The Use of the Weibull Three-Parameter Model for Estimating Mean Wind Power Densities

Auwera, L. Van Der; Meyer, F. De; Malet, L. M.

doi:10.1175/1520-0450(1980)019<0819:tuotwt>2.0.co;2

Cited by 88 publications

(28 citation statements)

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“…Os valores obtidos para os teste do χ 2 e KS foram, respectivamente, 0,1 e 3,7, inferiores aos valores críticos a 95% (0,89 e 7,8). Este resultado confirma que esta distribuição é a mais apropriada não só para descrever os extremos de vento, como para extrapolá-los para longos períodos de retorno (Van der Auwera et al 1980;Garcia et al 1998, Bautista, 2002e Sansigolo, 2005. Os valores esperados de temperaturas máximas e mínimas absolutas, precipitações máximas diárias e velocidades máximas do vento para períodos e retorno de 10 a 500 anos, assim como seus intervalos de confiança a 95% de probabilidade, encontram-se na tabela 1.…”

Section: Resultsunclassified

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Distribuições de extremos de precipitação diária, temperatura máxima e mínima e velocidade do vento em Piracicaba, SP (1917-2006)

Sansigolo

2008

Rev. bras. meteorol.

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RESUMODiversas distribuições teóricas de probabilidade incluindo a Normal, Gumbel, Fréchet, Weibull, LogNormal e Pearson 3, foram ajustadas aos extremos anuais de precipitação máxima diária, temperatura máxima e mínima absolutas e velocidade instantânea do vento em Piracicaba, SP (1917SP ( -2006. Métodos de máxima verossimilhança foram utilizados para estimar os parâmetros das distribuições, assim como seus intervalos de confiança. Testes χ 2 e KS foram usados para comparar os ajuste e selecionar as melhores distribuições teóricas. Os ajustes também foram avaliados em gráficos PP e QQ. A distribuição de Gumbel foi a que melhor se ajustou aos extremos de precipitação máxima diária e de temperaturas máximas absolutas. As temperaturas mínimas absolutas apresentaram uma distribuição simétrica e foram melhor ajustadas pela distribuição normal. A distribuição de Weibull foi a que melhor se ajustou às velocidades instantâneas do vento. São apresentados resultados sobre os valores esperados, assim como seus intervalos de confiança, para períodos de retorno de até 500 anos. Palavras-chave: Ajustes de distribuições de extremos; variáveis meteorológicas; Piracicaba, Brasil ABSTRACT:EXTREME DISTRIBUTIONS OF DAILY RAINFALL, MINIMUM AND MAXIMUM TEMPERATURES AND WIND SPEED IN PIRACICABA, BRAZIL (1917BRAZIL ( -2006. Several theoretical probability distributions, including Normal, Gumbel, Fréchet, Weibull, Lognormal, and Pearson 3 were fitted to annual extremes of daily maximum rainfall, maximum and minimum absolute temperature, and instantaneous wind speed in Piracicaba, São Paulo, Brasil (1917Brasil ( -2006. Maximum likelihood methods were used to estimate the distributions parameters as well as their confidence intervals. KS and χ 2 tests were used to compare the fits and select the best theoretical distribution. The Gumbel distribution was that best fitted the extremes of daily maximum rainfall and maximum absolute temperature. The minimum absolute temperatures presented a symmetrical distribution and were best fitted by the normal distribution. The Weibull distribution best fitted the maxima wind speeds. Results showing the expected values, as well as their confidence intervals, for return periods up to 500 years, are presented.

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Section: Resultsunclassified

“…No caso das velocidades máximas do vento, uma das distribuições mais utilizadas é a de Weibull ( Van der Auwera et al, 1980;Garcia et al, 1999e Sansigolo, 2005.…”

Section: Introductionunclassified

Distribuições de extremos de precipitação diária, temperatura máxima e mínima e velocidade do vento em Piracicaba, SP (1917-2006)

Sansigolo

2008

Rev. bras. meteorol.

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“…In some cases, the addition of a third parameter that effectively shifts the distribution along the W-axis has been shown to provide improved fits to data (Stewart and Essenwanger 1978;van der Auwera et al 1980;Tuller and Brett 1985), but we neglect this refinement here. The mean and standard deviation of the Weibull distribution are given by…”

Section: Characterizing the Observed Surface Wind Speed Distributionmentioning

confidence: 99%

Statistical downscaling of historical monthly mean winds over a coastal region of complex terrain. I. Predicting wind speed

2011

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Surface wind speed is a key climatic variable of interest in many applications, including assessments of storm-related infrastructure damage and feasibility studies of wind power generation. In this work and a companion paper (van der Kamp et al. 2011), the relationship between local surface wind and large-scale climate variables was studied using multiple regression analysis. The analysis was performed using monthly mean station data from British Columbia, Canada and large-scale climate variables (predictors) from the NCEP-2 reanalysis over the period 1979-2006. Two regression-based methodologies were compared. The first relates the annual cycle of station wind speed to that of the large-scale predictors at the closest grid box to the station. It is shown that the relatively high correlation coefficients obtained with this method are attributable to the dominant influence of region-wide seasonality, and thus contain minimal information about local wind behaviour at the stations. The second method uses interannually varying data for individual months, aggregated into seasons, and is demonstrated to contain intrinsically local information about the surface winds. The dependence of local wind speed upon large-scale predictors over a much larger region surrounding the station was also explored, resulting in 2D maps of spatial correlations. The cross-validated explained variance using the interannual method was highest in autumn and winter, ranging from 30 to 70% at about a dozen stations in the region. Reasons for the limited predictive skill of the regressions and directions for future progress are reviewed.

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“…Some researches related to wind behavior are concentrated on the problem of adjustment of statistics distribution to data of wind speed ( [1,2] and others). Results of these researches also indicate the distribution of Weibull as the one that fits better to these data.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Wind Power in the State of Paraíba Using the Mesoscale Atmospheric Model Brazilian Developments on the Regional Atmospheric Modelling System

Lima

Cavalcanti

Souza

et al. 2012

ISRN Renewable Energy

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This work aims to describe the wind power density in five sites in the State of Paraiba, as well as to access the ability of the mesoscale atmospheric model Brazilian developments on the regional atmospheric modeling system (BRAMS) in describing the intensity of wind in São Gonçalo Monteiro, Patos, Campina Grande, and João Pessoa. Observational data are wind speed and direction at 10 m high, provided by the National Institute of Meteorology (INMET). We used the numerical model BRAMS in simulations for two different months. We ran the model for rainy months: March and April. It was concluded that the BRAMS model is able to satisfactorily reproduce the monthly cycle of the wind regime considered, as well as the main direction. However the model tends to underestimate the wind speed.

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The Use of the Weibull Three-Parameter Model for Estimating Mean Wind Power Densities

Cited by 88 publications

References 0 publications

Distribuições de extremos de precipitação diária, temperatura máxima e mínima e velocidade do vento em Piracicaba, SP (1917-2006)

Distribuições de extremos de precipitação diária, temperatura máxima e mínima e velocidade do vento em Piracicaba, SP (1917-2006)

Statistical downscaling of historical monthly mean winds over a coastal region of complex terrain. I. Predicting wind speed

Evaluation of the Wind Power in the State of Paraíba Using the Mesoscale Atmospheric Model Brazilian Developments on the Regional Atmospheric Modelling System

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