基于CNN-LSTM的珠江河口台风过程实时滚动修正预报

Real-time rolling correction forecasting of typhoon process in the Pearl River estuary based on CNN-LSTM

邓志弘 ¹刘丙军 ²张卡 ¹胡仕焜 ¹曾慧 ³张明珠 ³李丹³

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作者信息

1. 中山大学土木工程学院,广东珠海 519085
2. 中山大学土木工程学院,广东珠海 519085;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
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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%.

Abstract

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.

关键词

实时滚动预报/台风/珠江河口/深度学习/误差校正

Key words

real-time rolling forecast/typhoon/Pearl River estuary/deep learning/error correction

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基金项目

广州市水务科技项目(GZSWKJ-2020-2)

国家自然科学基金资助项目(52179029)

国家自然科学基金资助项目(51879289)

出版年

2024

海洋预报

国家海洋环境预报中心

海洋预报

CSTPCDCSCD北大核心

影响因子：0.37

ISSN：1003-0239

参考文献量24

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