", 
      "atmospheric integrated water vapor", 
      "subsequent model forecasts", 
      "general circulation model", 
      "impact of clouds", 
      "circulation model", 
      "successive cyclones", 
      "model forecasts", 
      "turbulence scheme", 
      "GPS measurements", 
      "catchment region", 
      "model versions", 
      "model predictions", 
      "meteorological conditions", 
      "cyclones", 
      "Kerstin", 
      "weak system", 
      "lower values", 
      "vapor", 
      "forecasts", 
      "stations", 
      "lianes", 
      "cloud", 
      "good agreement", 
      "impact", 
      "prediction", 
      "region", 
      "data", 
      "Finland", 
      "model", 
      "period", 
      "Sweden", 
      "minimum", 
      "measurements", 
      "agreement", 
      "conditions", 
      "package", 
      "structure", 
      "different versions", 
      "values", 
      "comparison", 
      "Monika", 
      "center", 
      "scheme", 
      "version", 
      "band", 
      "consistency", 
      "curving", 
      "shape", 
      "results", 
      "system", 
      "summary structure"
    ], 
    "name": "Impacts of cloud and turbulence schemes on integrated water vapor: comparison between model predictions and GPS measurements", 
    "pagination": "131-144", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1011521493"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s007030170022"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s007030170022", 
      "https://app.dimensions.ai/details/publication/pub.1011521493"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-09-02T15:49", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220902/entities/gbq_results/article/article_332.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s007030170022"
  }
]
 

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/s007030170022'

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/s007030170022'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s007030170022'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s007030170022'


 

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

121 TRIPLES      20 PREDICATES      82 URIs      74 LITERALS      6 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s007030170022 schema:about anzsrc-for:04
2 anzsrc-for:0401
3 schema:author N6a3d9175df624950bbcbc7418b680aae
4 schema:datePublished 2001-06
5 schema:datePublishedReg 2001-06-01
6 schema:description Summary Structures in atmospheric Integrated Water Vapor (IWV) have been studied for the three successive cyclones, Kerstin, Liane and Monika, which controlled the meteorological conditions in the Baltic Sea catchment region in the period from 28 August to 5 September 1995 (part of the PIDCAP observational campaign defined within BALTEX). Several model predictions of these cyclones have been performed with a regional atmospheric general circulation model (RACMO). The impact of two different versions of the model physics package (standard ECHAM4 and a revised version with modifications in the cloud and turbulence scheme) has been investigated. Model predicted IWV has been evaluated with GPS station data from several stations in Sweden and Finland. For the most strongly developed cyclone Monika, the revised scheme generates more pronounced IWV structures, with well defined bands of high and low values of IWV curving into the center of the cyclone. In particular, the shape of the minima are in better agreement with the GPS station data, and the consistency between two subsequent model forecasts is also larger with the revised physics package. For the weaker systems, Kerstin and Liane, results from both model versions are very similar.
7 schema:genre article
8 schema:isAccessibleForFree false
9 schema:isPartOf N413a5a21578e4582b84485cf3ac61bfd
10 N481d85f961784979bc580f95cfb230cb
11 sg:journal.1271293
12 schema:keywords Finland
13 GPS measurements
14 GPS station data
15 Kerstin
16 Monika
17 Sweden
18 agreement
19 atmospheric general circulation model
20 atmospheric integrated water vapor
21 band
22 catchment region
23 center
24 circulation model
25 cloud
26 comparison
27 conditions
28 consistency
29 curving
30 cyclones
31 data
32 different versions
33 forecasts
34 general circulation model
35 good agreement
36 impact
37 impact of clouds
38 integrated water vapor
39 lianes
40 lower values
41 measurements
42 meteorological conditions
43 minimum
44 model
45 model forecasts
46 model predictions
47 model versions
48 package
49 period
50 physics package
51 prediction
52 region
53 results
54 scheme
55 shape
56 station data
57 stations
58 structure
59 subsequent model forecasts
60 successive cyclones
61 summary structure
62 system
63 turbulence scheme
64 values
65 vapor
66 version
67 water vapor
68 weak system
69 schema:name Impacts of cloud and turbulence schemes on integrated water vapor: comparison between model predictions and GPS measurements
70 schema:pagination 131-144
71 schema:productId N522b31a624c74d7fb1cd53275fb272a0
72 N9655b0237bcc4acb915576b9a5fd38e3
73 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011521493
74 https://doi.org/10.1007/s007030170022
75 schema:sdDatePublished 2022-09-02T15:49
76 schema:sdLicense https://scigraph.springernature.com/explorer/license/
77 schema:sdPublisher N20c3dece90e74c7997bb8117156d2679
78 schema:url https://doi.org/10.1007/s007030170022
79 sgo:license sg:explorer/license/
80 sgo:sdDataset articles
81 rdf:type schema:ScholarlyArticle
82 N20c3dece90e74c7997bb8117156d2679 schema:name Springer Nature - SN SciGraph project
83 rdf:type schema:Organization
84 N413a5a21578e4582b84485cf3ac61bfd schema:issueNumber 1
85 rdf:type schema:PublicationIssue
86 N481d85f961784979bc580f95cfb230cb schema:volumeNumber 77
87 rdf:type schema:PublicationVolume
88 N522b31a624c74d7fb1cd53275fb272a0 schema:name doi
89 schema:value 10.1007/s007030170022
90 rdf:type schema:PropertyValue
91 N6a3d9175df624950bbcbc7418b680aae rdf:first sg:person.010547300007.01
92 rdf:rest Nc50593302fa14417a312e352791cc577
93 N9655b0237bcc4acb915576b9a5fd38e3 schema:name dimensions_id
94 schema:value pub.1011521493
95 rdf:type schema:PropertyValue
96 Nc50593302fa14417a312e352791cc577 rdf:first sg:person.016410121107.52
97 rdf:rest rdf:nil
98 anzsrc-for:04 schema:inDefinedTermSet anzsrc-for:
99 schema:name Earth Sciences
100 rdf:type schema:DefinedTerm
101 anzsrc-for:0401 schema:inDefinedTermSet anzsrc-for:
102 schema:name Atmospheric Sciences
103 rdf:type schema:DefinedTerm
104 sg:journal.1271293 schema:issn 0177-7971
105 1436-5065
106 schema:name Meteorology and Atmospheric Physics
107 schema:publisher Springer Nature
108 rdf:type schema:Periodical
109 sg:person.010547300007.01 schema:affiliation grid-institutes:grid.8653.8
110 schema:familyName Lenderink
111 schema:givenName G.
112 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010547300007.01
113 rdf:type schema:Person
114 sg:person.016410121107.52 schema:affiliation grid-institutes:grid.8653.8
115 schema:familyName van Meijgaard
116 schema:givenName E.
117 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016410121107.52
118 rdf:type schema:Person
119 grid-institutes:grid.8653.8 schema:alternateName Royal Netherlands Meteorological Institute (KNMI), De Bilt, The Netherlands, NL
120 schema:name Royal Netherlands Meteorological Institute (KNMI), De Bilt, The Netherlands, NL
121 rdf:type schema:Organization
 




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


...