Predicting significant wave height with artificial neural networks in the South Atlantic Ocean: a hybrid approach

Accurate simulations of significant wave height (Hs) are extremely important for the safety of navigation, port operations, and oil and gas exploration. Thus, accurate forecasts of Hs are essential for accident prevention and maintenance of services vital to the economy. Considering the limitations of traditional numerical modeling, such as the typical model underestimation of Hs under severe conditions, forecasting Hs using artificial neural networks is a promising method and a complementary approach. In this study we develop a post-processing model using Long Short-Term Memory (LSTM) algorithm to improve outputs from the numerical model WAVEWATCH III (WW3) at Santos Basin, Brazil. The hybrid scheme is focused on the simulations of 1-, 2-, 3- and 4-day residues (difference between observations and WW3) using measurements from a local wave buoy moored in deep water. The results of the hybrid model (WW3+LSTM) show a better performance compared with WW3, being capable of better representing the peak of the events and storms. On average, the gains from using WW3+LSTM reach 3.8% in Correlation Coefficient (CORR), 14.2% in Bias (BIAS), 10.2% in Root Mean Squared Error (RMSE), and 10.7% in Scatter Index (SI). The hybrid model developed allows high-skill forecasts to be carried out on large domains and through longer horizons.

Automated summary

Accurate simulations of significant wave height (Hs) are crucial for navigation safety and resource exploration. In this study, a post-processing model using LSTM algorithm is developed to improve the outputs of the numerical model WW3. The hybrid model, WW3+LSTM, shows better performance compared to WW3, with improved representation of peak events and storms. On average, the gains from using WW3+LSTM reach 3.8% in CORR, 14.2% in BIAS, 10.2% in RMSE, and 10.7% in SI.