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1. Identity statement
Reference TypeJournal Article
Sitemtc-m16d.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP7W/3C8ARG8
Repositorysid.inpe.br/mtc-m19/2012/07.05.17.07   (restricted access)
Last Update2012:07.05.17.08.58 (UTC) administrator
Metadata Repositorysid.inpe.br/mtc-m19/2012/07.05.17.07.25
Metadata Last Update2021:01.02.22.17.25 (UTC) administrator
Secondary KeyINPE--PRE/
DOI10.1002/asl.385
ISSN1530-261X
Citation KeyDolifNobr:2012:ArSyPa
TitleImproving extreme precipitation forecasts in Rio de Janeiro, Brazil: are synoptic patterns efficient for distinguishing ordinary from heavy rainfall episodes?
Year2012
Monthmay
Access Date2024, May 18
Secondary TypePRE PI
Number of Files1
Size203 KiB
2. Context
Author1 Dolif, Giovanni
2 Nobre, Carlos Afonso
Resume Identifier1
2 8JMKD3MGP5W/3C9JGQ7
Group1 DOP-CPT-INPE-MCTI-GOV-BR
2 CST-CST-INPE-MCTI-GOV-BR
Affiliation1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address1 giovanni.dolif@cptec.inpe.br
JournalAtmospheric Science Letters
Volume*
Secondary MarkB3_ENGENHARIAS_I B3_GEOCIÊNCIAS
History (UTC)2012-07-05 17:07:25 :: valdirene -> administrator ::
2012-07-05 17:07:25 :: administrator -> valdirene :: 2012
2012-07-05 17:08:58 :: valdirene -> administrator :: 2012
2021-01-02 22:17:25 :: administrator -> valdirene :: 2012
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
Keywordsheavy rainfall forecast
Rio de Janeiro
artificial neural network
adaptive resonance theory
AbstractThis work analysed heavy rainfall events and their predictability on Rio de Janeiro, Brazil, using rain gauge data from 2000 to 2010, atmospheric model outputs, and an artificial neural network based on adaptive resonance theory. The latter was applied on top of atmospheric simulations for 2009 and 2010, and we were able to predict 55% of the heavy rainfall events using a combination of relative humidity at 900 hPa and meridional winds at 10 m for a domain covering central and southern Brazil, which represents a relative gain of 67% on predictability when compared to the model predicted rainfall. Copyright © 2012 Royal Meteorological Society.
AreaMET
Arrangement 1urlib.net > BDMCI > Fonds > Produção anterior à 2021 > COCST > Improving extreme precipitation...
Arrangement 2urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDOP > Improving extreme precipitation...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
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4. Conditions of access and use
Languageen
User Groupadministrator
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Reader Groupadministrator
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Visibilityshown
Archiving Policydenypublisher denyfinaldraft
Read Permissiondeny from all and allow from 150.163
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/mtc-m19@80/2009/08.21.17.02.53
Next Higher Units8JMKD3MGPCW/3F3T29H
8JMKD3MGPCW/43SQKNE
Citing Item Listsid.inpe.br/bibdigital/2013/10.19.20.40 4
sid.inpe.br/mtc-m21/2012/07.13.14.42.59 1
DisseminationWEBSCI; PORTALCAPES.
Host Collectionsid.inpe.br/mtc-m19@80/2009/08.21.17.02
6. Notes
Empty Fieldsalternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn label lineage mark nextedition notes number orcid pages parameterlist parentrepositories previousedition previouslowerunit progress project rightsholder schedulinginformation secondarydate session shorttitle sponsor subject targetfile tertiarymark tertiarytype typeofwork url
7. Description control
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