Neural Network Model to Predict a Storm Surge

Marilia M. F. de Oliveira Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil

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Nelson Francisco F. Ebecken Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil

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Jorge Luiz Fernandes de Oliveira Departamento de Geografia, Instituto de Geociências, Universidade Federal Fluminense, Niterói, Brazil

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Isimar de Azevedo Santos Departamento de Meteorologia, Instituto de Geociências, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil

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Abstract

The southeastern coast of Brazil is frequently affected by meteorological disturbances such as cold fronts, which are sometimes associated with intense extratropical cyclones. These disturbances cause oscillations on the sea surface, generating low-frequency motions. The relationship of these meteorologically driven forces in low frequency to the storm-surge event is investigated in this work. A method to predict coastal sea level variations related to meteorological events that use a neural network model (NNM) is presented here. Pressure and wind values from NCEP–NCAR reanalysis data and tide gauge time series from the Cananéia reference station in São Paulo State, Brazil, were used to analyze the relationship between these variables and to use them as input to the model. Meteorological influences in the sea level fluctuations can be verified by filtering the astronomical tide frequencies for periods lower than tidal cycles (periods higher than 24 h). Thus, a low-pass filter was applied in the tide gauge and meteorological time series for periods lower than tides to identify more readily the interactions between coastal sea level response and atmospheric-driven forces. Statistical analyses on time and frequency domain were used. Maxima correlations and coherence between the low-frequency sea level and meteorological series could be defined using the time lag of the NNM input variables. The model was tested for 6-, 12-, 18-, and 24-hourly forecasts, and the results were compared with filtered sea level values. The results show that this model is able to capture the effects of atmospheric and oceanic interactions. It can be considered to be an efficient model for predicting the nontidal residuals and can effectively complement the standard constant harmonic analysis model. A case study of a storm that impacted coastal areas of southeastern Brazil in March 1998 was analyzed and indicates that the neural network model can be effectively utilized in the Cananéia region.

Corresponding author address: Marilia M. F. de Oliveira, Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia—COPPE, UFRJ, Caixa Postal 68501, CEP 21945-970, Rio de Janeiro, RJ, Brazil. Email: marilia@coc.ufrj.br

Abstract

The southeastern coast of Brazil is frequently affected by meteorological disturbances such as cold fronts, which are sometimes associated with intense extratropical cyclones. These disturbances cause oscillations on the sea surface, generating low-frequency motions. The relationship of these meteorologically driven forces in low frequency to the storm-surge event is investigated in this work. A method to predict coastal sea level variations related to meteorological events that use a neural network model (NNM) is presented here. Pressure and wind values from NCEP–NCAR reanalysis data and tide gauge time series from the Cananéia reference station in São Paulo State, Brazil, were used to analyze the relationship between these variables and to use them as input to the model. Meteorological influences in the sea level fluctuations can be verified by filtering the astronomical tide frequencies for periods lower than tidal cycles (periods higher than 24 h). Thus, a low-pass filter was applied in the tide gauge and meteorological time series for periods lower than tides to identify more readily the interactions between coastal sea level response and atmospheric-driven forces. Statistical analyses on time and frequency domain were used. Maxima correlations and coherence between the low-frequency sea level and meteorological series could be defined using the time lag of the NNM input variables. The model was tested for 6-, 12-, 18-, and 24-hourly forecasts, and the results were compared with filtered sea level values. The results show that this model is able to capture the effects of atmospheric and oceanic interactions. It can be considered to be an efficient model for predicting the nontidal residuals and can effectively complement the standard constant harmonic analysis model. A case study of a storm that impacted coastal areas of southeastern Brazil in March 1998 was analyzed and indicates that the neural network model can be effectively utilized in the Cananéia region.

Corresponding author address: Marilia M. F. de Oliveira, Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia—COPPE, UFRJ, Caixa Postal 68501, CEP 21945-970, Rio de Janeiro, RJ, Brazil. Email: marilia@coc.ufrj.br

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