WRF model assessment for wind intensity and power density simulation in the southern coast of Brazil

Tuchtenhagen, Patrícia; Carvalho, Gilvani Gomes de; Martins, Guilherme; Silva, Pollyanne Evangelista da; Oliveira, Cristiano Prestrelo de; Andrade, Lára de Melo Barbosa; Araújo, João M. de; Mutti, Pedro Rodrigues; Lúcio, Paulo Sérgio; Silva, Cláudio Moisés Santos e

doi:10.1016/j.energy.2019.116341

Cited by 9 publications

(4 citation statements)

References 66 publications

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“…The estimated PD in this study was 499.30 W·m −2 for the NEB region, similar to that found by Torres et al (2016) of 558 W·m −2 , using data at a height of 50 m from the National Environmental Data Organization System (SONDA). For the SUB region, the mean DP was 624.29 W·m −2 , a value close to the mean value (651.2 W·m −2 ) reported by Tuchtenhagen et al (2020), who used wind data from the Blended Sea Winds (BSW) database 100 m offshore. In the SEB region, averages of 109.79 W·m −2 were estimated, lower than those found by Pimenta et al (2008) (300 and 450 W·m −2 ), who used satellite data (QuikSCAT) for the offshore region.…”

Section: Resultssupporting

confidence: 84%

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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: Resultssupporting

confidence: 84%

“…In the Northeast region of Brazil (NEB), the inter‐annual variability of wind speed is influenced by the El Niño‐Southern Oscillation (ENSO), whereas in the southern region, the Antarctic Oscillation (AO) impacts the wind intensity over the southernmost part of Brazil (Bianchi et al, 2017; Tuchtenhagen et al, 2020).…”

Section: Introductionmentioning

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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“…Numerical Weather and Prediction models have been used for wind energy assessment, and Weather Research and Forecasting (WRF) [7], since version 3.8, has included a wind farm parameterization to represent the wind turbine's momentum sink in the main wind flow as well as the transference of kinetic energy into electricity and turbulent kinetic energy [8]. WRF has been used for the wind energy assessment in Brazil [9], Chile [10], Tanzania [11], Portugal [12] and Alaska [13]. In regard to wind resource data, the Global Wind Atlas (GWA) was developed, which combines mesoscale and microscale modelling (downscaling process) to provide wind climate data at high horizontal resolution (250 m).…”

Section: Introductionmentioning

confidence: 99%

Wind Power Potential in Highlands of the Bolivian Andes: A Numerical Approach

Mamani

Hendrick

2022

Energies

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Wind resource assessment is a key factor for the development and implementation of wind farms with the purpose of generating green, eco-friendly and clean electricity. The Bolivian Andes, as a large dry region, represents an important source of renewable energy. However, the altitude and high wind energy resources of the Bolivian Andes require further knowledge and understanding of the wind energy resources. In this study, the GWA have been used to determine the total area available to install wind farms considering the protected areas, roads, cities and transmission lines. In addition, the Weather Research and Forecasting (WRF v3.8.1) model is employed to complement the results of the GWA based on the validation of WRF simulations with measurements from Qollpana wind farm. The main purpose is to estimate the wind power potential along the Bolivian Andes and its variability in time. The wind power simulations have been compared with the power generated by the Qollpana wind farm to verify the WRF’s performance. The wind power potential in the highlands of the Bolivian Andes could reach between 225 (WRF) and 277 (GWA) GW, distributed mainly over the Western and Eastern Cordillera of the Altiplano.

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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.…”

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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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WRF model assessment for wind intensity and power density simulation in the southern coast of Brazil

Cited by 9 publications

References 66 publications

Evolution of wind speed observed in Brazil between 1961 and 2020

Evolution of wind speed observed in Brazil between 1961 and 2020

Wind Power Potential in Highlands of the Bolivian Andes: A Numerical Approach

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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