Data Assimilation for CT-Modelling Based on Optimum Interpolation View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2004

AUTHORS

Johannes Flemming , Eberhard Reimer , Rainer Stern

ABSTRACT

In this work data assimilation of ground based concentration measurements into the fields of the Eulerian transport model REM3 was presented. The assimilation is based on the theoretical framework of Optimum Interpolation. Special emphasis was given to the modelling of the covariance of the background error and the variance of the observation error. An new scheme which accounts for the inhomogeneity of the concentration fields and their measurements was developed. It relies on the discrimination of air quality regimes in both the observations and the calculated fields of the model REM3. The performance of the new approach was compared with that of the “standard” homogeneous and isotropic covariance modelling. The new approach leads to more pronounced gradients in the NO2 concentrations, especially to lower concentration in rural areas, which seem to be more realistic. The work should be considered as example to assess air quality by using all available information, i.e. observations, model results and climatological studies. The techniques of data assimilation are sophisticated tools to combine these information sources in an objective way. In order to apply these techniques, which were mainly developed for numerical weather forecasting, one has to adapt them to the specific problems of the air quality measurements as is was done in this work. More... »

PAGES

255-263

References to SciGraph publications

Book

TITLE

Air Pollution Modeling and Its Application XV

ISBN

978-0-306-47294-7
978-0-306-47813-0

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/0-306-47813-7_25

DOI

http://dx.doi.org/10.1007/0-306-47813-7_25

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1033700854


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/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "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": [
      {
        "familyName": "Flemming", 
        "givenName": "Johannes", 
        "id": "sg:person.016057466540.57", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016057466540.57"
        ], 
        "type": "Person"
      }, 
      {
        "familyName": "Reimer", 
        "givenName": "Eberhard", 
        "id": "sg:person.01323235731.80", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01323235731.80"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Freie Universit\u00e4t Berlin", 
          "id": "https://www.grid.ac/institutes/grid.14095.39", 
          "name": [
            "Institut f\u00fcr Meteorologie, Freie Universit\u00e4t Berlin, 12165\u00a0Berlin, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Stern", 
        "givenName": "Rainer", 
        "id": "sg:person.016646461025.52", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016646461025.52"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1029/97jd01213", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005132181"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4615-3052-7_56", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007748937", 
          "https://doi.org/10.1007/978-1-4615-3052-7_56"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4615-3052-7_56", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007748937", 
          "https://doi.org/10.1007/978-1-4615-3052-7_56"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s1464-1909(01)00085-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009866554"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1600-0870.1986.tb00460.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038941161"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1600-0870.1986.tb00460.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038941161"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s1352-2310(97)00066-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051566734"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2004", 
    "datePublishedReg": "2004-01-01", 
    "description": "In this work data assimilation of ground based concentration measurements into the fields of the Eulerian transport model REM3 was presented. The assimilation is based on the theoretical framework of Optimum Interpolation. Special emphasis was given to the modelling of the covariance of the background error and the variance of the observation error. An new scheme which accounts for the inhomogeneity of the concentration fields and their measurements was developed. It relies on the discrimination of air quality regimes in both the observations and the calculated fields of the model REM3. The performance of the new approach was compared with that of the \u201cstandard\u201d homogeneous and isotropic covariance modelling. The new approach leads to more pronounced gradients in the NO2 concentrations, especially to lower concentration in rural areas, which seem to be more realistic. The work should be considered as example to assess air quality by using all available information, i.e. observations, model results and climatological studies. The techniques of data assimilation are sophisticated tools to combine these information sources in an objective way. In order to apply these techniques, which were mainly developed for numerical weather forecasting, one has to adapt them to the specific problems of the air quality measurements as is was done in this work.", 
    "editor": [
      {
        "familyName": "Borrego", 
        "givenName": "Carlos", 
        "type": "Person"
      }, 
      {
        "familyName": "Schayes", 
        "givenName": "Guy", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/0-306-47813-7_25", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-0-306-47294-7", 
        "978-0-306-47813-0"
      ], 
      "name": "Air Pollution Modeling and Its Application XV", 
      "type": "Book"
    }, 
    "name": "Data Assimilation for CT-Modelling Based on Optimum Interpolation", 
    "pagination": "255-263", 
    "productId": [
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/0-306-47813-7_25"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "a08a4cc8a6c1cd2fd999e826195559a262943648c511b48929da0aef8974833e"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1033700854"
        ]
      }
    ], 
    "publisher": {
      "location": "Boston, MA", 
      "name": "Springer US", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/0-306-47813-7_25", 
      "https://app.dimensions.ai/details/publication/pub.1033700854"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2019-04-15T22:56", 
    "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_8695_00000264.jsonl", 
    "type": "Chapter", 
    "url": "http://link.springer.com/10.1007/0-306-47813-7_25"
  }
]
 

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.10.1007/0-306-47813-7_25'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/0-306-47813-7_25'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/0-306-47813-7_25'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/0-306-47813-7_25'


 

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

98 TRIPLES      23 PREDICATES      32 URIs      20 LITERALS      8 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/0-306-47813-7_25 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author N7a2223e494e749c880bd55c92c11c6e9
4 schema:citation sg:pub.10.1007/978-1-4615-3052-7_56
5 https://doi.org/10.1016/s1352-2310(97)00066-6
6 https://doi.org/10.1016/s1464-1909(01)00085-5
7 https://doi.org/10.1029/97jd01213
8 https://doi.org/10.1111/j.1600-0870.1986.tb00460.x
9 schema:datePublished 2004
10 schema:datePublishedReg 2004-01-01
11 schema:description In this work data assimilation of ground based concentration measurements into the fields of the Eulerian transport model REM3 was presented. The assimilation is based on the theoretical framework of Optimum Interpolation. Special emphasis was given to the modelling of the covariance of the background error and the variance of the observation error. An new scheme which accounts for the inhomogeneity of the concentration fields and their measurements was developed. It relies on the discrimination of air quality regimes in both the observations and the calculated fields of the model REM3. The performance of the new approach was compared with that of the “standard” homogeneous and isotropic covariance modelling. The new approach leads to more pronounced gradients in the NO2 concentrations, especially to lower concentration in rural areas, which seem to be more realistic. The work should be considered as example to assess air quality by using all available information, i.e. observations, model results and climatological studies. The techniques of data assimilation are sophisticated tools to combine these information sources in an objective way. In order to apply these techniques, which were mainly developed for numerical weather forecasting, one has to adapt them to the specific problems of the air quality measurements as is was done in this work.
12 schema:editor N451832a3dcaa4734aa0ae3fbd4a363ec
13 schema:genre chapter
14 schema:inLanguage en
15 schema:isAccessibleForFree false
16 schema:isPartOf N72d476f0446947bbb1c3830b882f664c
17 schema:name Data Assimilation for CT-Modelling Based on Optimum Interpolation
18 schema:pagination 255-263
19 schema:productId N20893fb6b3334af3a3158dc4a7d710f1
20 N2fe1110777eb400bbe0b98ed33c4b9da
21 Nb737c0cedf9d472aad447c00d4512c26
22 schema:publisher N865483e08fcd4e8e94a0f4660d54f098
23 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033700854
24 https://doi.org/10.1007/0-306-47813-7_25
25 schema:sdDatePublished 2019-04-15T22:56
26 schema:sdLicense https://scigraph.springernature.com/explorer/license/
27 schema:sdPublisher Nd2e04c0ce7144822a12e23615096d16e
28 schema:url http://link.springer.com/10.1007/0-306-47813-7_25
29 sgo:license sg:explorer/license/
30 sgo:sdDataset chapters
31 rdf:type schema:Chapter
32 N149e90265a17417ab69535a83ad4f5d9 rdf:first Nc38e20290c1447a0bb3075325ce5e24b
33 rdf:rest rdf:nil
34 N20893fb6b3334af3a3158dc4a7d710f1 schema:name doi
35 schema:value 10.1007/0-306-47813-7_25
36 rdf:type schema:PropertyValue
37 N2fe1110777eb400bbe0b98ed33c4b9da schema:name readcube_id
38 schema:value a08a4cc8a6c1cd2fd999e826195559a262943648c511b48929da0aef8974833e
39 rdf:type schema:PropertyValue
40 N451832a3dcaa4734aa0ae3fbd4a363ec rdf:first N6adf44e79a504f84b1ff2f3606ffb975
41 rdf:rest N149e90265a17417ab69535a83ad4f5d9
42 N6adf44e79a504f84b1ff2f3606ffb975 schema:familyName Borrego
43 schema:givenName Carlos
44 rdf:type schema:Person
45 N72d476f0446947bbb1c3830b882f664c schema:isbn 978-0-306-47294-7
46 978-0-306-47813-0
47 schema:name Air Pollution Modeling and Its Application XV
48 rdf:type schema:Book
49 N7a2223e494e749c880bd55c92c11c6e9 rdf:first sg:person.016057466540.57
50 rdf:rest Nd7c9005386294ecfb4359634f2c83837
51 N865483e08fcd4e8e94a0f4660d54f098 schema:location Boston, MA
52 schema:name Springer US
53 rdf:type schema:Organisation
54 Nb737c0cedf9d472aad447c00d4512c26 schema:name dimensions_id
55 schema:value pub.1033700854
56 rdf:type schema:PropertyValue
57 Nc38e20290c1447a0bb3075325ce5e24b schema:familyName Schayes
58 schema:givenName Guy
59 rdf:type schema:Person
60 Nd2e04c0ce7144822a12e23615096d16e schema:name Springer Nature - SN SciGraph project
61 rdf:type schema:Organization
62 Nd6f8137edac44c0abc7bb711b4f0d7cb rdf:first sg:person.016646461025.52
63 rdf:rest rdf:nil
64 Nd7c9005386294ecfb4359634f2c83837 rdf:first sg:person.01323235731.80
65 rdf:rest Nd6f8137edac44c0abc7bb711b4f0d7cb
66 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
67 schema:name Information and Computing Sciences
68 rdf:type schema:DefinedTerm
69 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
70 schema:name Artificial Intelligence and Image Processing
71 rdf:type schema:DefinedTerm
72 sg:person.01323235731.80 schema:familyName Reimer
73 schema:givenName Eberhard
74 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01323235731.80
75 rdf:type schema:Person
76 sg:person.016057466540.57 schema:familyName Flemming
77 schema:givenName Johannes
78 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016057466540.57
79 rdf:type schema:Person
80 sg:person.016646461025.52 schema:affiliation https://www.grid.ac/institutes/grid.14095.39
81 schema:familyName Stern
82 schema:givenName Rainer
83 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016646461025.52
84 rdf:type schema:Person
85 sg:pub.10.1007/978-1-4615-3052-7_56 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007748937
86 https://doi.org/10.1007/978-1-4615-3052-7_56
87 rdf:type schema:CreativeWork
88 https://doi.org/10.1016/s1352-2310(97)00066-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051566734
89 rdf:type schema:CreativeWork
90 https://doi.org/10.1016/s1464-1909(01)00085-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009866554
91 rdf:type schema:CreativeWork
92 https://doi.org/10.1029/97jd01213 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005132181
93 rdf:type schema:CreativeWork
94 https://doi.org/10.1111/j.1600-0870.1986.tb00460.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1038941161
95 rdf:type schema:CreativeWork
96 https://www.grid.ac/institutes/grid.14095.39 schema:alternateName Freie Universität Berlin
97 schema:name Institut für Meteorologie, Freie Universität Berlin, 12165 Berlin, Germany
98 rdf:type schema:Organization
 




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


...