Estimativa dos períodos de retorno da velocidade e rajada do vento e altura significativa das ondas no Atlântico Sudoeste

• Autor(a) principal: Mazzaro Dias, Emille
• Data de Publicação: 2021
• Outros Autores: Pinto de Almeida Palmeira, Ana Cristina, Libonati, Renata
• Tipo de documento: Artigo
• Idioma: por; eng
• Título da fonte: Sistemas & Gestão
• Texto Completo: https://www.revistasg.uff.br/sg/article/view/1703

The Southwest Atlantic region hosts the two main oil basins in Brazil: Campos Basin and Santos Basin. In these regions, the study of extreme events and the estimation of return level are crucial issues to ensure the structural safety of ships, offshore platforms and coastal constructions. The knowledge of the wave and wind regime of a region is indispensable because from this analysis it is possible to estimate the energy load that a structure may be subjected to during its lifetime. In this context, the present work aims to estimate the 100-year return level of the variables: significant wave height (Hs), wind speed and gust at 10 m for each direction in the two main oil basins in Brazil. For this, data from ECMWF's ERA5 Reanalysis over the period 1979-2019 and the Peaks Over Threshold (POT) statistical model were used, using the Maximum Likelihood estimator. The results show that the SE, S and NW directions returned the highest return level values for wind speed. According to the analysis of the gusts results, the largest values were between 20 m/s and 30 m/s and considering the highest confidence interval, the values reached 38.64 m/s for the S direction. In the results of the return level estimates for Hs, it was found that the largest swells were from SW. With this, it is concluded that: although in situ observed data are preferable when conducting a study of extremes, reanalysis is a great alternative for limited locations of collected data. Another finding was that the POT Model is an excellent tool for the study of extremes, as it is improved and can select a more consistent and larger number of values than previously used models.