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1. Identity statement
Reference TypeJournal Article
Sitemtc-m21d.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34T/45DD652
Repositorysid.inpe.br/mtc-m21d/2021/09.09.11.51   (restricted access)
Last Update2021:09.09.11.51.28 (UTC) simone
Metadata Repositorysid.inpe.br/mtc-m21d/2021/09.09.11.51.28
Metadata Last Update2022:04.03.22.27.34 (UTC) administrator
DOI10.3390/app11178001
ISSN2076-3417
Citation KeyTcheouLoviFreiChou:2021:ReFoEr
TitleReducing forecast errors of a regional climate model using adaptive filters
Year2021
MonthSept.
Access Date2024, May 19
Type of Workjournal article
Secondary TypePRE PI
Number of Files1
Size733 KiB
2. Context
Author1 Tcheou, Michel Pompeu
2 Lovisolo, Lisandro
3 Freitas, Alexandre Ribeiro
4 Chou, Sin Chan
ORCID1 0000-0003-2068-2865
2 0000-0002-7404-9371
3 0000-0002-9817-2606
4 0000-0002-8973-1808
Group1
2
3
4 DIMNT-CGCT-INPE-MCTI-GOV-BR
Affiliation1 Universidade do Estado do Rio de Janeiro (UERJ)
2 Universidade do Estado do Rio de Janeiro (UERJ)
3 Universidade do Estado do Rio de Janeiro (UERJ)
4 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address1
2 lisandro@uerj.br
3
4 chou.chan@inpe.br
JournalApplied Sciences (Switzerland)
Volume11
Number17
Pagese8001
Secondary MarkB2_CIÊNCIAS_BIOLÓGICAS_I B3_ENGENHARIAS_I B4_ENGENHARIAS_II B5_QUÍMICA B5_CIÊNCIAS_AGRÁRIAS_I C_CIÊNCIAS_BIOLÓGICAS_III C_CIÊNCIA_DE_ALIMENTOS
History (UTC)2021-09-09 11:53:20 :: simone -> administrator :: 2021
2022-04-03 22:27:34 :: administrator -> simone :: 2021
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
KeywordsAdaptive filtering
Forecast error
Regional climate model
Signal processing
AbstractIn this work, the use of adaptive filters for reducing forecast errors produced by a Regional Climate Model (RCM) is investigated. Seasonal forecasts are compared against the reanalysis data provided by the National Centers for Environmental Prediction. The reanalysis is used to train adaptive filters based on the Recursive Least Squares algorithm in order to reduce the forecast error. The K-means unsupervised learning algorithm is used to obtain the number of filters to employ from the climate variables. The proposed approach is applied to some climate variables such as the meridional wind, zonal wind, and the geopotential height. The forecast is produced by the Eta RCM at 40-km resolution in a domain covering most of Brazil. Results show that the proposed approach is capable of reducing the forecast errors, according to evaluation metrics such as normalized mean square error, maximum absolute error, and maximum normalized absolute error, thus improving the seasonal climate forecasts.
AreaMET
Arrangementurlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGCT > Reducing forecast errors...
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4. Conditions of access and use
Languageen
Target Filetcheou_reducing.pdf
User Groupsimone
Reader Groupadministrator
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Visibilityshown
Read Permissiondeny from all and allow from 150.163
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/46KUATE
Citing Item Listsid.inpe.br/bibdigital/2022/04.03.22.23 5
DisseminationSCOPUS
Host Collectionurlib.net/www/2021/06.04.03.40
6. Notes
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