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1. Identity statement
Reference TypeJournal Article
Sitemtc-m16b.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifierx6e6X3pFwXQZ3DUS8rS5/Cc23K
Repositorycptec.inpe.br/walmeida/2004/05.18.14.35   (restricted access)
Last Update2005:11.18.14.51.00 (UTC) administrator
Metadata Repositorycptec.inpe.br/walmeida/2004/05.18.14.35.45
Metadata Last Update2021:02.10.18.59.52 (UTC) administrator
Secondary KeyINPE-13476-PRE/8689
ISSN1099-2391
Citation KeyNowosadRiosCamp:2000:DaAsUs
TitleData Assimilation Using an Adaptative Kalman Filter and Laplace Transform
ProjectModelagem da atmosfera e interfaces
Year2000
Secondary Date20051118
Access Date2024, Apr. 27
Secondary TypePRE PI
Number of Files1
Size576 KiB
2. Context
Author1 Nowosad, Alexandre Guirland
2 Rios Neto, A.
3 Campos Velho, Haroldo Fraga de
Resume Identifier1 8JMKD3MGP5W/3C9JGGN
2
3 8JMKD3MGP5W/3C9JHC3
Group1 DMD-INPE-MCT-BR
2 CPT-INPE-MCT-BR
3 LAC-INPE-MCT-BR
Affiliation1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
3 Instituto Nacional de Pesquisas Espaciais (INPE)
e-Mail Addressatus@cptec.inpe.br
JournalHybrid Methods in Engineering
Volume2
Number3
Pages289-307
History (UTC)2006-02-08 15:27:51 :: Fabia -> administrator ::
2008-06-10 19:46:16 :: administrator -> estagiario ::
2010-05-11 16:53:13 :: estagiario -> administrator ::
2021-02-10 18:59:52 :: administrator -> marciana :: 2000
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Keywordsdata assimilation
kalman filter
laplace transform
molinear dynamics
AbstractAn Adaptive Extended Kalman Filter is used for data assimilation in two non-linear dynamical systems: the Lorenz system in chaotic state and the computational model DYNAMO for the atmosphere. This approach does not require the modelling error to be stationary and uses a linear Kalman filter to estimate this error. This method is compared to the methods using Laplace Transform, Linear and Extended Kalman Filter. The conclusion was that the choice between using Laplace Transform and Adaptative Kalman Filter assimilation methods for DYNAMO depended on whether one was willing to completely reject high-frequency information or not. When that information was considered useless the Laplace filtering eliminated it better than Kalman. Otherwise Kalman assimilated it better than Laplace.
AreaMET
Arrangement 1urlib.net > BDMCI > Fonds > Produção anterior à 2021 > LABAC > Data Assimilation Using...
Arrangement 2urlib.net > BDMCI > Fonds > Produção anterior à 2021 > CGCPT > Data Assimilation Using...
Arrangement 3urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDMD > Data Assimilation Using...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Contentthere are no files
4. Conditions of access and use
Languageen
Target FileNowosad_DataAssimilation.pdf
User Groupadministrator
estagiario
Visibilityshown
Copy HolderSID/SCD
Archiving Policydenypublisher denyfinaldraft
Read Permissiondeny from all and allow from 150.163
5. Allied materials
Next Higher Units8JMKD3MGPCW/3ESGTTP
8JMKD3MGPCW/3EUPEJL
8JMKD3MGPCW/43SKC35
DisseminationWEBSCI
Host Collectioncptec.inpe.br/walmeida/2003/04.25.17.12
6. Notes
Empty Fieldsalternatejournal archivist callnumber copyright creatorhistory descriptionlevel documentstage doi electronicmailaddress format isbn label lineage mark mirrorrepository month nextedition notes orcid parameterlist parentrepositories previousedition previouslowerunit progress readergroup rightsholder schedulinginformation secondarymark session shorttitle sponsor subject tertiarymark tertiarytype typeofwork url versiontype
7. Description control
e-Mail (login)marciana
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