基于ConvLSTM的中国东南沿海波浪智能预报和评估

Intelligent wave forecasting and evaluation along the southeast coast of China based on ConvLSTM method

相较于半理论半分析和数值模型的波浪预报方法,智能波浪预报有着精度高、计算资源需求低的优势。该文基于卷积长短期记忆网络(convolutional long short-term memory network,ConvLSTM)算法,建立了有效波高(significant wave height,SWH)二维预报模型,以中国东南沿海2014—2022年ERA5数据进行训练,通过敏感性试验优化模型配置,并开展中国东南沿海SWH在2023年4个预报时效(6 h、12 h、18 h、24 h)下的预测性能评估。敏感性试验显示,输入时间序列长度N=4(即输入-18 h,-12 h,-6 h,0 h的SWH值)时,模型在4个预报时效下的准确性均优于其他时间序列长度;输入物理要素组合为SWH、平均波向和海面10 m风矢量时,模型在12 h、18 h和24 h预报时效下的准确性优于其他组合。通过对ConvLSTM模型训练及配置的精细调整,可以实现对中国东南沿海SWH的二维、高精度的智能预报。

Compared with the semi-theoretical and semi-analytical wave forecasting and numerical modeling,artificial intelligence wave forecasting has the advantages of higher forecasting accuracy and lower computational resource requirements.In this paper,a two-dimensional significant wave height(SWH)forecasting model for the southeast coast of China is established based on the convolutional long short-term memory network(ConvLSTM)algorithm using ERA5(ECMWF Reanalysis v5)reanalysis data as the initial field.The data from 2014-2022 are used to train the forecasts of SWH for the next 6 h,12 h,18 h and 24 h,and the data from 2023 are used for testing.Sensitivity tests are carried out to optimize the model configuration and evaluate the prediction performance of SWH in the southeast coast of China at four period validity(6 h,12 h,18 h,24 h)in 2023.Sensitivity tests show that when input time series length N=4(input SWH value of-18 h,-12 h,-6 h,0 h),the accuracies of the model at four period validity are better than those of other time series length.When the combination of input physical elements is SWH,mean wave direction and sea surface 10 m wind vector,the accuracy of the model is better than other combinations at 12 h,18 h and 24 h.Through the fine-tuning of ConvLSTM model training and configuration,the two-dimensional and high-precision intelligent prediction of SWH in the southeast coast of China can be realized.