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Registro Completo |
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Biblioteca(s): |
Embrapa Meio Ambiente. |
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Data corrente: |
08/10/2015 |
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Data da última atualização: |
09/08/2021 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
MAIA, A. de H. N.; MEINKE, H. |
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Afiliação: |
ALINE DE HOLANDA NUNES MAIA, CNPMA; H. MEINKE, Department of Primary Industries and Fisheries, Austrália. |
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Título: |
Assessing uncertainty of seasonal probabilistic forecasts: distribution-free confidence limits. |
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Ano de publicação: |
2006 |
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Fonte/Imprenta: |
In: INTERNATIONAL CONFERENCE ON SOUTHERN HEMISPHERE METEOROLOGY AND OCEANOGRAPHY, 8., 2006, Foz do Iguaçu,PR. [Anais?]. Foz do Iguaçu,PR: ICSHMO, 2006. p. 569-573. |
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Idioma: |
Português |
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Conteúdo: |
Probabilistic climate information, including climate forecasts, often rely on time series data of prognostic variables (Y, eg. rainfall or yield), represented as cumulative distribution probabilities functions (CDFs) or their complement, probability of exceeding functions (POEs). Useful information for decision-making is then derived from such distributions and expressed as Y percentiles or the probability of Y exceeding a certain threshold c (Pr[Y> c]). Such estimates are frequently reported without any measure of uncertainty. The degree of uncertainty associated with such estimates depends on the length of the time series and their internal variability. Lack of uncertainty assessments can lead to misguided beliefs about the true performance of the forecast systems, possibly resulting in inappropriate actions by the decision maker (Potts et al. 1996; Jolliffe 2004; Maia et al. 2006). However, even when uncertainty estimates are provided, these are often based on methods that rely on assumptions of data being normally distributed. This is in spite of the well-known fact that distributions of important climate variables, such as rainfall, are notoriously skewed, particularly in areas with strong seasonality (eg. high frequencies of ?zero? rainfall amounts). As an alternative for Normal-based procedures, we therefore propose the use of distribution free methods for constructing percentile and POE confidence limits as described in Hahn and Meeker (1991) and implemented into ?The Capability Procedure?? of the SAS® System. Such distribution-free tools are particularly useful for spatial uncertainty assessments that would otherwise require a tedious, location-by-location checking of assumptions regarding underlying probability distributions (Maia et al., 2006). Here, we discuss the rationale, advantages and limitations of both, parametric and nonparametric approaches. We illustrate the use of distribution-free methods by assessing the uncertainty of percentiles and POEs estimates for 3-monthly rainfall series from selected locations in Australia and the Southeast of South America. MenosProbabilistic climate information, including climate forecasts, often rely on time series data of prognostic variables (Y, eg. rainfall or yield), represented as cumulative distribution probabilities functions (CDFs) or their complement, probability of exceeding functions (POEs). Useful information for decision-making is then derived from such distributions and expressed as Y percentiles or the probability of Y exceeding a certain threshold c (Pr[Y> c]). Such estimates are frequently reported without any measure of uncertainty. The degree of uncertainty associated with such estimates depends on the length of the time series and their internal variability. Lack of uncertainty assessments can lead to misguided beliefs about the true performance of the forecast systems, possibly resulting in inappropriate actions by the decision maker (Potts et al. 1996; Jolliffe 2004; Maia et al. 2006). However, even when uncertainty estimates are provided, these are often based on methods that rely on assumptions of data being normally distributed. This is in spite of the well-known fact that distributions of important climate variables, such as rainfall, are notoriously skewed, particularly in areas with strong seasonality (eg. high frequencies of ?zero? rainfall amounts). As an alternative for Normal-based procedures, we therefore propose the use of distribution free methods for constructing percentile and POE confidence limits as described in Hahn and Meeker (1991) and implemented into ?Th... Mostrar Tudo |
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Thesagro: |
Estatística; Previsão do tempo. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/130866/1/2006AA-011.pdf
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Marc: |
LEADER 02701nam a2200145 a 4500
001 2026087
005 2021-08-09
008 2006 bl uuuu u00u1 u #d
100 1 $aMAIA, A. de H. N.
245 $aAssessing uncertainty of seasonal probabilistic forecasts$bdistribution-free confidence limits.$h[electronic resource]
260 $aIn: INTERNATIONAL CONFERENCE ON SOUTHERN HEMISPHERE METEOROLOGY AND OCEANOGRAPHY, 8., 2006, Foz do Iguaçu,PR. [Anais?]. Foz do Iguaçu,PR: ICSHMO, 2006. p. 569-573.$c2006
520 $aProbabilistic climate information, including climate forecasts, often rely on time series data of prognostic variables (Y, eg. rainfall or yield), represented as cumulative distribution probabilities functions (CDFs) or their complement, probability of exceeding functions (POEs). Useful information for decision-making is then derived from such distributions and expressed as Y percentiles or the probability of Y exceeding a certain threshold c (Pr[Y> c]). Such estimates are frequently reported without any measure of uncertainty. The degree of uncertainty associated with such estimates depends on the length of the time series and their internal variability. Lack of uncertainty assessments can lead to misguided beliefs about the true performance of the forecast systems, possibly resulting in inappropriate actions by the decision maker (Potts et al. 1996; Jolliffe 2004; Maia et al. 2006). However, even when uncertainty estimates are provided, these are often based on methods that rely on assumptions of data being normally distributed. This is in spite of the well-known fact that distributions of important climate variables, such as rainfall, are notoriously skewed, particularly in areas with strong seasonality (eg. high frequencies of ?zero? rainfall amounts). As an alternative for Normal-based procedures, we therefore propose the use of distribution free methods for constructing percentile and POE confidence limits as described in Hahn and Meeker (1991) and implemented into ?The Capability Procedure?? of the SAS® System. Such distribution-free tools are particularly useful for spatial uncertainty assessments that would otherwise require a tedious, location-by-location checking of assumptions regarding underlying probability distributions (Maia et al., 2006). Here, we discuss the rationale, advantages and limitations of both, parametric and nonparametric approaches. We illustrate the use of distribution-free methods by assessing the uncertainty of percentiles and POEs estimates for 3-monthly rainfall series from selected locations in Australia and the Southeast of South America.
650 $aEstatística
650 $aPrevisão do tempo
700 1 $aMEINKE, H.
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Registro original: |
Embrapa Meio Ambiente (CNPMA) |
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Registro Completo
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Biblioteca(s): |
Embrapa Acre. |
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Data corrente: |
09/03/2006 |
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Data da última atualização: |
07/02/2024 |
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Tipo da produção científica: |
Resumo em Anais de Congresso |
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Autoria: |
THOMAZINI, M. J.; LINO NETO, J.; COSTA, V. A.; BERTI FILHO, E. |
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Afiliação: |
MARCILIO JOSE THOMAZINI, CPAF-AC; J. LINO NETO, Universidade Federal de Viçosa (UFV); V. A. COSTA, Seção de Controle Biológico de Pragas (IB), Campinas, SP; E. BERTI FILHO, ESALQ/USP. |
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Título: |
Caracterização das fases imaturas e desenvolvimento pós-embrionário do parasitóide Muscidifurax uniraptor (Hymenoptera, Pteromalidae) em pupas de Musca domestica (Diptera, Muscidae). |
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Ano de publicação: |
1998 |
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Fonte/Imprenta: |
In: CONGRESSO BRASILEIRO DE ENTOMOLOGIA, 17.; ENCONTRO NACIONAL DE FITOSSANITARISTAS, 8., 1998, Rio de Janeiro, RJ. Resumos... Rio de Janeiro: Universidade Federal Rural do Rio de Janeiro, 1998. |
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Volume: |
v. 1, |
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Páginas: |
p. 996. |
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Idioma: |
Português |
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Conteúdo: |
Este trabalho objetivou identificar os diferentes estágios de desenvolvimento das pupas de Muscidifurax uniraptor. |
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Palavras-Chave: |
Desarrollo animal; House fly; Muscidifurax uniraptor; Pteromalid wasps. |
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Thesagro: |
Etapa de Desenvolvimento Animal; Mosca Domestica; Parasitismo; Pupa; Vespa. |
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Thesaurus NAL: |
Animal development; Musca domestica; Parasitism; Pupae. |
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Categoria do assunto: |
O Insetos e Entomologia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/503886/1/12718.pdf
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Marc: |
LEADER 01266nam a2200325 a 4500
001 1503886
005 2024-02-07
008 1998 bl uuuu u00u1 u #d
100 1 $aTHOMAZINI, M. J.
245 $aCaracterização das fases imaturas e desenvolvimento pós-embrionário do parasitóide Muscidifurax uniraptor (Hymenoptera, Pteromalidae) em pupas de Musca domestica (Diptera, Muscidae).$h[electronic resource]
260 $aIn: CONGRESSO BRASILEIRO DE ENTOMOLOGIA, 17.; ENCONTRO NACIONAL DE FITOSSANITARISTAS, 8., 1998, Rio de Janeiro, RJ. Resumos... Rio de Janeiro: Universidade Federal Rural do Rio de Janeiro$c1998
300 $ap. 996. v. 1,
490 $vv. 1,
520 $aEste trabalho objetivou identificar os diferentes estágios de desenvolvimento das pupas de Muscidifurax uniraptor.
650 $aAnimal development
650 $aMusca domestica
650 $aParasitism
650 $aPupae
650 $aEtapa de Desenvolvimento Animal
650 $aMosca Domestica
650 $aParasitismo
650 $aPupa
650 $aVespa
653 $aDesarrollo animal
653 $aHouse fly
653 $aMuscidifurax uniraptor
653 $aPteromalid wasps
700 1 $aLINO NETO, J.
700 1 $aCOSTA, V. A.
700 1 $aBERTI FILHO, E.
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Registro original: |
Embrapa Acre (CPAF-AC) |
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