The Advanced Regional Prediction System (ARPS) – A multi-scale nonhydrostatic atmospheric simulation and prediction model. Part I: Model dynamics and ... View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2000-12

AUTHORS

M. Xue, K. K. Droegemeier, V. Wong

ABSTRACT

A completely new nonhydrostatic model system known as the Advanced Regional Prediction System (ARPS) has been developed in recent years at the Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma. The ARPS is designed from the beginning to serve as an effective tool for basic and applied research and as a system suitable for explicit prediction of convective storms as well as weather systems at other scales. The ARPS includes its own data ingest, quality control and objective analysis packages, a data assimilation system which includes single-Doppler velocity and thermodynamic retrieval algorithms, the forward prediction component, and a self-contained post-processing, diagnostic and verification package. The forward prediction component of the ARPS is a three-dimensional, nonhydrostatic compressible model formulated in generalized terrain-following coordinates. Minimum approximations are made to the original governing equations. The split-explicit scheme is used to integrate the sound-wave containing equations, which allows the horizontal domain-decomposition strategy to be efficiently implemented for distributed-memory massively parallel computers. The model performs equally well on conventional shared-memory scalar and vector processors. The model employs advanced numerical techniques, including monotonic advection schemes for scalar transport and variance-conserving fourth-order advection for other variables. The model also includes state-of-the-art physics parameterization schemes that are important for explicit prediction of convective storms as well as the prediction of flows at larger scales. Unique to this system are the consistent code styling maintained for the entire model system and thorough internal documentation. Modern software engineering practices are employed to ensure that the system is modular, extensible and easy to use. The system has been undergoing real-time prediction tests at the synoptic through storm scales in the past several years over the continental United States as well as in part of Asia, some of which included retrieved Doppler radar data and hydrometeor types in the initial condition. As the first of a two-part paper series, we describe herein the dynamic and numerical framework of the model, together with the subgrid-scale turbulence and the PBL parameterization. The model dynamic and numerical framework is then verified using idealized and realistic mountain flow cases and an idealized density current. Other physics parameterization schemes will be described in Part II, which is followed by verification against observational data of the coupled soil-vegetation model, surface layer fluxes and the PBL parameterization. Applications of the model to the simulation of an observed supercell storm and to the prediction of a real case are also found in Part II. In the latter case, a long-lasting squall line developed and propagated across the eastern part of the United States following a historical number of tornado outbreak in the state of Arkansas. More... »

PAGES

161-193

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s007030070003

DOI

http://dx.doi.org/10.1007/s007030070003

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1024606075


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/2008/0803", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Computer Software", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing 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, University of Oklahoma, Norman OK 73019"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Xue", 
        "givenName": "M.", 
        "id": "sg:person.01155124164.00", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01155124164.00"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Oklahoma", 
          "id": "https://www.grid.ac/institutes/grid.266900.b", 
          "name": [
            "Center for Analysis and Prediction of Storms, University of Oklahoma, Norman OK 73019"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Droegemeier", 
        "givenName": "K. K.", 
        "id": "sg:person.01041222474.74", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01041222474.74"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Oklahoma", 
          "id": "https://www.grid.ac/institutes/grid.266900.b", 
          "name": [
            "Center for Analysis and Prediction of Storms, University of Oklahoma, Norman OK 73019"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wong", 
        "givenName": "V.", 
        "id": "sg:person.016520652515.31", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016520652515.31"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/b978-0-12-460817-7.50009-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047904090"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2000-12", 
    "datePublishedReg": "2000-12-01", 
    "description": "A completely new nonhydrostatic model system known as the Advanced Regional Prediction System (ARPS) has been developed in recent years at the Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma. The ARPS is designed from the beginning to serve as an effective tool for basic and applied research and as a system suitable for explicit prediction of convective storms as well as weather systems at other scales. The ARPS includes its own data ingest, quality control and objective analysis packages, a data assimilation system which includes single-Doppler velocity and thermodynamic retrieval algorithms, the forward prediction component, and a self-contained post-processing, diagnostic and verification package. The forward prediction component of the ARPS is a three-dimensional, nonhydrostatic compressible model formulated in generalized terrain-following coordinates. Minimum approximations are made to the original governing equations. The split-explicit scheme is used to integrate the sound-wave containing equations, which allows the horizontal domain-decomposition strategy to be efficiently implemented for distributed-memory massively parallel computers. The model performs equally well on conventional shared-memory scalar and vector processors. The model employs advanced numerical techniques, including monotonic advection schemes for scalar transport and variance-conserving fourth-order advection for other variables. The model also includes state-of-the-art physics parameterization schemes that are important for explicit prediction of convective storms as well as the prediction of flows at larger scales. Unique to this system are the consistent code styling maintained for the entire model system and thorough internal documentation. Modern software engineering practices are employed to ensure that the system is modular, extensible and easy to use. The system has been undergoing real-time prediction tests at the synoptic through storm scales in the past several years over the continental United States as well as in part of Asia, some of which included retrieved Doppler radar data and hydrometeor types in the initial condition. As the first of a two-part paper series, we describe herein the dynamic and numerical framework of the model, together with the subgrid-scale turbulence and the PBL parameterization. The model dynamic and numerical framework is then verified using idealized and realistic mountain flow cases and an idealized density current. Other physics parameterization schemes will be described in Part II, which is followed by verification against observational data of the coupled soil-vegetation model, surface layer fluxes and the PBL parameterization. Applications of the model to the simulation of an observed supercell storm and to the prediction of a real case are also found in Part II. In the latter case, a long-lasting squall line developed and propagated across the eastern part of the United States following a historical number of tornado outbreak in the state of Arkansas.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s007030070003", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1271293", 
        "issn": [
          "0177-7971", 
          "1436-5065"
        ], 
        "name": "Meteorology and Atmospheric Physics", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "3-4", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "75"
      }
    ], 
    "name": "The Advanced Regional Prediction System (ARPS) \u2013 A multi-scale nonhydrostatic atmospheric simulation and prediction model. Part I: Model dynamics and verification", 
    "pagination": "161-193", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "753950b06217b39bd9395540b103e5aa3ec610b4cccaf7e4fa18a27e6ffd3dcf"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s007030070003"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1024606075"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s007030070003", 
      "https://app.dimensions.ai/details/publication/pub.1024606075"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T23:19", 
    "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_8693_00000488.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007/s007030070003"
  }
]
 

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/s007030070003'

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/s007030070003'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s007030070003'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s007030070003'


 

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

78 TRIPLES      21 PREDICATES      28 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s007030070003 schema:about anzsrc-for:08
2 anzsrc-for:0803
3 schema:author Nf4cce065ee00417192f29f868f09ab05
4 schema:citation https://doi.org/10.1016/b978-0-12-460817-7.50009-4
5 schema:datePublished 2000-12
6 schema:datePublishedReg 2000-12-01
7 schema:description A completely new nonhydrostatic model system known as the Advanced Regional Prediction System (ARPS) has been developed in recent years at the Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma. The ARPS is designed from the beginning to serve as an effective tool for basic and applied research and as a system suitable for explicit prediction of convective storms as well as weather systems at other scales. The ARPS includes its own data ingest, quality control and objective analysis packages, a data assimilation system which includes single-Doppler velocity and thermodynamic retrieval algorithms, the forward prediction component, and a self-contained post-processing, diagnostic and verification package. The forward prediction component of the ARPS is a three-dimensional, nonhydrostatic compressible model formulated in generalized terrain-following coordinates. Minimum approximations are made to the original governing equations. The split-explicit scheme is used to integrate the sound-wave containing equations, which allows the horizontal domain-decomposition strategy to be efficiently implemented for distributed-memory massively parallel computers. The model performs equally well on conventional shared-memory scalar and vector processors. The model employs advanced numerical techniques, including monotonic advection schemes for scalar transport and variance-conserving fourth-order advection for other variables. The model also includes state-of-the-art physics parameterization schemes that are important for explicit prediction of convective storms as well as the prediction of flows at larger scales. Unique to this system are the consistent code styling maintained for the entire model system and thorough internal documentation. Modern software engineering practices are employed to ensure that the system is modular, extensible and easy to use. The system has been undergoing real-time prediction tests at the synoptic through storm scales in the past several years over the continental United States as well as in part of Asia, some of which included retrieved Doppler radar data and hydrometeor types in the initial condition. As the first of a two-part paper series, we describe herein the dynamic and numerical framework of the model, together with the subgrid-scale turbulence and the PBL parameterization. The model dynamic and numerical framework is then verified using idealized and realistic mountain flow cases and an idealized density current. Other physics parameterization schemes will be described in Part II, which is followed by verification against observational data of the coupled soil-vegetation model, surface layer fluxes and the PBL parameterization. Applications of the model to the simulation of an observed supercell storm and to the prediction of a real case are also found in Part II. In the latter case, a long-lasting squall line developed and propagated across the eastern part of the United States following a historical number of tornado outbreak in the state of Arkansas.
8 schema:genre research_article
9 schema:inLanguage en
10 schema:isAccessibleForFree false
11 schema:isPartOf N584870096ba14920a4eb48f97bfda82f
12 Nfd729506ae8d4838919f23528647c283
13 sg:journal.1271293
14 schema:name The Advanced Regional Prediction System (ARPS) – A multi-scale nonhydrostatic atmospheric simulation and prediction model. Part I: Model dynamics and verification
15 schema:pagination 161-193
16 schema:productId N55a83809e9724dd39076b367360fae43
17 N6a5d877703454adda1fe06f14631b11b
18 N7e81ff0ce3c346b3915ba7bbb018c80c
19 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024606075
20 https://doi.org/10.1007/s007030070003
21 schema:sdDatePublished 2019-04-10T23:19
22 schema:sdLicense https://scigraph.springernature.com/explorer/license/
23 schema:sdPublisher Na3511367390f45de9ea20544840fe178
24 schema:url http://link.springer.com/10.1007/s007030070003
25 sgo:license sg:explorer/license/
26 sgo:sdDataset articles
27 rdf:type schema:ScholarlyArticle
28 N55a83809e9724dd39076b367360fae43 schema:name dimensions_id
29 schema:value pub.1024606075
30 rdf:type schema:PropertyValue
31 N584870096ba14920a4eb48f97bfda82f schema:volumeNumber 75
32 rdf:type schema:PublicationVolume
33 N64e8525e4a3a44bfb37f78dd8da2a7b3 rdf:first sg:person.01041222474.74
34 rdf:rest Nb1011fadd2844378995dcee149111637
35 N6a5d877703454adda1fe06f14631b11b schema:name readcube_id
36 schema:value 753950b06217b39bd9395540b103e5aa3ec610b4cccaf7e4fa18a27e6ffd3dcf
37 rdf:type schema:PropertyValue
38 N7e81ff0ce3c346b3915ba7bbb018c80c schema:name doi
39 schema:value 10.1007/s007030070003
40 rdf:type schema:PropertyValue
41 Na3511367390f45de9ea20544840fe178 schema:name Springer Nature - SN SciGraph project
42 rdf:type schema:Organization
43 Nb1011fadd2844378995dcee149111637 rdf:first sg:person.016520652515.31
44 rdf:rest rdf:nil
45 Nf4cce065ee00417192f29f868f09ab05 rdf:first sg:person.01155124164.00
46 rdf:rest N64e8525e4a3a44bfb37f78dd8da2a7b3
47 Nfd729506ae8d4838919f23528647c283 schema:issueNumber 3-4
48 rdf:type schema:PublicationIssue
49 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
50 schema:name Information and Computing Sciences
51 rdf:type schema:DefinedTerm
52 anzsrc-for:0803 schema:inDefinedTermSet anzsrc-for:
53 schema:name Computer Software
54 rdf:type schema:DefinedTerm
55 sg:journal.1271293 schema:issn 0177-7971
56 1436-5065
57 schema:name Meteorology and Atmospheric Physics
58 rdf:type schema:Periodical
59 sg:person.01041222474.74 schema:affiliation https://www.grid.ac/institutes/grid.266900.b
60 schema:familyName Droegemeier
61 schema:givenName K. K.
62 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01041222474.74
63 rdf:type schema:Person
64 sg:person.01155124164.00 schema:affiliation https://www.grid.ac/institutes/grid.266900.b
65 schema:familyName Xue
66 schema:givenName M.
67 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01155124164.00
68 rdf:type schema:Person
69 sg:person.016520652515.31 schema:affiliation https://www.grid.ac/institutes/grid.266900.b
70 schema:familyName Wong
71 schema:givenName V.
72 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016520652515.31
73 rdf:type schema:Person
74 https://doi.org/10.1016/b978-0-12-460817-7.50009-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047904090
75 rdf:type schema:CreativeWork
76 https://www.grid.ac/institutes/grid.266900.b schema:alternateName University of Oklahoma
77 schema:name Center for Analysis and Prediction of Storms, University of Oklahoma, Norman OK 73019
78 rdf:type schema:Organization
 




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


...