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 | Acesso ao texto completo restrito à biblioteca da Embrapa Meio Ambiente. Para informações adicionais entre em contato com cnpma.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Meio Ambiente. |
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Data corrente: |
11/01/2008 |
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Data da última atualização: |
24/03/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
MAIA, A. H. N.; MEINKE, H.; LENNOX, S.; STONE, R. |
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Afiliação: |
Aline de Holanda Nunes Maia, Embrapa Meio Ambiente; Holger Meinke, Queensland Departament of Primary Industries and Fisheries; Sarah Lennox, Queensland Departament of Primary Industries and Fisheries; Roger Stone, Queensland Departament of Primary Industries and Fisheries. |
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Título: |
Inferential, nonparametric statistics to assess the quality of probabilistic forecast systems. |
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Ano de publicação: |
2007 |
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Fonte/Imprenta: |
Monthly Weather Review, Boston, v. 135, n. 2, p. 351-362, 2007. |
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Idioma: |
Inglês |
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Conteúdo: |
Many statistical forecast systems are available to interested users. To be useful for decision making, these systems must be based on evidence of underlying mechanisms. Once causal connections between the mechanism and its statistical manifestation have been firmly established, the forecasts must also provide some quantitative evidence of ?quality.? However, the quality of statistical climate forecast systems (forecast quality) is an ill-defined and frequently misunderstood property. Often, providers and users of such forecast systems are unclear about what quality entails and how to measure it, leading to confusion and misinformation. A generic framework is presented that quantifies aspects of forecast quality using an inferential approach to calculate nominal significance levels (p values), which can be obtained either by directly applying nonparametric statistical tests such as Kruskal?Wallis (KW) or Kolmogorov?Smirnov (KS) or by using Monte Carlo methods (in the case of forecast skill scores). Once converted to p values, these forecast quality measures provide a means to objectively evaluate and compare temporal and spatial patterns of forecast quality across datasets and forecast systems. The analysis demonstrates the importance of providing p values rather than adopting some arbitrarily chosen significance levels such as 0.05 or 0.01, which is still common practice. This is illustrated by applying nonparametric tests (such as KW and KS) and skill scoring methods [linear error in the probability space (LEPS) and ranked probability skill score (RPSS)] to the five-phase Southern Oscillation index classification system using historical rainfall data from Australia, South Africa, and India. The selection of quality measures is solely based on their common use and does not constitute endorsement. It is found that nonparametric statistical tests can be adequate proxies for skill measures such as LEPS or RPSS. The framework can be implemented anywhere, regardless of dataset, forecast system, or quality measure. Eventually such inferential evidence should be complemented by descriptive statistical methods in order to fully assist in operational risk management. MenosMany statistical forecast systems are available to interested users. To be useful for decision making, these systems must be based on evidence of underlying mechanisms. Once causal connections between the mechanism and its statistical manifestation have been firmly established, the forecasts must also provide some quantitative evidence of ?quality.? However, the quality of statistical climate forecast systems (forecast quality) is an ill-defined and frequently misunderstood property. Often, providers and users of such forecast systems are unclear about what quality entails and how to measure it, leading to confusion and misinformation. A generic framework is presented that quantifies aspects of forecast quality using an inferential approach to calculate nominal significance levels (p values), which can be obtained either by directly applying nonparametric statistical tests such as Kruskal?Wallis (KW) or Kolmogorov?Smirnov (KS) or by using Monte Carlo methods (in the case of forecast skill scores). Once converted to p values, these forecast quality measures provide a means to objectively evaluate and compare temporal and spatial patterns of forecast quality across datasets and forecast systems. The analysis demonstrates the importance of providing p values rather than adopting some arbitrarily chosen significance levels such as 0.05 or 0.01, which is still common practice. This is illustrated by applying nonparametric tests (such as KW and KS) and skill scoring methods [linea... Mostrar Tudo |
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Palavras-Chave: |
Inferência; Modelo espacial. |
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Thesagro: |
Análise Estatística; Estatística; Modelo Matemático; Previsão do Tempo; Qualidade. |
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Categoria do assunto: |
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Marc: |
LEADER 02904naa a2200241 a 4500
001 1015905
005 2023-03-24
008 2007 bl uuuu u00u1 u #d
100 1 $aMAIA, A. H. N.
245 $aInferential, nonparametric statistics to assess the quality of probabilistic forecast systems.$h[electronic resource]
260 $c2007
520 $aMany statistical forecast systems are available to interested users. To be useful for decision making, these systems must be based on evidence of underlying mechanisms. Once causal connections between the mechanism and its statistical manifestation have been firmly established, the forecasts must also provide some quantitative evidence of ?quality.? However, the quality of statistical climate forecast systems (forecast quality) is an ill-defined and frequently misunderstood property. Often, providers and users of such forecast systems are unclear about what quality entails and how to measure it, leading to confusion and misinformation. A generic framework is presented that quantifies aspects of forecast quality using an inferential approach to calculate nominal significance levels (p values), which can be obtained either by directly applying nonparametric statistical tests such as Kruskal?Wallis (KW) or Kolmogorov?Smirnov (KS) or by using Monte Carlo methods (in the case of forecast skill scores). Once converted to p values, these forecast quality measures provide a means to objectively evaluate and compare temporal and spatial patterns of forecast quality across datasets and forecast systems. The analysis demonstrates the importance of providing p values rather than adopting some arbitrarily chosen significance levels such as 0.05 or 0.01, which is still common practice. This is illustrated by applying nonparametric tests (such as KW and KS) and skill scoring methods [linear error in the probability space (LEPS) and ranked probability skill score (RPSS)] to the five-phase Southern Oscillation index classification system using historical rainfall data from Australia, South Africa, and India. The selection of quality measures is solely based on their common use and does not constitute endorsement. It is found that nonparametric statistical tests can be adequate proxies for skill measures such as LEPS or RPSS. The framework can be implemented anywhere, regardless of dataset, forecast system, or quality measure. Eventually such inferential evidence should be complemented by descriptive statistical methods in order to fully assist in operational risk management.
650 $aAnálise Estatística
650 $aEstatística
650 $aModelo Matemático
650 $aPrevisão do Tempo
650 $aQualidade
653 $aInferência
653 $aModelo espacial
700 1 $aMEINKE, H.
700 1 $aLENNOX, S.
700 1 $aSTONE, R.
773 $tMonthly Weather Review, Boston$gv. 135, n. 2, p. 351-362, 2007.
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Registro original: |
Embrapa Meio Ambiente (CNPMA) |
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| Acesso ao texto completo restrito à biblioteca da Embrapa Soja. Para informações adicionais entre em contato com valeria.cardoso@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Soja. |
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Data corrente: |
30/06/1997 |
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Data da última atualização: |
05/09/2007 |
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Autoria: |
DOMIT, L. A. |
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Afiliação: |
EMBRAPA-CNPSo. Londrina, PR. |
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Título: |
Inoculacao de sementes de cereais de estacao fria com Bradyrhizobium japonicum e seu efeito na soja cultivada em sucessao. |
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Ano de publicação: |
1989 |
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Fonte/Imprenta: |
Porto Alegre: UFRGS, 1989. |
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Páginas: |
62p. |
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Idioma: |
Português |
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Notas: |
Dissertacao Mestrado. |
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Conteúdo: |
Com o objetivo de avaliar o efeito da inoculacao de sementes de trigo (Triticum aestivum), de aveia branca (Avena sativa) e de aveia preta (Avena strigosa) na populacao de Bradyrhizobium japonicum no solo, na nodulacao, na ocorrencia de nodulos com as estirpes inoculadas, no N total na parte e no rendimento de graos da soja cultivada em sucessao, foi conduzido um experimento na EEA-UFRGS, em eldorado do Sul, Depressao Central do RS, em 1987/88, em solo Podzolico Vermelho-Ecuro distrofico, Paleudult, unidade de mapeamento Sao Jeronimo, onde a soja nao havia sido cultivada nos ultimos dez anos. Apos o cultivo dos cereais de estacao fria, inoculados e nao inoculados, foi semeada a soja, cultivar BR-4, inoculada e nao inoculada. Foi utilizado inoculante em meio turfoso, composto das estirpes SEMIA 587 e SEMIA 5019. a populacao de B.japonicum no solo foi avaliada pelo metodo de diluicao em plantas - MPN. A populacao estabelecida de B.japonicum no solo foi superior a 10.10.10 celulas.g de solo. A inoculacao dos cereais de estacao fria, mesmo tendo aumentado a populacao do B.japonicum no solo, nao resultou em aumento de nodulacao, de ocorrencia de nodulos com as estirpes inoculadas e de N total na parte aerea. |
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Palavras-Chave: |
Aveia branca; Aveia preta; Black oat; Brasil; Cereais; Cereals; Grain; Inoculation; N; Rendimento de grao; Rio Grande do Sul; Root nodulation; Seed; Soybean; Sucessao; Trifolium aestivum; Wheats; White oat; Yield. |
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Thesagro: |
Aveia; Avena Sativa; Avena Strigosa; Bactéria; Bradyrhizobium Japonicum; Inoculação; Nitrogênio; Nodulação; Produção; Semente; Soja; Solo; Trigo; Triticum Aestivum. |
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Thesaurus NAL: |
Brazil; inoculation methods; nitrogen; oats; sequential cropping; soil; soybeans; wheat. |
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Categoria do assunto: |
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Marc: |
LEADER 02721nam a2200625 a 4500
001 1444315
005 2007-09-05
008 1989 bl uuuu m 00u1 u #d
100 1 $aDOMIT, L. A.
245 $aInoculacao de sementes de cereais de estacao fria com Bradyrhizobium japonicum e seu efeito na soja cultivada em sucessao.
260 $aPorto Alegre: UFRGS$c1989
300 $a62p.
500 $aDissertacao Mestrado.
520 $aCom o objetivo de avaliar o efeito da inoculacao de sementes de trigo (Triticum aestivum), de aveia branca (Avena sativa) e de aveia preta (Avena strigosa) na populacao de Bradyrhizobium japonicum no solo, na nodulacao, na ocorrencia de nodulos com as estirpes inoculadas, no N total na parte e no rendimento de graos da soja cultivada em sucessao, foi conduzido um experimento na EEA-UFRGS, em eldorado do Sul, Depressao Central do RS, em 1987/88, em solo Podzolico Vermelho-Ecuro distrofico, Paleudult, unidade de mapeamento Sao Jeronimo, onde a soja nao havia sido cultivada nos ultimos dez anos. Apos o cultivo dos cereais de estacao fria, inoculados e nao inoculados, foi semeada a soja, cultivar BR-4, inoculada e nao inoculada. Foi utilizado inoculante em meio turfoso, composto das estirpes SEMIA 587 e SEMIA 5019. a populacao de B.japonicum no solo foi avaliada pelo metodo de diluicao em plantas - MPN. A populacao estabelecida de B.japonicum no solo foi superior a 10.10.10 celulas.g de solo. A inoculacao dos cereais de estacao fria, mesmo tendo aumentado a populacao do B.japonicum no solo, nao resultou em aumento de nodulacao, de ocorrencia de nodulos com as estirpes inoculadas e de N total na parte aerea.
650 $aBrazil
650 $ainoculation methods
650 $anitrogen
650 $aoats
650 $asequential cropping
650 $asoil
650 $asoybeans
650 $awheat
650 $aAveia
650 $aAvena Sativa
650 $aAvena Strigosa
650 $aBactéria
650 $aBradyrhizobium Japonicum
650 $aInoculação
650 $aNitrogênio
650 $aNodulação
650 $aProdução
650 $aSemente
650 $aSoja
650 $aSolo
650 $aTrigo
650 $aTriticum Aestivum
653 $aAveia branca
653 $aAveia preta
653 $aBlack oat
653 $aBrasil
653 $aCereais
653 $aCereals
653 $aGrain
653 $aInoculation
653 $aN
653 $aRendimento de grao
653 $aRio Grande do Sul
653 $aRoot nodulation
653 $aSeed
653 $aSoybean
653 $aSucessao
653 $aTrifolium aestivum
653 $aWheats
653 $aWhite oat
653 $aYield
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