ConvLSTM-Based Wave Forecasts in the South and East China Seas - Zhou, Shuyi; Xie, Wenhong; Lu, Yuxiang; Wang, Yuanlin; Zhou, Yulong; Hui, Nian; Dong, Changming (Frontiers in Marine Science, 2021)
Numerical wave models have been developed for the wave forecast in last two decades; however, it faces challenges in terms of the requirement of large computing resources and improvement of accuracy. Based on a convolutional long short-term memory (ConvLSTM) algorithm, this paper establishes a two-dimensional (2D) significant wave height (SWH) prediction model for the South and East China Seas trained by WaveWatch III (WW3) reanalysis data. We conduct 24-h predictions under normal and extreme conditions, respectively. Under the normal wave condition, for 6-, 12-, and 24-h forecasting, their correlation coefficients are 0.98, 0.93, and 0.83, and the mean absolute percentage errors are 15, 29, and 61%. Under the extreme condition (typhoon), for 6 and 12 h, their correlation coefficients are 0.98 and 0.94, and the mean absolute percentage errors are 19 and 40%, which is better than the model trained by all the data. It is concluded that the ConvLSTM can be applied to the 2D wave forecast with high accuracy and efficiency.