Assimilation of land surface temperature into the land surface model JULES with an ensemble Kalman filter
Identifieur interne : 000048 ( Main/Exploration ); précédent : 000047; suivant : 000049Assimilation of land surface temperature into the land surface model JULES with an ensemble Kalman filter
Auteurs : D. Ghent [Royaume-Uni] ; J. Kaduk [Royaume-Uni] ; J. Remedios [Royaume-Uni] ; J. Ardö [Suède] ; H. Balzter [Royaume-Uni]Source :
- Journal of Geophysical Research: Atmospheres [ 0148-0227 ] ; 2010-10-16.
Abstract
Land surface models have uncertainties due to their approximation of physical processes and the heterogeneity of the land surface. These can be compounded when key variables are inadequately represented. Land surface temperature (LST) is critical as it forms an integral component in the surface energy budget, water stress evaluation, fuel moisture derivation, and soil moisture–climate feedbacks. A reduction in the uncertainty of surface energy fluxes, and moisture quantification, is assumed to be achievable by constraining simulations of LST with observation data. This technique is known as data assimilation and involves the adjustment of the model state at observation times with measurements of a predictable uncertainty. In this paper, the validity of LST simulations in a regionalized parameterization of the land surface model Joint UK Land Environment Simulator (JULES) for Africa is assessed by way of a multitemporal intercomparison study with the Moderate Resolution Imaging Spectroradiometer (MODIS), the Advanced Along Track Scanning Radiometer (AATSR), and the Spinning Enhanced Visible and Infrared Imager (SEVIRI) thermal products, with a two‐thirds reduction in model bias found when soil properties are reparameterized. A data assimilation experiment of SEVIRI LST into the JULES model via an ensemble Kalman filter shows an improvement in the modeled LST, soil moisture, and latent and sensible heat fluxes. This paper presents the first investigation into reducing the uncertainty in modeling energy and water fluxes with the United Kingdom's most important land surface model, JULES, by means of data assimilation of LST.
Url:
DOI: 10.1029/2010JD014392
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
<record><TEI wicri:istexFullTextTei="biblStruct"><teiHeader><fileDesc><titleStmt><title xml:lang="en">Assimilation of land surface temperature into the land surface model JULES with an ensemble Kalman filter</title><author><name sortKey="Ghent, D" sort="Ghent, D" uniqKey="Ghent D" first="D." last="Ghent">D. Ghent</name></author><author><name sortKey="Kaduk, J" sort="Kaduk, J" uniqKey="Kaduk J" first="J." last="Kaduk">J. Kaduk</name></author><author><name sortKey="Remedios, J" sort="Remedios, J" uniqKey="Remedios J" first="J." last="Remedios">J. Remedios</name></author><author><name sortKey="Ardo, J" sort="Ardo, J" uniqKey="Ardo J" first="J." last="Ardö">J. Ardö</name></author><author><name sortKey="Balzter, H" sort="Balzter, H" uniqKey="Balzter H" first="H." last="Balzter">H. Balzter</name></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:169D012DD9AB149DC0EC5FE5430B4837136BD4AD</idno><date when="2010" year="2010">2010</date><idno type="doi">10.1029/2010JD014392</idno><idno type="url">https://api.istex.fr/document/169D012DD9AB149DC0EC5FE5430B4837136BD4AD/fulltext/pdf</idno><idno type="wicri:Area/Main/Corpus">000C22</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Corpus" wicri:corpus="ISTEX">000C22</idno><idno type="wicri:Area/Main/Curation">000C22</idno><idno type="wicri:Area/Main/Exploration">000048</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Exploration">000048</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a" type="main" xml:lang="en">Assimilation of land surface temperature into the land surface model JULES with an ensemble Kalman filter</title><author><name sortKey="Ghent, D" sort="Ghent, D" uniqKey="Ghent D" first="D." last="Ghent">D. Ghent</name><affiliation wicri:level="1"><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>Department of Geography, University of Leicester, Leicester</wicri:regionArea><wicri:noRegion>Leicester</wicri:noRegion></affiliation><affiliation wicri:level="1"><country wicri:rule="url">Royaume-Uni</country></affiliation></author><author><name sortKey="Kaduk, J" sort="Kaduk, J" uniqKey="Kaduk J" first="J." last="Kaduk">J. Kaduk</name><affiliation wicri:level="1"><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>Department of Geography, University of Leicester, Leicester</wicri:regionArea><wicri:noRegion>Leicester</wicri:noRegion></affiliation></author><author><name sortKey="Remedios, J" sort="Remedios, J" uniqKey="Remedios J" first="J." last="Remedios">J. Remedios</name><affiliation wicri:level="1"><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>Department of Physics and Astronomy, University of Leicester, Leicester</wicri:regionArea><wicri:noRegion>Leicester</wicri:noRegion></affiliation></author><author><name sortKey="Ardo, J" sort="Ardo, J" uniqKey="Ardo J" first="J." last="Ardö">J. Ardö</name><affiliation wicri:level="1"><country xml:lang="fr">Suède</country><wicri:regionArea>Department of Earth and Ecosystem Sciences, Lund University, Lund</wicri:regionArea><wicri:noRegion>Lund</wicri:noRegion></affiliation></author><author><name sortKey="Balzter, H" sort="Balzter, H" uniqKey="Balzter H" first="H." last="Balzter">H. Balzter</name><affiliation wicri:level="1"><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>Department of Geography, University of Leicester, Leicester</wicri:regionArea><wicri:noRegion>Leicester</wicri:noRegion></affiliation></author></analytic><monogr></monogr><series><title level="j">Journal of Geophysical Research: Atmospheres</title><title level="j" type="abbrev">J. Geophys. Res.</title><idno type="ISSN">0148-0227</idno><idno type="eISSN">2156-2202</idno><imprint><publisher>Blackwell Publishing Ltd</publisher><date type="published" when="2010-10-16">2010-10-16</date><biblScope unit="volume">115</biblScope><biblScope unit="issue">D19</biblScope><biblScope unit="page" from="n/a">n/a</biblScope><biblScope unit="page" to="n/a">n/a</biblScope></imprint><idno type="ISSN">0148-0227</idno></series><idno type="istex">169D012DD9AB149DC0EC5FE5430B4837136BD4AD</idno><idno type="DOI">10.1029/2010JD014392</idno><idno type="ArticleID">2010JD014392</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0148-0227</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract">Land surface models have uncertainties due to their approximation of physical processes and the heterogeneity of the land surface. These can be compounded when key variables are inadequately represented. Land surface temperature (LST) is critical as it forms an integral component in the surface energy budget, water stress evaluation, fuel moisture derivation, and soil moisture–climate feedbacks. A reduction in the uncertainty of surface energy fluxes, and moisture quantification, is assumed to be achievable by constraining simulations of LST with observation data. This technique is known as data assimilation and involves the adjustment of the model state at observation times with measurements of a predictable uncertainty. In this paper, the validity of LST simulations in a regionalized parameterization of the land surface model Joint UK Land Environment Simulator (JULES) for Africa is assessed by way of a multitemporal intercomparison study with the Moderate Resolution Imaging Spectroradiometer (MODIS), the Advanced Along Track Scanning Radiometer (AATSR), and the Spinning Enhanced Visible and Infrared Imager (SEVIRI) thermal products, with a two‐thirds reduction in model bias found when soil properties are reparameterized. A data assimilation experiment of SEVIRI LST into the JULES model via an ensemble Kalman filter shows an improvement in the modeled LST, soil moisture, and latent and sensible heat fluxes. This paper presents the first investigation into reducing the uncertainty in modeling energy and water fluxes with the United Kingdom's most important land surface model, JULES, by means of data assimilation of LST.</div></front></TEI><affiliations><list><country><li>Royaume-Uni</li><li>Suède</li></country></list><tree><country name="Royaume-Uni"><noRegion><name sortKey="Ghent, D" sort="Ghent, D" uniqKey="Ghent D" first="D." last="Ghent">D. Ghent</name></noRegion><name sortKey="Balzter, H" sort="Balzter, H" uniqKey="Balzter H" first="H." last="Balzter">H. Balzter</name><name sortKey="Ghent, D" sort="Ghent, D" uniqKey="Ghent D" first="D." last="Ghent">D. Ghent</name><name sortKey="Kaduk, J" sort="Kaduk, J" uniqKey="Kaduk J" first="J." last="Kaduk">J. Kaduk</name><name sortKey="Remedios, J" sort="Remedios, J" uniqKey="Remedios J" first="J." last="Remedios">J. Remedios</name></country><country name="Suède"><noRegion><name sortKey="Ardo, J" sort="Ardo, J" uniqKey="Ardo J" first="J." last="Ardö">J. Ardö</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Wicri/France/explor/AussoisV1/Data/Main/Exploration
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000048 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd -nk 000048 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Wicri/France
|area= AussoisV1
|flux= Main
|étape= Exploration
|type= RBID
|clé= ISTEX:169D012DD9AB149DC0EC5FE5430B4837136BD4AD
|texte= Assimilation of land surface temperature into the land surface model JULES with an ensemble Kalman filter
}}
| This area was generated with Dilib version V0.6.29. |  |