Land-surface scheme validation using the Oklahoma Atmospheric Surface-layer Instrumentation System (OASIS) and Oklahoma Mesonet data: Preliminary results View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2002-06

AUTHORS

J. A. Brotzge, D. Weber

ABSTRACT

The Oklahoma Atmospheric Surface-layer Instrumentation System (OASIS) is a recently-developed observational system that collects, archives, and quality controls atmospheric, surface, and soil data in real-time from 90 stations across Oklahoma. Ten of the 90 sites, termed “super sites”, are equipped with additional sonic anemometry and four-component net radiometers to provide complete observations of the surface energy balance. Oklahoma Mesonet and OASIS data are used in this study to validate the sensitivity and accuracy of a land-surface scheme within a numerical prediction model. The Advanced Regional Prediction System (ARPS) is a three-dimensional, nonhydrostatic mesoscale model developed by the Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma. The land-surface model (LSM) used within ARPS is the Interactions Soil Biosphere Atmosphere (ISBA) scheme. Mesonet and OASIS data collected from the super site located in Norman, Oklahoma, are used as verification for the ISBA. Research presented in this study outlines the challenges in developing, maintaining, and using in-situ data for model validation. Such problems as instrument error, surface heterogeneity, and non-closure of the surface energy budget limit data accuracy. Preliminary results of model validation focus on the sensitivity of the soil physics within the ISBA scheme. Model sensitivity to vegetation cover, surface roughness, and soil type are investigated. Furthermore, several recent improvements to ISBA are evaluated and compared to observations. This study concludes that the sensitivity of the ISBA to a priori soil and vegetation type is detrimental for this scheme to be used in a mesoscale model without improved treatment of surface heterogeneity. More... »

PAGES

189-206

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s007030200025

DOI

http://dx.doi.org/10.1007/s007030200025

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1039657848


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/2008/0503", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Soil Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/05", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Environmental Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "University of Oklahoma", 
          "id": "https://www.grid.ac/institutes/grid.266900.b", 
          "name": [
            "Center for Analysis and Prediction of Storms (CAPS), University of Oklahoma, Norman, Oklahoma, US"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Brotzge", 
        "givenName": "J. A.", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Oklahoma", 
          "id": "https://www.grid.ac/institutes/grid.266900.b", 
          "name": [
            "Center for Analysis and Prediction of Storms (CAPS), University of Oklahoma, Norman, Oklahoma, US"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Weber", 
        "givenName": "D.", 
        "type": "Person"
      }
    ], 
    "datePublished": "2002-06", 
    "datePublishedReg": "2002-06-01", 
    "description": "The Oklahoma Atmospheric Surface-layer Instrumentation System (OASIS) is a recently-developed observational system that collects, archives, and quality controls atmospheric, surface, and soil data in real-time from 90 stations across Oklahoma. Ten of the 90 sites, termed \u201csuper sites\u201d, are equipped with additional sonic anemometry and four-component net radiometers to provide complete observations of the surface energy balance. Oklahoma Mesonet and OASIS data are used in this study to validate the sensitivity and accuracy of a land-surface scheme within a numerical prediction model. The Advanced Regional Prediction System (ARPS) is a three-dimensional, nonhydrostatic mesoscale model developed by the Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma. The land-surface model (LSM) used within ARPS is the Interactions Soil Biosphere Atmosphere (ISBA) scheme. Mesonet and OASIS data collected from the super site located in Norman, Oklahoma, are used as verification for the ISBA. Research presented in this study outlines the challenges in developing, maintaining, and using in-situ data for model validation. Such problems as instrument error, surface heterogeneity, and non-closure of the surface energy budget limit data accuracy. Preliminary results of model validation focus on the sensitivity of the soil physics within the ISBA scheme. Model sensitivity to vegetation cover, surface roughness, and soil type are investigated. Furthermore, several recent improvements to ISBA are evaluated and compared to observations. This study concludes that the sensitivity of the ISBA to a priori soil and vegetation type is detrimental for this scheme to be used in a mesoscale model without improved treatment of surface heterogeneity.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s007030200025", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1271293", 
        "issn": [
          "0177-7971", 
          "1436-5065"
        ], 
        "name": "Meteorology and Atmospheric Physics", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "80"
      }
    ], 
    "name": "Land-surface scheme validation using the Oklahoma Atmospheric Surface-layer Instrumentation System (OASIS) and Oklahoma Mesonet data: Preliminary results", 
    "pagination": "189-206", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "bce9874d3fc491c47d324085b5c251d5827ba1e64913fcf8f68faf8f736458f3"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s007030200025"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1039657848"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s007030200025", 
      "https://app.dimensions.ai/details/publication/pub.1039657848"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T19:04", 
    "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/v90/0000000001_0000000264/records_8678_00000490.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007/s007030200025"
  }
]
 

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/s007030200025'

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/s007030200025'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s007030200025'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s007030200025'


 

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

66 TRIPLES      20 PREDICATES      27 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s007030200025 schema:about anzsrc-for:05
2 anzsrc-for:0503
3 schema:author N05d4e95accd344ac8fb68e67714ed036
4 schema:datePublished 2002-06
5 schema:datePublishedReg 2002-06-01
6 schema:description The Oklahoma Atmospheric Surface-layer Instrumentation System (OASIS) is a recently-developed observational system that collects, archives, and quality controls atmospheric, surface, and soil data in real-time from 90 stations across Oklahoma. Ten of the 90 sites, termed “super sites”, are equipped with additional sonic anemometry and four-component net radiometers to provide complete observations of the surface energy balance. Oklahoma Mesonet and OASIS data are used in this study to validate the sensitivity and accuracy of a land-surface scheme within a numerical prediction model. The Advanced Regional Prediction System (ARPS) is a three-dimensional, nonhydrostatic mesoscale model developed by the Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma. The land-surface model (LSM) used within ARPS is the Interactions Soil Biosphere Atmosphere (ISBA) scheme. Mesonet and OASIS data collected from the super site located in Norman, Oklahoma, are used as verification for the ISBA. Research presented in this study outlines the challenges in developing, maintaining, and using in-situ data for model validation. Such problems as instrument error, surface heterogeneity, and non-closure of the surface energy budget limit data accuracy. Preliminary results of model validation focus on the sensitivity of the soil physics within the ISBA scheme. Model sensitivity to vegetation cover, surface roughness, and soil type are investigated. Furthermore, several recent improvements to ISBA are evaluated and compared to observations. This study concludes that the sensitivity of the ISBA to a priori soil and vegetation type is detrimental for this scheme to be used in a mesoscale model without improved treatment of surface heterogeneity.
7 schema:genre research_article
8 schema:inLanguage en
9 schema:isAccessibleForFree false
10 schema:isPartOf N35c31be655234edb885c6df1e556b3e1
11 Nad595cb2ac1146bda7aecdc94517f696
12 sg:journal.1271293
13 schema:name Land-surface scheme validation using the Oklahoma Atmospheric Surface-layer Instrumentation System (OASIS) and Oklahoma Mesonet data: Preliminary results
14 schema:pagination 189-206
15 schema:productId N3b385af378e24cda8f3a24356314ceb1
16 N854b7f77b776405fa9d7d03a684b14b2
17 Na6f69499fcb14a058204fbbae3a4fdd9
18 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039657848
19 https://doi.org/10.1007/s007030200025
20 schema:sdDatePublished 2019-04-10T19:04
21 schema:sdLicense https://scigraph.springernature.com/explorer/license/
22 schema:sdPublisher N3364384a1da94fc3b151eff24cddf7ea
23 schema:url http://link.springer.com/10.1007/s007030200025
24 sgo:license sg:explorer/license/
25 sgo:sdDataset articles
26 rdf:type schema:ScholarlyArticle
27 N05d4e95accd344ac8fb68e67714ed036 rdf:first Nab6bd09ce020487996ddb7b6a0e88847
28 rdf:rest N1b677e638d684ee786da6e23d5b0b178
29 N1b677e638d684ee786da6e23d5b0b178 rdf:first Ndd01334477584ba9b4f6af8d9dcf5aa9
30 rdf:rest rdf:nil
31 N3364384a1da94fc3b151eff24cddf7ea schema:name Springer Nature - SN SciGraph project
32 rdf:type schema:Organization
33 N35c31be655234edb885c6df1e556b3e1 schema:volumeNumber 80
34 rdf:type schema:PublicationVolume
35 N3b385af378e24cda8f3a24356314ceb1 schema:name readcube_id
36 schema:value bce9874d3fc491c47d324085b5c251d5827ba1e64913fcf8f68faf8f736458f3
37 rdf:type schema:PropertyValue
38 N854b7f77b776405fa9d7d03a684b14b2 schema:name dimensions_id
39 schema:value pub.1039657848
40 rdf:type schema:PropertyValue
41 Na6f69499fcb14a058204fbbae3a4fdd9 schema:name doi
42 schema:value 10.1007/s007030200025
43 rdf:type schema:PropertyValue
44 Nab6bd09ce020487996ddb7b6a0e88847 schema:affiliation https://www.grid.ac/institutes/grid.266900.b
45 schema:familyName Brotzge
46 schema:givenName J. A.
47 rdf:type schema:Person
48 Nad595cb2ac1146bda7aecdc94517f696 schema:issueNumber 1
49 rdf:type schema:PublicationIssue
50 Ndd01334477584ba9b4f6af8d9dcf5aa9 schema:affiliation https://www.grid.ac/institutes/grid.266900.b
51 schema:familyName Weber
52 schema:givenName D.
53 rdf:type schema:Person
54 anzsrc-for:05 schema:inDefinedTermSet anzsrc-for:
55 schema:name Environmental Sciences
56 rdf:type schema:DefinedTerm
57 anzsrc-for:0503 schema:inDefinedTermSet anzsrc-for:
58 schema:name Soil Sciences
59 rdf:type schema:DefinedTerm
60 sg:journal.1271293 schema:issn 0177-7971
61 1436-5065
62 schema:name Meteorology and Atmospheric Physics
63 rdf:type schema:Periodical
64 https://www.grid.ac/institutes/grid.266900.b schema:alternateName University of Oklahoma
65 schema:name Center for Analysis and Prediction of Storms (CAPS), University of Oklahoma, Norman, Oklahoma, US
66 rdf:type schema:Organization
 




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


...