基于CNN-LSTM的珠江河口台风过程实时滚动修正预报-海洋预报

基于CNN-LSTM的珠江河口台风过程实时滚动修正预报

单位：1. 中山大学土木工程学院, 广东珠海 519085;
2. 中山大学水资源与环境研究中心, 广东广州 510275;
3. 广州市水务科学研究所, 广东广州 510220

分类号：P457.8

出版年·卷·期（页码）：2024·41·第一期（94-103）

摘要：

为改善台风预报精度，基于实时滚动修正预报思路，利用卷积神经网络嵌套长短期记忆神经网络（CNN-LSTM）和误差校正（EC）技术，搭建了珠江河口台风实时预报模型。研究结果表明：“滚动预报”比单次预报有更好的路径和强度预报效果，随着模型滚动时间的延长，预报整体精度有逐渐改善的趋势。路径预报结果的均方根误差比单次预报减小了25.67%，强度预报结果的平均绝对误差比单次预报减小了65.04%；考虑误差校正的CNN-LSTM-EC的路径、强度“滚动预报”效果均优于CNN-LSTM，前者的路径预报误差较后者减小了22.57%，强度预报误差减小2.5%。

In order to improve the accuracy of typhoon forecasting, this paper introduces a real-time rolling corrected typhoon forecasting model in the Pearl River Estuary utilizing Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) neural network and Error Correction (EC) method. The results show that the rolling forecasts have better performances on typhoon's track and intensity than the single-time forecasts. The overall accuracy of the rolling forecasts increases gradually along with the prolong of the rolling time of the model. In comparison with the single-time forecasts, the root mean squared error of typhoon's track rolling forecasts decreases by 25.67% and the mean absolute error of typhoon's intensity rolling forecasts decreases by 65.04%. The real-time rolling corrected forecasts of typhoon's track and intensity based on CNN-LSTM-EC are better than those based on CNN-LSTM. Compared with the latter, the forecasting error of the former decreases by 22.57% on the typhoon's track and by 2.5% on the typhoon's intensity.

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