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Metadados

1. Identificação
Tipo de ReferênciaArtigo em Evento (Conference Proceedings)
Sitemtc-m16b.sid.inpe.br
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Repositóriocptec.inpe.br/adm_conf/2005/10.31.10.20
Última Atualização2006:04.19.20.46.58 (UTC) administrator
Repositório de Metadadoscptec.inpe.br/adm_conf/2005/10.31.10.20.15
Última Atualização dos Metadados2018:06.05.03.42.52 (UTC) administrator
Chave de CitaçãoMaiaMein:2006:DiCoLi
TítuloAssessing uncertainty of seasonal probabilistic forecasts: distribution-free confidence limits
FormatoCD-ROM, On-line.
Ano2006
Data de Acesso20 maio 2024
Tipo SecundárioPRE CI
Número de Arquivos1
Tamanho271 KiB
2. Contextualização
Autor1 Maia, Aline de Holanda Nunes
2 Meinke, Holger
Afiliação1 Embrapa Meio Ambiente, PO Box 69, Jaguariúna, SP, Brazil (Maia)
2 Department of Primary Industries and Fisheries, PO Box 102, Toowoomba, Qld 4350, Australia (Meinke)
3
Endereço de e-Mail do Autor1 ahmaia@cnpma.embrapa.br
2 holger.meinke@dpi.qld,gov.au
3
EditorVera, Carolina
Nobre, Carlos
Endereço de e-Mailahmaia@cnpma.embrapa.br
Nome do EventoInternational Conference on Southern Hemisphere Meteorology and Oceanography, 8 (ICSHMO).
Localização do EventoFoz do Iguaçu
Data24-28 Apr. 2006
Editora (Publisher)American Meteorological Society (AMS)
Cidade da Editora45 Beacon Hill Road, Boston, MA, USA
Páginas569-573
Título do LivroProceedings
Tipo TerciárioPoster
OrganizaçãoAmerican Meteorological Society (AMS)
Histórico (UTC)2005-10-31 10:20:15 :: ahmaia@cnpma.embrapa.br -> administrator ::
2005-11-11 02:10:48 :: administrator -> adm_conf ::
2005-12-16 01:06:03 :: adm_conf -> ahmaia@cnpma.embrapa.br ::
2006-04-03 23:31:52 :: ahmaia@cnpma.embrapa.br -> administrator ::
2006-04-18 21:04:46 :: administrator -> lise@dpi.inpe.br ::
2010-12-28 12:36:32 :: lise@dpi.inpe.br -> administrator ::
2010-12-29 15:56:56 :: administrator -> lise@dpi.inpe.br :: 2006
2010-12-29 16:05:55 :: lise@dpi.inpe.br -> administrator :: 2006
2010-12-29 18:52:44 :: administrator -> banon :: 2006
2011-01-02 17:14:54 :: banon -> administrator :: 2006
2018-06-05 03:42:52 :: administrator -> :: 2006
3. Conteúdo e estrutura
É a matriz ou uma cópia?é a matriz
Estágio do Conteúdoconcluido
Transferível1
Palavras-Chaveprobabilistic forecasts
uncertainty
confidence intervals
ResumoProbabilistic climate forecasts often rely on information coming from historical climate series of prognostic variables, represented by cumulative distribution probabilities functions (CDFs) or their complement, probability of exceeding functions (POEs). They are a simple and convenient way to represent probabilistic information arising from a time series that exhibit no or only weak auto-correlation patterns. However, if the time series shows moderate to strong auto-correlation patterns, a CDF/POE summary will result in some loss of information. Yearly sequences of rainfall data from a specific month or period generally exhibit only weak auto-correlation, thus allowing the CDF/POE representation to convey seasonal climate forecast information. Useful information required by decision makers is then derived from such distribution and expressed as the probability of exceeding a certain threshold (e.g. probability of exceeding historical median value of rainfall or any other derived quantity such as agricultural yield or income). Such estimates are frequently reported without any measure of uncertainty. The degree of uncertainty depends on the length of the time series and its internal variability. Lack of uncertainty assessments can lead to misguided beliefs about the true performance of the forecast systems (e.g. due to the possible existence of artificial skill, especially if forecasts are based on short time series) possibly resulting in inappropriate actions by the decision maker. Parametric methods to assess uncertainty of percentiles and probability of exceeding estimates are frequently based on normality assumptions. However, distributions of some important climate variables, such as rainfall, are notoriously skewed, particularly in areas with strong seasonality that can result in high frequencies of zero rainfall amounts. For such cases there are often no mathematical transformations available that would overcome this lack of normality. As an alternative for Normal-based procedures, we propose the use of distribution free methods for constructing percentile and POE confidence limits. Those distribution-free tools are particularly useful for spatial uncertainty assessments that would require a tedious, location-by-location checking of assumptions regarding underlying probability distributions. Normal-based and distribution-free methods are both available in The Capability Procedure of the Statistical Analysis System (SAS, version 7 and latter releases). In this work, we discuss the rationale, advantages and limitations of both, parametric and non-parametric approaches. We illustrate these methods by assessing the uncertainty of percentiles and POEs estimates for 3-monthly rainfall series from locations in Australia and South America. The SAS codes for computing the uncertainty measures will also be presented.
ÁreaMET
TipoClimate predictions
Conteúdo da Pasta docacessar
Conteúdo da Pasta source
MAIA & MEINKE CONFIDENCE LIMITS.doc 03/04/2006 20:31 136.0 KiB 
Conteúdo da Pasta agreementnão têm arquivos
4. Condições de acesso e uso
URL dos dadoshttp://urlib.net/ibi/cptec.inpe.br/adm_conf/2005/10.31.10.20
URL dos dados zipadoshttp://urlib.net/zip/cptec.inpe.br/adm_conf/2005/10.31.10.20
Idiomaen
Arquivo Alvo569-573.pdf
Grupo de Usuáriosahmaia@cnpma.embrapa.br
administrator
Visibilidadeshown
5. Fontes relacionadas
Acervo Hospedeirocptec.inpe.br/nobre/2005/06.02.21.14
cptec.inpe.br/walmeida/2003/04.25.17.12
6. Notas
Nota1
Campos Vaziosarchivingpolicy archivist callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage doi edition group identifier isbn issn label lineage mirrorrepository nextedition nexthigherunit notes numberofvolumes orcid parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark serieseditor session shorttitle sponsor subject tertiarymark url versiontype volume


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