Estimativa da Velocidade do Vento Offshore na Região Litorânea do Sul do Estado da Bahia

Autores

DOI:

https://doi.org/10.26848/rbgf.v18.2.p1523-1547

Palavras-chave:

energia eólica, modelagem atmosférica, perfil de vento, wrf, WRF

Resumo

A energia eólica offshore ganhou destaque globalmente devido à maior intensidade dos ventos marítimos em comparação com os terrestres, o que aumenta a eficiência dos parques eólicos. Este estudo avalia o perfil de vento no Litoral Sul do Estado da Bahia, estendendo-se dos munícipios de Ilhéus a Mucuri, para estimar as velocidades médias de vento offshore, utilizando o modelo WRF (Weather Research and Forecasting). O método envolveu análises estatísticas com os dados de reanálise do ERA5 (European Centre for Medium-Range Weather Forecasts Reanalysis v5) e observações de Estações Meteorológicas Automáticas (EMAs) como correlação de Pearson, ANOVA com medidas repetidas e Diagramas de Taylor para validar os dados do WRF. A validação do modelo WRF indicou uma boa correspondência com os dados observacionais, embora pequenas discrepâncias tenham sido observadas. Os resultados mostraram variações sazonais significativas, com ventos mais intensos no inverno, especialmente entre as latitudes 17,5°S a 18,5°S. As velocidades médias do vento a 100 m variaram entre 3,5 e 4,5 m/s, e a 150 m, entre 4,5 e 5,5 m/s, com picos de até 6,5 m/s. Conclui-se que a região apresenta um significativo potencial para a geração de energia eólica offshore a alturas superiores a 100 m. As simulações com o modelo WRF, validadas com dados observacionais, fornecem uma base sólida para o planejamento de projetos eólicos na região.

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Biografia do Autor

Angelo Teixeira Lemos, Universidade Federal do Sul da Bahia

Professor efetivo do magistério superior em regime de dedicação exclusiva da Universidade Federal do Sul da Bahia, Centro de Formação em Ciências Ambientais, Campus Sosígenes Costa, Porto Seguro/Bahia.

Douglas Bitencourt Vidal, Universidade Federal da Bahia

Engenheiro Ambiental e Sanitarista pelo Centro Universitário de Caratinga (2013), Normalista pela Universidade Presidente Antônio Carlos (2007), Mestre em Energia pelo Programa de Pós-Graduação em Energia na Linha de Pesquisa Eficiência Energética da Universidade Federal do Espírito Santo (2017) e Doutorando no Programa de Engenharia Industrial pela Universidade Federal da Bahia com previsão de defesa para agosto de 2023. Membro do Banco de Avaliadores do Sistema Nacional de Avaliação da Educação Superior - BASis, através da PORTARIAS DE 17 DE SETEMBRO DE 2018.

Ednildo Andrade Torres, Universidade Federal da Bahia

Ednildo Andrade Torres é coordenador do Laboratório de Energia e Gás (LEN), da Escola Politécnica da UFBA, com pós-doutorado na FAMU/FSU US, doutor em Energia pela UNICAMP, Mestre pela Universidade de São Paulo/Escola Politécnica, graduação na Universidade Federal da Bahia. Foi chefe por dois períodos do Departamento de Engenharia Química/UFBA, possui mais de 35 anos de experiência na área de desenvolvimento tecnológico entre Centros de Pesquisa Industriais e Universidades. É membro titular da Academia de Ciências da Bahia, foi vice-coordenador do INCT Energia e Ambiente, com sede na UFBA.

Referências

Alayat, M., Kassem, Y., & Çamur, H. (2018). Assessment of wind energy potential as a power generation source: A case study of eight selected locations in Northern Cyprus. Energies, 11(10), 2697. https://doi.org/10.3390/en11102697

Anacona, H. V., et al. (2023). Wind simulations over Western Patagonia using the Weather Research and Forecasting model and reanalysis. Atmosphere, 14(7), 1062. https://doi.org/10.3390/atmos14071062

Andrade, A. R. de et al. (2021). Wind speed trends and the potential of electricity generation at new wind power plants in Northeast Brazil. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 43(4), 182. http://dx.doi.org/10.1007/s40430-021-02911-y

Araújo, R. D., & Gorayeb, A. (2023). Perception of the socio-environmental impacts caused by wind generators in the state of Piauí, Northeast of Brazil. Sustainability in Debate, 14(3), 52–87. http://dx.doi.org/10.18472/SustDeb.v14n3.2023.50457

Bahamonde, M. I., & Litrán, S. P. (2019). Study of the energy production of a wind turbine in the open sea considering the continuous variations of the atmospheric stability and the sea surface roughness. Renewable Energy, 135, 163–175. https://doi.org/10.1016/j.renene.2018.11.075

Bañuelos-Ruedas, F., Angeles-Camacho, C., & Rios-Marcuello, S. (2010). Analysis and validation of the methodology used in the extrapolation of wind speed data at different heights. Renewable and Sustainable Energy Reviews, 14(8), 2383–2391. https://doi.org/10.1016/j.rser.2010.05.001

Barber, S., et al. (2022). The wide range of factors contributing to wind resource assessment accuracy in complex terrain. Wind Energy Science, 7(4), 1503–1525. https://doi.org/10.5194/wes-7-1503-2022

Barroso, L. L., et al. (2022). Aspectos gerais sobre a viabilidade de instalação de energia eólica no Brasil. Research, Society and Development, 11(9), e308911931781. http://dx.doi.org/10.33448/rsd-v11i9.31781

Barthelmie, R. J., Wang, H., Doubrawa, P., & Pryor, S. C. (2016). Best practice for measuring wind speeds and turbulence offshore through in-situ and remote sensing technologies. Departamento de Energia dos Estados Unidos. https://doi.org/10.7298/X4QV3JGF

Benesty, J., Chen, J., Huang, Y., & Cohen, I. (2009). Pearson correlation coefficient. In Noise reduction in speech processing (pp. 37-40). Springer. https://doi.org/10.1007/978-3-642-00296-0_5

Brower, M. (2012). Wind resource assessment: A practical guide to developing a wind project. John Wiley & Sons. https://doi.org/10.1002/9781118249864

Carvalho, D., Rocha, A., Gómez-Gesteira, M., & Santos, C. S. (2014). Comparison of reanalyzed, analyzed, satellite-retrieved and NWP modelled winds with buoy data along the Iberian Peninsula coast. Remote Sensing of Environment, 152, 480–492. https://doi.org/10.1016/j.rse.2014.07.017

Carvalho, D., Rocha, A., Gómez-Gesteira, M., & Santos, C. S. (2014). Sensitivity of the WRF model wind simulation and wind energy production estimates to planetary boundary layer parameterizations for onshore and offshore areas in the Iberian Peninsula. Applied Energy, 135, 234–246. https://doi.org/10.1016/j.apenergy.2014.08.086

Centro de Pesquisas de Energia Elétrica - CEPEL. (2017). Atlas do potencial eólico brasileiro: Simulações 2013 (1ª ed.). CEPEL. Disponível em http://mtc-m21b.sid.inpe.br/col/sid.inpe.br/mtc-m21b/2017/12.21.11.22/doc/Novo-Atlas-do-Potencial-Eolico-Brasileiro-SIM_2013.pdf

Chancham, C., Waewsak, J., & Gagnon, Y. (2017). Offshore wind resource assessment and wind power plant optimization in the Gulf of Thailand. Energy, 139, 706–731. DOI: 10.1016/j.energy.2017.08.026

Coles, S. (2001). An introduction to statistical modeling of extreme values (1st ed.). Springer London. https://doi.org/10.1007/978-1-4471-3675-0

Coriolano, T. R., et al. (2022). Study of the temporal variation of offshore wind energy potential in southeast Brazil. Ciência e Natura, 44. http://dx.doi.org/10.5902/2179460X68814

Crippa, P., et al. (2021). A temporal model for vertical extrapolation of wind speed and wind energy assessment. Applied Energy, 301, 117378. https://doi.org/10.1016/j.apenergy.2021.117378

Daley, R. (1991). Atmospheric data analysis. Cambridge University Press. https://doi.org/10.1002/joc.3370120708

de Jong, P., et al. (2017). Forecasting high proportions of wind energy supplying the Brazilian Northeast electricity grid. Applied Energy, 195, 538–555. https://doi.org/10.1016/j.apenergy.2017.03.058

Lucena, A. F. P., Szklo, A. S., Schaeffer, R., & Dutra, R. M. (2010). The vulnerability of wind power to climate change in Brazil. Renewable Energy, 35(5), 904–912. https://doi.org/10.1016/j.renene.2009.10.022

Delgado, F., & Filgueiras, R. (2022). O&G e energia renovável offshore: perspectivas e desafios. Revista Conjuntura Econômica, 76, 42–44.

Dörenkämper, M., et al. (2020). The making of the New European Wind Atlas – Part 2: Production and evaluation. Geoscientific Model Development, 13(10), 5079–5102. https://doi.org/10.5194/gmd-13-5079-2020

Franz, G., et al. (2021). Coastal ocean observing and modeling systems in Brazil: Initiatives and future perspectives. Frontiers in Marine Science, 8. https://doi.org/10.3389/fmars.2021.681619

Freire, A. Í., & Fontgalland, I. L. (2022). Perspectivas e desafios econômicos da geração de energia eólica na região Nordeste do Brasil. Research, Society and Development, 11(1), e58911125429. https://doi.org/10.33448/rsd-v11i1.25429

Freire, L. S. (2022). Large-eddy simulation of the atmospheric boundary layer with near-wall resolved turbulence. Boundary-Layer Meteorology, 184(1), 25–43. https://doi.org/10.1007/s10546-022-00702-z

Góes, M. de F. B., et al. (2021). Wind power projects in Brazil: Challenges and opportunities increasing co-benefits and implications for climate and energy policies. Environment, Development and Sustainability, 23(10), 15341–15367. https://doi.org/10.1007/s10668-021-01300-8

Golbazi, M., & Archer, C. L. (2020). Surface roughness for offshore wind energy. Journal of Physics: Conference Series, 1452(1), 012024. DOI: 10.1088/1742-6596/1452/1/012024

Gonçalves, L. de J. M., et al. (2024). Evaluation of a high-resolution WRF model for southeast Brazilian coast: The importance of physical parameterization to wind representation. Atmosphere, 15(5), 533. https://doi.org/10.3390/atmos15050533

González-Mingueza, C., & Muñoz-Gutiérrez, F. (2014). RETRACTED: Wind prediction using Weather Research Forecasting model (WRF): A case study in Peru. Energy Conversion and Management, 81, 363–373. https://doi.org/10.1016/j.enconman.2014.02.024

Gorayeb, A., Araújo, R. M., & Silva, C. F. (2022). Análise multicritério de parques eólicos onshore e offshore no Ceará: Em foco as comunidades tradicionais litorâneas. Revista Mutirõ: Folhetim de Geografias Agrárias do Sul, 3(2), 32. https://doi.org/10.51359/2675-3472.2022.253079

Gorayeb, A., & Brannstrom, C. (2016). Toward participatory management of renewable energy resources (wind-farm) in Northeastern Brazil. Mercator, 15(1), 105–115. DOI 10.4215/RM0000.0000.0000

Gorayeb, A., & Brannstrom, C. (2016). Toward participatory management of renewable energy resources (wind-farm) in northeastern Brazil. Mercator, 15(1), 105–115. DOI: 10.4215/rm2016.1501.0008

Hersbach, H., et al. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), 1999–2049. https://doi.org/10.1002/qj.3803

Jacondino, W. D., et al. (2021). Hourly day-ahead wind power forecasting at two wind farms in northeast Brazil using WRF model. Energy, 230, 120841. https://doi.org/10.1016/j.energy.2021.120841

Juárez, A. A., et al. (2014). Development of the wind power in Brazil: Political, social and technical issues. Renewable and Sustainable Energy Reviews, 39, 828–834. https://doi.org/10.1016/j.rser.2014.07.086

Kaiser-Weiss, A. K., Kaspar, F., Heene, V., & Fiedler, S. (2015). Comparison of regional and global reanalysis near-surface winds with station observations over Germany. Advances in Science and Research, 12(1), 187–198. https://doi.org/10.5194/asr-12-187-2015

Kent, C. W., Grimmond, C. S. B., Gatey, D., & Barlow, J. F. (2018). Assessing methods to extrapolate the vertical wind-speed profile from surface observations in a city centre during strong winds. Journal of Wind Engineering and Industrial Aerodynamics, 173, 100–111. https://doi.org/10.1016/j.jweia.2017.12.013

Khan, K. S., & Tariq, M. (2018). Wind resource assessment using SODAR and meteorological mast: A case study of Pakistan. Renewable and Sustainable Energy Reviews, 81, 2443–2449. https://doi.org/10.1016/j.rser.2017.06.040

Kosovic, B., Haupt, S. E., Adriaansen, D., Alessandrini, S., & Sullivan, P. (2020). A comprehensive wind power forecasting system integrating artificial intelligence and numerical weather prediction. Energies, 13(6), 1372. https://doi.org/10.3390/en13061372

Kovalski, M. L. (2023). Variabilidade de alta frequência da velocidade do vento próximo à superfície no Nordeste do Brasil: Clima presente e tendências futuras (Dissertação de mestrado, Universidade de São Paulo, Instituto de Astronomia, Geofísica e Ciências Atmosféricas).

Kruse, C., Del Vento, D., Montuoro, R., Lubin, M., & McMillan, S. (2013, August). Evaluation of WRF scaling to several thousand cores on the Yellowstone supercomputer. Em Proceedings of the Front Range Consortium for Research Computing Conference (Vol. 14). Boulder, CO, EUA.

Lange, B., Larsen, S., Højstrup, J., & Barthelmie, R. J. (2004). Importance of thermal effects and sea surface roughness for offshore wind resource assessment. Journal of Wind Engineering and Industrial Aerodynamics, 92(11), 959–988. https://doi.org/10.1016/j.jweia.2004.06.002

Langenberg, B., Janczyk, M., Koob, V., & Kliegl, R. (2022). A tutorial on using the paired t test for power calculations in repeated measures ANOVA with interactions. Behavior Research Methods, 55(5), 2467–2484. https://doi.org/10.3758/s13428-022-01902-8

Letcher, T. (Ed.). (2023). Wind energy engineering: A handbook for onshore and offshore wind turbines. Elsevier. https://doi.org/10.1016/C2021-0-00258-3

Li, H., Claremar, B., Wu, L., Hallgren, C., Körnich, H., Rutgersson, A., & Ivanell, S. (2021). A sensitivity study of the WRF model in offshore wind modeling over the Baltic Sea. Geoscience Frontiers, 12(6), 101229. https://doi.org/10.1016/j.gsf.2021.101229

Li, J., Wang, G., Li, Z., Yang, S., & Chong, W. T. (2020). A review on development of offshore wind energy conversion system. International Journal of Energy Research, 44(12), 9283–9297. https://doi.org/10.1002/er.5751

Lima, M. A., Mendes, L. F. R., Mothé, G. A., Linhares, F. G., & Soares, S. R. (2020). Renewable energy in reducing greenhouse gas emissions: Reaching the goals of the Paris agreement in Brazil. Environmental Development, 33, 100504. https://doi.org/10.1016/j.envdev.2020.100504

Li, Z., Wan, B., Duan, Z., He, Y., Yu, Y., & Chen, H. (2023). Evaluation of HY-2C and CFOSAT satellite retrieval offshore wind energy using Weather Research and Forecasting (WRF) simulations. Remote Sensing, 15(17), 4172. https://doi.org/10.3390/rs15174172

Liu, X., Xing, X., Kang, C. W., & Zhang, X. (2024). Towards Offshore Wind Farm Design through Comprehensive Meteo-Oceanographic Analysis in Extreme Weather Conditions. Ocean and Polar Engineering, 2024.

Loriato, A. G., Salvador, N., Loriato, A. A. B., & Andrade, M. F. (2018). Inventário de emissões com alta resolução para a região da Grande Vitória utilizando o sistema de modelagem integrada WRF-SMOKE-CMAQ. Revista Brasileira de Meteorologia, 33(3), 521–536. https://doi.org/10.1590/0102-77863330046

Lucena, J. de A. Y., & Lucena, K. Â. A. (2019). Wind energy in Brazil: An overview and perspectives under the triple bottom line. Clean Energy, 3(2), 69–84. https://doi.org/10.1093/ce/zkz001

Lumbreras, S., & Ramos, A. (2013). Offshore wind farm electrical design: A review. Wind Energy, 16(3), 459–473. https://doi.org/10.1002/we.1498

Machrafi, H. (Ed.). (2012). Green energy and technology. Bentham Science Publishers. https://doi.org/10.2174/97816080528511120101

Marinha do Brasil. (n.d.). Limites Marítimos e LEPLAC. Diretoria de Hidrografia e Navegação. Disponível em https://www.marinha.mil.br/dhn/?q=pt-br/node/168

Maslor, N. A., & Hasan, H. B. (2024, January 24). Analysis of daily maximum temperature and wind speed in Malaysia using repeated measures ANOVA. AIP Conference Proceedings. AIP Publishing. https://doi.org/10.1063/5.0192718

Mathos, K. P., Donnou, H. E. V., & Kouchadé, C. A. (2020). Vertical extrapolation of wind speeds under a neutral atmosphere and evaluation of the wind energy potential on different sites in Guinea. American Journal of Energy Engineering, 8(1), 9. https://doi.org/10.11648/j.ajee.20200801.12

Mattar, C., & Borvarán, D. (2016). Offshore wind power simulation by using WRF in the central coast of Chile. Renewable Energy, 94, 22–31. https://doi.org/10.1016/j.renene.2016.03.041

Miglietta, M. M., Zecchetto, S., & De Biasio, F. (2013). A comparison of WRF model simulations with SAR wind data in two case studies of orographic lee waves over the Eastern Mediterranean Sea. Atmospheric Research, 120–121, 127–146. https://doi.org/10.1016/j.atmosres.2012.09.017

Moreno, R., Arias, E., Cazorla, D., & Pardo, J. J. (2020). Analysis of a new MPI process distribution for the Weather Research and Forecasting (WRF) model. Scientific Programming, 2020, 8148373. https://doi.org/10.1155/2020/8148373

Mughal, M. O., Lynch, M., Yu, F., & McGann, B. (2017). Wind modelling, validation and sensitivity study using Weather Research and Forecasting model in complex terrain. Environmental Modelling & Software, 90, 107–125. https://doi.org/10.1016/j.envsoft.2017.01.020

Muhammad, L. N. (2023). Guidelines for repeated measures statistical analysis approaches with basic science research considerations. Journal of Clinical Investigation, 133(11). https://doi.org/10.1172/JCI171058

Nascimento, M. M. de S., Shadman, M., Silva, C., & Nascimento, J. (2022). Offshore wind and solar complementarity in Brazil: A theoretical and technical potential assessment. Energy Conversion and Management, 270, 116194. https://doi.org/10.1016/j.enconman.2022.116194

Nezhad, M. M., Neshat, M., & Groppi, D. (2021). A primary offshore wind farm site assessment using reanalysis data: A case study for Samothraki Island. Renewable Energy, 172, 667–679. https://doi.org/10.1016/j.renene.2021.03.089

Olaofe, Z. O. (2019). Quantification of the near-surface wind conditions of the African coast: A comparative approach (satellite, NCEP CFSR, and WRF-based). Energy, 189, 116232. https://doi.org/10.1016/j.energy.2019.116232

Paulino, S. R., et al. (2023). Conflitos socioambientais e a implantação de parques eólicos no Nordeste brasileiro. Sustainability in Debate, 14(3), 21–51. https://doi.org/10.20873/j.sustain.14.3.2023

Pelser, T., et al. (2024). Reviewing accuracy & reproducibility of large-scale wind resource assessments. Advances in Applied Energy, 13, 100158. https://doi.org/10.1016/j.adapen.2024.100158

Perini, N. B. P., et al. (2022). Wind mapping using the mesoscale WRF model in a tropical region of Brazil. Energy, 240, 122491. https://doi.org/10.1016/j.energy.2021.122491

Petersen, E. L. (2017). In search of the wind energy potential. Journal of Renewable and Sustainable Energy, 9(5). https://doi.org/10.1063/1.4995245

Piasecki, A., Jurasz, J., & Kies, A. (2019). Measurements and reanalysis data on wind speed and solar irradiation from energy generation perspectives at several locations in Poland. SN Applied Sciences, 1(8), 865. https://doi.org/10.1007/s42452-019-0909-x

Pimenta, F. M., et al. (2019). Brazil offshore wind resources and atmospheric surface layer stability. Energies, 12(21), 4195. https://doi.org/10.3390/en12214195

Placide, G., & Lollchund, M. R. (2024). An evaluation of the reliability of the Weather Research Forecasting (WRF) model in predicting wind data: A case study of Burundi. BMC Environmental Science, 1(1), 2. https://doi.org/10.1186/s44329-024-00001-7

Rabelo, D. R., de Araújo, L. F., Santos, M. A., & Souza, J. M. (2023). Generation of wind energy in the state of Bahia, Brazil: Challenges and possibilities. Revista Brasileira de Geografia Física, 16(3), 1145–1155. https://doi.org/10.26848/rbgf.v16.3.p1145-1155

Saadatabadi, A. R., Mohammadi, H., & Asadi, F. (2024). Optimization and evaluation of the Weather Research and Forecasting (WRF) model for wind energy resource assessment and mapping in Iran. Applied Sciences, 14(8), 3304. https://doi.org/10.3390/app14083304

Sampaio, K. R. A., & Batista, V. (2021). O atual cenário da produção de energia eólica no Brasil: Uma revisão de literatura. Research, Society and Development, 10(1), e57710112107. https://doi.org/10.33448/rsd-v10i1.12107

Santana, L. V. R., Stosic, T., & Ferreira, T. A. E. (2020). Comparison of wind speed data in the Northeast of Brazil from ERA-40 and the National Institute of Meteorology (INMET) using entropy measurements. Research, Society and Development, 9(8), e446985257. https://doi.org/10.33448/rsd-v9i8.5257

Santana, L. V. R., & Silva, A. S. A. da. (2020). Análise de agrupamento da velocidade do vento no Nordeste do Brasil. Em Ciências Exatas e da Terra: Exploração e Qualificação de Diferentes Tecnologias 2 (pp. 61–69). Atena Editora. DOI: 10.22533/at.ed.8562027105

Santos, J. V. C. (2020). Estudo das características espaciais e temporais da velocidade do vento utilizando a técnica DFA e o modelo WRF em regiões do Estado da Bahia e no Oceano Atlântico Sul (Tese de doutorado, Centro Universitário SENAI CIMATEC, Programa de Pós-Graduação em Modelagem Computacional e Tecnologia Industrial). Salvador: SENAI CIMATEC.

Sheridan, L. M., Phillips, C., Orrell, A. C., & Lantz, K. (2022). Validation of wind resource and energy production simulations for small wind turbines in the United States. Wind Energy Science, 7(2), 659–676. https://doi.org/10.5194/wes-7-659-2022

Shi, P., Leung, L. R., & Wang, B. (2024). Development and evaluation of a new 4DEnVar-based weakly coupled ocean data assimilation system in E3SMv2. Geoscientific Model Development. https://doi.org/10.5194/gmd-2024-183

Silva, G. K. da, Santos, A. C. dos, Silva, M. V. M. da, & Lima, J. S. (2017). Estudo dos padrões de ventos offshore no litoral do Ceará utilizando dados estimados pelo produto de satélites BSW. Revista Brasileira de Meteorologia, 32(4), 679–690. https://doi.org/10.1590/0102-77863240013

Silva, S. S. F. da, Alves, A. C., & Ramalho, Â. M. C. (2020). Energia eólica e complementaridade energética: Estratégia e desafio para o desenvolvimento sustentável na região Nordeste do Brasil. Qualitas Revista Eletrônica, 19(3), 53. http://dx.doi.org/10.18391/req.v19i3.5640

Silva, S. S. F. da, & Cândido, G. A. (2015). Energy matrix clean and renewable: A challenge for the National energy planning and an opportunity for the Northeast Region of Brazil. Revista Espacios, 36(15). DOI: 10.48082/espacios-a24v45n05

Silveira, W. W. da, & Carvalho, V. S. B. (2021). Avaliação das condições meteorológicas simuladas pelo modelo WRF na região metropolitana do Rio de Janeiro em dias com altas concentrações de poluentes. Revista Brasileira de Meteorologia, 36(2), 317–325. https://doi.org/10.1590/0102-77863623005

Simas, M., & Pacca, S. (2014). Assessing employment in renewable energy technologies: A case study for wind power in Brazil. Renewable and Sustainable Energy Reviews, 31, 83–90. https://doi.org/10.1016/j.rser.2013.11.046

Stoevesandt, B., Schepers, J. G., Martínez-Tossas, L. A., & Le Pape, A. (Eds.). (2022). Handbook of wind energy aerodynamics. Springer Nature. https://doi.org/10.1007/978-3-030-31307-4

Stull, R. (2016). Practical meteorology: An algebra-based survey of atmospheric science (Edição ilustrada). AVP International, University of British Columbia, BC Open Textbook Collection.

Teng, J., & Markfort, C. D. (2020). A calibration procedure for an analytical wake model using wind farm operational data. Energies, 13(14), 3537. http://dx.doi.org/10.3390/en13143537

Tian, X., Conibear, L., & Steward, J. (2023). A neural-network based MPAS—Shallow water model and its 4D-Var data assimilation system. Atmosphere, 14(1), 157. https://doi.org/10.3390/atmos14010157

Tuchtenhagen, P., et al. (2020). WRF model assessment for wind intensity and power density simulation in the southern coast of Brazil. Energy, 190, 116341. http://dx.doi.org/10.1016/j.energy.2019.116341

Tuchtenhagen, P., Basso, J., & Yamasaki, Y. (2014). Avaliação do p otencial eólico no Brasil em 2011. Ciência e Natura, 36(2). http://dx.doi.org/10.5902/2179460X13148

Tuchtenhagen, P. N. (2019). Variabilidade do vento e potencial para energia eólica offshore no litoral sul do Brasil (Tese de doutorado). Universidade Federal do Rio Grande do Norte, Centro de Ciências Exatas e da Terra, Programa de Pós-Graduação em Ciências Climáticas, Natal.

Turkovska, O., Castro, G., Klingler, M., Gomes, S. C., & Lucena, A. F. P. (2021). Land-use impacts of Brazilian wind power expansion. Environmental Research Letters, 16(2), 024010. https://doi.org/10.1088/1748-9326/abd12f

Ulazia, A., Saenz, J., & Ibarra-Berastegui, G. (2016). Sensitivity to the use of 3DVAR data assimilation in a mesoscale model for estimating offshore wind energy potential: A case study of the Iberian northern coastline. Applied Energy, 180, 617–627. https://doi.org/10.1016/j.apenergy.2016.07.115

Varga, Á. J., & Breuer, H. (2022). Evaluation of convective parameters derived from pressure level and native ERA5 data and different resolution WRF climate simulations over Central Europe. Climate Dynamics, 58(5–6), 1569–1585. https://doi.org/10.1007/s00382-021-05979-3

Vidal, D. B., Torres, E. A., & de Jong, P. (2023). Study of indicators on regulation for offshore wind energy exploitation in Brazil. Delos: Desarrollo Local Sostenible, 16(48), 3375–3398. https://doi.org/10.55905/rdelosv16.n48-025

Vinhoza, A., & Schaeffer, R. (2021). Brazil’s offshore wind energy potential assessment based on a spatial multi-criteria decision analysis. Renewable and Sustainable Energy Reviews, 146, 111185. https://doi.org/10.1016/j.rser.2021.111185

Wang, Y., Li, X., & Chen, Y. (2023). Combined assimilation of hourly rainfall data and every 10-min AHI radiance with WRF 4DVar for the short-range heavy rainfall forecast in Eastern China. Atmospheric Research, 292(1), 106867. https://doi.org/10.1016/j.atmosres.2023.106867

Wieringa, J. (1986). Roughness‐dependent geographical interpolation of surface wind speed averages. Quarterly Journal of the Royal Meteorological Society, 112(473), 867–889. https://doi.org/10.1002/qj.49711247316

Wilks, D. S. (2020). Statistical methods in the atmospheric sciences (4ª ed.). Elsevier. https://doi.org/10.1016/C2017-0-03921-6

Willmott, C. J., Matsuura, K., & Robeson, S. M. (2009). Ambiguities inherent in sums-of-squares-based error statistics. Atmospheric Environment, 43(3), 749–752. https://doi.org/10.1016/j.atmosenv.2008.10.005

Witha, B., Hahmann, A. N., Sile, T., Dörenkämper, M., Ezber, Y., Bustamante, E. G., Gonzalez-Rouco, J. F., Leroy, G., & Navarro, J. (2019). Report on WRF model sensitivity studies and specifications for the mesoscale wind atlas production runs: Deliverable D4.3. NEWA - New European Wind Atlas. https://doi.org/10.5281/zenodo.2682604

Wu, C., Wang, Q., Luo, K., & Fan, J. (2022). Mesoscale impact of the sea surface on the performance of offshore wind farms. Journal of Cleaner Production, 372, 133741. https://doi.org/10.1016/j.jclepro.2022.133741

Xiang, L., Yang, X., Hu, A., Su, H., & Wang, P. (2022). Condition monitoring and anomaly detection of wind turbine based on cascaded and bidirectional deep learning networks. Applied Energy, 305, 117925. https://doi.org/10.1016/j.apenergy.2021.117925

Yu, E., Bai, R., Chen, X., & Shao, L. (2022). Impact of physical parameterizations on wind simulation with WRF V3.9.1.1 under stable conditions at planetary boundary layer gray-zone resolution: A case study over the coastal regions of North China. Geoscientific Model Development, 15(21), 8111–8134. https://doi.org/10.5194/gmd-15-8111-2022

Zack, J., Natenberg, E., Young, S., Manobianco, J., & Kamath, C. (2010). Application of ensemble sensitivity analysis to observation targeting for short-term wind speed forecasting (LLNL-TR-424442). Lawrence Livermore National Laboratory. https://doi.org/10.2172/972845

Zucatelli, P. J., Nascimento, E. G. S., Santos, A. Á. B., Arce, A. M. G., & Lopes, F. C. (2021). An investigation on deep learning and wavelet transform to nowcast wind power and wind power ramp: A case study in Brazil and Uruguay. Energy, 230, 120842. https://doi.org/10.1016/j.energy.2021.120842

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2025-02-17

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Cruz Fernandes, G., Teixeira Lemos, A., Bitencourt Vidal, D., & Andrade Torres, E. (2025). Estimativa da Velocidade do Vento Offshore na Região Litorânea do Sul do Estado da Bahia. Revista Brasileira De Geografia Física, 18(2), 1523–1547. https://doi.org/10.26848/rbgf.v18.2.p1523-1547

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