Wind mapping using the mesoscale WRF model in a tropical region of Brazil

Souza, Noéle Bissoli Perini de; Nascimento, Erick Giovani Sperandio; Santos, Alex Álisson Bandeira; Moreira, Davidson Martins

doi:10.1016/j.energy.2021.122491

Cited by 14 publications

(4 citation statements)

References 30 publications

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“…The highest frequencies occur for speeds below 2 mÁs −1 for both datasets. There was a reduction in the standard deviation in the imputed data, consistent with previous analyses (Gilliland & Keim, 2018a;Pes et al, 2017).…”

Section: Linear Trend Analysissupporting

confidence: 88%

“…The cities located in the coastal region experienced the highest population growth in NEB (IBGE, 2021). Another possible explanation is the increase in El Niño events during the period of P2, which is generally associated with a decrease in wind speed (Perini De Souza et al, 2022). The increase in sea surface temperature anomalies may favour negative wind speed trends, both in the NEB coastal region and in the SEB.…”

Section: Scenariosmentioning

confidence: 99%

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Evolution of wind speed observed in Brazil between 1961 and 2020

Lima,

Rodrigues,

Santos E Silva

et al. 2024

Intl Journal of Climatology

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The study's main objective was to analyse daily wind speed data in Brazil collected on‐site from 1961 to 2020 and assess changes in two 30‐year climatological normals, denoted as P1 and P2. Data were sourced from 54 INMET‐managed meteorological stations, with missing data filled using the bootstrap expectation maximization algorithm. Imputation performance was evaluated, and results suggested that the 10% imputed level was most reliable, showing low RMSE values (below 0.5), SD values near 1, and r values exceeding 0.93. This method proved suitable for reconstructing historical wind speed data in Brazil. The analysis included the application of the Mann–Kendall test to detect linear trends and Sen's slope test to quantify trend intensity, both at a 5% significance level. Positive trends during P2 were observed in the Amazon basin and Brazil's semiarid region, while negative trends increased along the coast and in the Southeast region. Average wind speeds during P1 were higher than in P2. Comparing the two climatological normals revealed significant positive trends in some regions during P2, with an increase in average monthly variation of up to 0.85 m·s−1. The average slopes of significant trends were −1 and 1 m·s−1·decade−1 for all periods. The results indicated high wind magnitudes, with maximum power density of 778.08 W·m−2 for the NEB region, 170.90 W·m−2 for the SEB region and 401.67 W·m−2 for the SUB, making these regions promising for future wind power projects. These findings have the potential to enhance the optimization of renewable wind energy generation in Brazil.

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Section: Linear Trend Analysissupporting

confidence: 88%

Section: Scenariosmentioning

confidence: 99%

Evolution of wind speed observed in Brazil between 1961 and 2020

Lima,

Rodrigues,

Santos E Silva

et al. 2024

Intl Journal of Climatology

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“…O uso da energia eólica tem crescido exponencialmente nas últimas décadas, tornando-se uma tecnologia bem estabelecida na área de energias renováveis. A região nordeste do Brasil gera a maior parte da energia eólica do país, respondendo por 84% da produção eólica nacional (Souza, 2022). De acordo com o Atlas do Potencial Eólico Brasileiro o território brasileiro disponibiliza velocidades médias anuais superiores a 7m/s para as alturas de 50m, 70m e 100m, com destaque ao nordeste brasileiro pela predominância dos ventos em baixos níveis.…”

Section: Introductionunclassified

Avaliação de métodos para atualização do mapeamento eólico do Estado de Alagoas

Silva

Silva²,

Lyra³

et al. 2023

RSD

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O presente trabalho apresenta o estudo do mapeamento eólico no estado de Alagoas, nordeste do Brasil, utilizando o modelo mesoescala Weather Research Forecast (WRF), versão (4.1.2), e aproximações de estabilidade atmosférica e declividade do terreno. As simulações foram executadas diariamente por um período de 8 anos (2007 - 2015), com resolução horizontal de 5 km e realizado um refinamento da resolução para 250 metros. Foram validadas estatisticamente com dados de 5 estações anemométricas de superfície situadas nas seguintes cidades: Água Branca (sertão), Girau do Ponciano (agreste), Palmeira dos Índios (agreste), Maragogi (litoral) e Roteiro (litoral). A ordem de grandeza e os resultados dos padrões do vento semelhantes para as resoluções de 5km e 250m, indica boa representação pelo modelo atmosférico WRF, como também, valida o método downscaling proposto. Sendo os índices de correlação moderada, BIAS entre -1,89 a 1,76 m/s e REQM entre 1,94 a 3,82 m/s para velocidade do vento e direção predominante do vento variando entre Leste/Sudeste. A comparação espacial dos resultados com simulações do modelo WAsP reforça que a inclusão da estabilidade atmosférica e declividade possibilitou representar com maior clareza o efeito da topografia e rugosidade superficial, o que é crucial para redução da cascata de incertezas no sistema de energia eólica. O refinamento da saída do modelo WRF ressaltaram as áreas promissoras para aproveitamento eólico nas regiões serranas das cidades de Água Branca e Palmeira dos Índios, com intensidade anual acima de 8m/s para 100m de altura.

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“…Studies around the world have used the WRF model in literature to assess wind energy. Some examples of notable works can include: [33] in Fiji, [34] in Chile, [35] in Greece, [36] in Lesotho, [37] in Hawaii, [38] for the southern coast of Brazil, and [39] in a tropical region of Brazil.…”

mentioning

confidence: 99%

An evaluation of the reliability of the Weather Research Forecasting (WRF) model in predicting wind data: a case study of Burundi

Placide,

Lollchund

2024

BMC Environ Sci

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Background The Weather Research and Forecasting (WRF) Model is an exceptional software for mesoscale climate modeling. It is extensively used to simulate key meteorological variables, including temperature, rainfall, and wind. Methods This study thoroughly examined the effectiveness of the WRF model in generating precise wind data for assessing the potential of wind power in Burundi. A meticulous evaluation of various combinations of model physics parameterization schemes was conducted to ensure accuracy. By comparing the simulated data with measurements from four meteorological stations and utilizing statistical metrics such as root-mean-square error (RMSE) and bias, the accuracy of the WRF model was determined. Results The findings of the study uncovered that utilizing WRF Single-Moment 3-Class (WSM3) for microphysics, Grell-Devenyi ensemble for cumulus physics, and Yonsei University for planetary boundary layer yields highly accurate wind data results for Burundi. Furthermore, the WRF model was utilized to create detailed seasonal and annual mean wind maps with a high resolution. Conclusion These maps demonstrated that the western part of Burundi experiences higher wind speeds (ranging from 4 to 9.7 m/s) during the dry seasons revealing the potential for wind energy harvesting in the different areas of Burundi.

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Wind mapping using the mesoscale WRF model in a tropical region of Brazil

Cited by 14 publications

References 30 publications

Evolution of wind speed observed in Brazil between 1961 and 2020

Evolution of wind speed observed in Brazil between 1961 and 2020

Avaliação de métodos para atualização do mapeamento eólico do Estado de Alagoas

An evaluation of the reliability of the Weather Research Forecasting (WRF) model in predicting wind data: a case study of Burundi

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