COMPARISON OF WIND DATA OF ERA-INTERIM REANALYSIS AND CFSR WITH THE DATA FROM AUTOMATIC INMET STATIONS IN RIO GRANDE DO SUL

Authors

  • Eduardo Stüker Universidade Federal do Pampa
  • Cristiano Henrique Schuster Universidade Federal do Pampa
  • Jean Jonathan Schuster Universidade Federal do Pampa
  • Daniel Caetano Santos Universidade Federal de Santa Maria
  • Luis Eduardo Medeiros Universidade Federal do Pampa
  • Felipe Denardin Costa Universidade Federal do Pampa
  • Giuliano Demarco Universidade Federal de Santa Maria
  • Franciano Scremin Puhales Universidade Federal de Santa Maria

DOI:

https://doi.org/10.5902/2179460X20233

Keywords:

Wind Energy. Wind field. Reanalysis. ERA-Interim. CFSR.

Abstract

Wind power is currently one of the sources of electricity the fastest growing worldwide. However, especially in Brazil, it is still very difficult to locate areas with reliable winds for the implementation of a wind farm, because there is not enough data density that guarante the wind farm efficiency. Thus, the development of models that simulate wind conditions are extremely important for studies and research in this area. In this way, weather reanalysis data can be used as input into high-resolution regional models, for example. Therefore, this work presents a study comparing two sets of weather Reanalysis of wind data - ERA-Interim and CFSR - with measured data from automated weather stations of the National Institute of Meteorology in the Rio Grande do Sul state in order to obtain the coefficient correlation of data from reanalysis with data measured for each measurement point and for every season. For better visualization of the correlation results, it is also built contour maps with correlation coefficient where can be seeing that the best performance of the CFSR reanalysis.

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Author Biography

Eduardo Stüker, Universidade Federal do Pampa

Possui Graduação em Engenharia Elétrica pela Universidade Federal do Pampa (2014). Atualmente é mestrando em engenharias no Programa de Pós Graduação em Engenharia (PPENG) pela Universidade Federal do Pampa.

References

Carvalho, D., Rocha, A., Gómez-Gesteira, M., Santos, C. S. (2013). WRF wind simulation and wind energy production estimates forced by different reanalyses: Comparison with observed data for Portugal. Applied Energy, 117, 116–126.

CCEE (2015). Usinas eólicas mais que dobram a produçãoo de energia no primeiro semestre, URL http://www.ccee.org.br/portal/faces/pages_publico/noticias-opiniao/noticias/noticialeitura?contentid=CCEE_357460&_adf.ctrl-state=17wx1e5can_45&_afrLoop=1130246065565635#%40%3Fcontentid%3DCCEE_357460%26_afrLoop%3D1130246065565635%26_adf.ctrl-state%3Dqi6wk9feb_4.

Dee, D., Uppala, S., Simmons, A., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M., Balsamo, G., Bauer, P., et al. (2011). The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society, 137(656), 553–597.

Hanna, S. R. (1988). Hibrid Plume Dispersion Model (HPDM) Development and Evaluation. Journal of applied Meteorology, 28, 206–223.

INMET (2011). NOTA TÉCNICA No. 001/2011/SEGER/LAIME/CSC/INMET - Rede de Estações Meteorológicas Automáticas do INMET. MINISTÉRIO DA

AGRICULTURA, PECUÁRIA E ABASTECIMENTO, URL http://www.inmet.gov.br/portal/css/content/topo_iframe/pdf/Nota_Tecnica-Rede_estacoes_INMET.pdf.

Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Leetmaa, A., Reynolds, R., Chelliah, M., Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K. C., Ropelewski, C., Wang, J., Jenne, R., Joseph, D. (1996). The ncep/ncar 40-year reanalysis project. Bulletin of the American Meteorological Society, 77(3), 437–471.

Liléo, S., Petrik, O. (2011). Investigation on the use of NCEP/NCAR, MERRA and NCEP/CFSR reanalysis data in wind resource analysis. European Wind Energy Association.

Pinto, M. d. O. (2013). Fundamentos de Energia Eólica. LTC, Rio de Janeiro, Brasil.

Rienecker, M. M., Suarez, M. J., Gelaro, R., Todling, R., Bacmeister, J., EmilyLiu, Bosilovich, M. G., Schubert, S. D., Takacs, L., Kim, G. K., Bloom, S., Chen, J., Collins, D., Conaty, A., da Silva, A., Gu, W., Joiner, J., D.Koster, R., Lucchesi, R., Molod, A., Owens, T., Pawson, S., Pegion, P., Redder, C. R., Reichle, R., Robertson, F. R., Ruddick, A. G., Sienkiewicz, M., Woollen, J. (2011). MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications. Journal of Climate, 24, 3624–3648.

Saha, S., Moorthi, S., Pan, H. L., Wu, X., Wang, J., Nadiga, S., ck Tripp, P., Kistler, R., en, J. W., d Behringer, D., Liu, H., Stokes, D., Grumbine, R., Gayno, G., Wang, J., Hou, Y. T., Chuang, H. y., Juang, H. M. H., Sela, J., Iredell, M., Treadon, R., Kleist, D., t, P. V. D., Keyser, D., Derber, J., Ek, M., e Meng, J., Wei, H., an Yang, R., Lord, S., van den Dool, H., Kumar, A., u Wang, W., Long, C., iah, M. C., Xue, Y., n Huang, B., Schemm, J. K., ey Ebisuzaki, W., Lin, R., Xie, P., Chen, M., Zhou, S., Higgins, W., Zou, C. Z., Liu, Q., Chen, Y., Han, Y., Cucurull, L., Reynolds, R. W., Rutledge, G., Goldberg, M. (2010). The NCEP climate forecast system reanalysis. American Meteorological Society, 91, 1015–1057.

Stensrud, D. J. (2007). Parameterization schemes: keys to understanding numerical weather prediction models. Cambridge University Press.

Published

2016-07-20

How to Cite

Stüker, E., Schuster, C. H., Schuster, J. J., Santos, D. C., Medeiros, L. E., Costa, F. D., Demarco, G., & Puhales, F. S. (2016). COMPARISON OF WIND DATA OF ERA-INTERIM REANALYSIS AND CFSR WITH THE DATA FROM AUTOMATIC INMET STATIONS IN RIO GRANDE DO SUL. Ciência E Natura, 38, 284–290. https://doi.org/10.5902/2179460X20233

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