ConvLSTM-Based Wave Forecasts in the South and East China Seas

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.

Automated summary

This paper establishes a 2D SWH prediction model for the South and East China Seas based on the ConvLSTM algorithm, achieving high accuracy and efficiency in wave forecasting under both normal and extreme conditions. The model shows improved performance compared to traditional numerical wave models.