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1. Identity statement
Reference TypeJournal Article
Siteplutao.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
IdentifierJ8LNKAN8RW/36GAU8R
Repositorydpi.inpe.br/plutao@80/2009/12.01.11.48.14   (restricted access)
Last Update2010:05.24.13.45.32 (UTC) administrator
Metadata Repositorydpi.inpe.br/plutao@80/2009/12.01.11.48.15
Metadata Last Update2018:06.05.00.12.44 (UTC) administrator
Secondary KeyINPE--PRE/
DOI10.1007/s00704-009-0193-y
ISSN0177-798X
Labellattes: 4411895644401494 1 MendesMare:2009:CoBeAr
Citation KeyMendesMare:2010:CoArNe
TitleTemporal downscaling: a comparison between artificial neural network and autocorrelation techniques over the Amazon Basin in present and future climate change scenarios
Year2010
Monthmay
Access Date2024, May 01
Secondary TypePRE PI
Number of Files1
Size367 KiB
2. Context
Author1 Mendes, David
2 Marengo, José A.
Group1 CST-CST-INPE-MCT-BR
2 CST-CST-INPE-MCT-BR
Affiliation1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address1 dmendes@cptec.inpe.br
e-Mail Addressdmendes@cptec.inpe.br
JournalTheoretical and Applied Climatology
Volume100
Number3-4
Pages413-421
Secondary MarkA2_CIÊNCIAS_AGRÁRIAS A2_GEOCIÊNCIAS A2_GEOGRAFIA A2_INTERDISCIPLINAR
History (UTC)2009-12-02 11:58:31 :: lattes -> marciana ::
2009-12-02 12:38:51 :: marciana -> administrator ::
2010-05-12 02:05:07 :: administrator -> marciana ::
2010-07-16 15:33:06 :: marciana -> administrator :: 2009 -> 2010
2010-11-17 21:34:22 :: administrator -> marciana :: 2010
2011-05-21 01:00:18 :: marciana -> administrator :: 2010
2018-06-05 00:12:44 :: administrator -> marciana :: 2010
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Keywordsseasonal cycle
South America
trends
preciptation
variability
AbstractSeveral studies have been devoted to dynamic and statistical downscaling for both climate variability and climate change. This paper introduces an application of temporal neural networks for downscaling global climate model output and autocorrelation functions. This method is proposed for downscaling daily precipitation time series for a region in the Amazon Basin. The downscaling models were developed and validated using IPCC AR4 model output and observed daily precipitation. In this paper, five AOGCMs for the twentieth century (20C3M; 1970-1999) and three SRES scenarios (A2, A1B, and B1) were used. The performance in downscaling of the temporal neural network was compared to that of an autocorrelation statistical downscaling model with emphasis on its ability to reproduce the observed climate variability and tendency for the period 1970-1999. The model test results indicate that the neural network model significantly outperforms the statistical models for the downscaling of daily precipitation variability.
AreaMET
Arrangementurlib.net > BDMCI > Fonds > Produção anterior à 2021 > COCST > Temporal downscaling: a...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Contentthere are no files
4. Conditions of access and use
Languageen
Target Filetemporal.pdf
User Groupadministrator
lattes
marciana
Visibilityshown
Archiving Policydenypublisher denyfinaldraft12
Read Permissiondeny from all and allow from 150.163
5. Allied materials
Next Higher Units8JMKD3MGPCW/3F3T29H
DisseminationWEBSCI; PORTALCAPES; MGA.
Host Collectiondpi.inpe.br/plutao@80/2008/08.19.15.01
6. Notes
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7. Description control
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