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1. Identity statement
Reference TypeJournal Article
Sitemtc-m21d.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
IdentifierQABCDSTQQW/46C4HFE
Repositoryurlib.net/www/2022/02.14.11.08
Last Update2022:02.14.11.08.47 (UTC) simone
Metadata Repositoryurlib.net/www/2022/02.14.11.08.47
Metadata Last Update2022:06.23.12.54.45 (UTC) administrator
DOI10.3390/atmos13020243
ISSN2073-4433
Citation KeyMonegoAnocCamp:2022:SoAmSe
TitleSouth America Seasonal Precipitation Prediction by Gradient-Boosting Machine-Learning Approach
Year2022
MonthFeb.
Access Date2022, July 07
Type of Workjournal article
Secondary TypePRE PI
Number of Files1
Size1997 KiB
2. Context
Author1 Monego, Vinicius Schmidt
2 Anochi, Juliana Aparecida
3 Campos Velho, Haroldo Fraga de
Resume Identifier1
2
3 8JMKD3MGP5W/3C9JHC3
ORCID1
2 0000-0003-0769-9750
3 0000-0003-4968-5330
Group1 CAP-COMP-DIPGR-INPE-MCTI-GOV-BR
2 DIPTC-CGCT-INPE-MCTI-GOV-BR
3 COPDT-CGIP-INPE-MCTI-GOV-BR
Affiliation1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
3 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address1 vinicius.monego@inpe.br
2 juliana.anochi@gmail.com
3 haroldo.camposelho@inpe.br
JournalAtmosphere
Volume13
Number2
Pagese243
Secondary MarkB3_ENGENHARIAS_III B3_ENGENHARIAS_I B3_CIÊNCIAS_AMBIENTAIS B4_ENGENHARIAS_II B5_GEOCIÊNCIAS
History (UTC)2022-02-14 11:08:47 :: simone -> administrator ::
2022-02-14 11:08:48 :: administrator -> simone :: 2022
2022-02-14 11:08:57 :: simone -> administrator :: 2022
2022-06-23 12:54:45 :: administrator -> self-uploading-INPE-MCTI-GOV-BR :: 2022
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
KeywordsGradient boosting
Machine learning
Precipitation
Seasonal climate prediction
AbstractMachine learning has experienced great success in many applications. Precipitation is a hard meteorological variable to predict, but it has a strong impact on society. Here, a machine-learning techniquea formulation of gradient-boosted treesis applied to climate seasonal precipitation prediction over South America. The Optuna framework, based on Bayesian optimization, was employed to determine the optimal hyperparameters for the gradient-boosting scheme. A comparison between seasonal precipitation forecasting among the numerical atmospheric models used by the National Institute for Space Research (INPE, Brazil) as an operational procedure for weather/climate forecasting, gradient boosting, and deep-learning techniques is made regarding observation, with some showing better performance for the boosting scheme.
AreaCOMP
Arrangement 1urlib.net > BDMCI > Fonds > Produção pgr ATUAIS > CAP > South America Seasonal...
Arrangement 2urlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGCT > South America Seasonal...
Arrangement 3urlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGIP > South America Seasonal...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
agreement.html 14/02/2022 08:08 1.0 KiB 
4. Conditions of access and use
data URLhttp://urlib.net/ibi/QABCDSTQQW/46C4HFE
zipped data URLhttp://urlib.net/zip/QABCDSTQQW/46C4HFE
Languageen
Target Fileatmosphere-13-00243-v2.pdf
User Groupsimone
Reader Groupadministrator
simone
Visibilityshown
Archiving Policyallowpublisher allowfinaldraft
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/mtc-m19/2013/05.22.12.17
Next Higher Units8JMKD3MGPCW/3F2PHGS
8JMKD3MGPCW/46KUATE
8JMKD3MGPCW/46KUES5
DisseminationWEBSCI; PORTALCAPES; SCOPUS.
Host Collectionurlib.net/www/2021/06.04.03.40
6. Notes
Empty Fieldsalternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn label lineage mark nextedition notes parameterlist parentrepositories previousedition previouslowerunit progress project readpermission rightsholder secondarydate secondarykey session shorttitle sponsor subject tertiarymark tertiarytype url
7. Description control
e-Mail (login)self-uploading-INPE-MCTI-GOV-BR
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