Comparison of wind speed data in Northeast Brazil of ERA-40 and National Institute of Meteorology (INMET) using entropy measurements

Authors

DOI:

https://doi.org/10.33448/rsd-v9i8.5257

Keywords:

Northeast; Wind speed; Sample entropy; Cross-sample entropy.

Abstract

Historical wind speed series from the databases of the National Institute of Meteorology (INMET) and ECMWF Re-Analyzes (ERA-40) were analyzed in order to quantify the degree of regularity of the time series and the degree of similarity between the databases of conventional stations (INMET) and reanalysis (ERA-40), using the Sample Entropy and cross-Sample Entropy methods of information theory. Due to the lack of information in the INMET database, the analyzes were carried out over a period of eight years of simultaneous data (1993 to 2000) for the INMET and ERA-40 database, during 00h, 12h and in the Complete / Total (original series). The results show that the largest wind speed records for different series are found in the North of the four sub-regions of the NE. Sample Entropy showed greater regularity of wind speed in the Middle North, an area where wind speed is lower, with better predictability in this area. The cross-Sample Entropy showed a moderate synchronization of the INMET and ERA-40 series, indicating an overestimation or underestimation of the ERA-40 data in relation to the INMET data.

References

ABEEólica. (2020). Associação Brasileira de Energia Eólica. Acesso em 15 maio 2020, em: http://abeeolica.org.br/wp-content/uploads/2020/04/Infovento-15_PT.pdf.

Amarante, O. A., Brower, M., Zack, J., & Sá, A. L. (2001). Atlas do potencial eólico brasileiro. Brasília, DF. CD-ROM.

Camelo, H. do N., Lucio, P. S., Gomes, O. M., & Leal Junior, J. B. V. (2016). Predição de velocidade do vento em municípios do Nordeste brasileiro através de regressão linear e não linear para fins de geração eólica. Revista Brasileira de Geografia Física, 9(03), 927-939.

Carneiro, T. C., & de Carvalho, P. C. M. (2015). Caracterização de potencial eólico: estudo de caso para Maracanaú (CE), Petrolina (PE) e Parnaíba (PI). Revista Brasileira de Energia Solar, 6(1). Acesso em 29 abril 2020. Disponível em: https://rbens.emnuvens.com.br/rbens/article/view/122/122.

Chou, C. M. (2014). Complexity analysis of rainfall and runoff time series based on sample entropy in different temporal scales. Stochastic Environmental Research And Risk Assessment, 28(6), 1401-1408. doi: https://doi.org/10.1007/s00477-014-0859-6.

ECMWF. European Centre for Medium-Range Weather Forecasts. 2017. Disponível em: <https://www.ecmwf.int/>.

GWEC, G. W. P. C. (2019). Global Wind Energy Outlook. Brussels.

Lira, M. A. T., Neto, J. M. M., Loiola, J. V. L. d., Silva, E. M. d., & Alves, J. M. B. (2017). Caracterização do regime de ventos no Piauí para o aproveitamento de energia eólica. Revista Brasileira de Meteorologia, 32(1), 77–88. doi: http://dx.doi.org/10.1590/0102-778632120150712.

Luo, W., Taylor, M. C., & Parker, S. R. (2008). A comparison of spatial interpolation methods to estimate continuous wind speed surfaces using irregularly distributed data from England and Wales. International Journal of Climatology: A Journal of the Royal Meteorological Society, 28(7), 947-959. doi: https://doi.org/10.1002/joc.1583.

Pereira A. S. et al. (2018). Metodologia da pesquisa científica. [e-book]. Santa Maria. Ed. UAB/NTE/UFSM. Disponível em: https://repositorio.ufsm.br/bitstream/handle/1/15824/Lic_Computacao_Metodologia-Pesquisa-Cientifica.pdf?sequence=1.

Pincus, S. M. (1991). Approximate entropy as a measure of system complexity. Proceedings of the National Academy of Sciences, 88(6), 2297–2301. doi: https://doi.org/10.1073/pnas.88.6.2297.

Reddy, Y. V., & Sebastin, A. (2006, December). Parameters for estimation of entropy to study price manipulation in stock market. In 10th Capital Markets Conference, Indian Institute of Capital Markets Paper. doi: http://dx.doi.org/10.2139/ssrn.962329.

Richman, J. S., & Moorman, J. R. (2000). Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology-Heart and Circulatory Physiology, 278(6), H2039–H2049. doi: https://doi.org/10.1152/ajpheart.2000.278.6.H2039.

Santana, L. V. R., Stosic, T., Dezotti, C. H., De Albuquerque Moura, G. B., De Araújo, L. H. G. D., & da Silva, A. S. A. (2015). Spatial analyses of wind speed in the North-Brazil with data from ERA-40. Revista Brasileira de Biometria, 33(3), 414-432. Acesso em 30 maio 2020. Disponível em: http://www.biometria.ufla.br/index.php/BBJ/article/view/24.

Schmidt, J., Cancella, R., & Junior, A. O. P. (2016). The effect of windpower on long-term variability of combined hydro-wind resources: The case of Brazil. Renewable and Sustainable Energy Reviews, 55, 131-141. doi: https://doi.org/10.1016/j.rser.2015.10.159.

Shepard, D. (1968). A two-dimensional interpolation function for irregularly-spaced data. In Proceedings of the 1968 23rd ACM national conference, 517-524. doi: https://doi.org/10.1145/800186.810616.

Silva, Vicente de Paulo Rodrigues da, Pereira, Emerson Ricardo Rodrigues, & Almeida, Rafaela Silveira Rodrigues. (2012). Estudo da variabilidade anual e intra-anual da precipitação na região Nordeste do Brasil. Revista Brasileira de Meteorologia, 27(2), 163-172. https://doi.org/10.1590/S0102-77862012000200005.

Stüker, E., Schuster, C. H., Schuster, J. J., Santos, D. C., Medeiros, L. E., Costa, F. D., ... & Puhales, F. S. (2016 Comparison of wind data of ERA-Interim reanalysis and cfsr with the data from automatic inmet stations Rio Grande Do Sul. Ciência e Natura, 38(IX WORKSHOP), 284. Doi: 10.5902/2179460X20233.

Team, R. C. (2000). A language and environment for statistical computing. R Foundation for Statistical Computing.

Varejão-Silva, M. A. (2006). Meteorologia e climatologia. Recife, PE.

Witzler, L. T., Ramos, D. S., Camargo, L. A. S., & Guarnier, E. (2016, June). Reconstruction of wind generation historical series aiming at the analysis of energy complementarity: Methodology and applications. In 2016 13th International Conference on the European Energy Market (EEM), 1-6. IEEE. Doi: 10.1109/EEM.2016.7521324.

Published

12/07/2020

How to Cite

SANTANA, L. V. R.; STOSIC, T.; FERREIRA, T. A. E.; SILVA, A. S. A. da. Comparison of wind speed data in Northeast Brazil of ERA-40 and National Institute of Meteorology (INMET) using entropy measurements. Research, Society and Development, [S. l.], v. 9, n. 8, p. e446985257, 2020. DOI: 10.33448/rsd-v9i8.5257. Disponível em: https://rsdjournal.org/index.php/rsd/article/view/5257. Acesso em: 5 nov. 2024.

Issue

Section

Agrarian and Biological Sciences