报告详情
Deep learning-based ensemble forecast and predictability analysis of the Kuroshio intrusion into the South China Sea
编号：206
访问权限：仅限参会人
更新：2024-10-10 15:50:16 浏览：43次
张贴报告
报告开始：2025年01月16日 17:50（Asia/Shanghai）
报告时间：15min
所在会场：[S44] Session 44-Western Boundary Currents, Eddies and Their Impacts on Multi-disciplinary Aspects [S44-P] Western Boundary Currents, Eddies and Their Impacts on Multi-disciplinary Aspects
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摘要
The Kuroshio intrusion (KI) into the South China Sea (SCS) significantly affects the environment, ecology, and climate change of the SCS. However, due to the nonlinearity of KI, its numerical prediction often requires large ensemble size to measure prediction uncertainty. The huge computational costs of large numbers of members and high-resolution numerical models pose significant challenges for KI prediction. We, therefore, construct a Kuroshio ensemble deep learning prediction system (KurNet) through taking different values of parameters to predict KI paths because the deep learning models have high prediction skills and low computational cost. The KurNet containing 64 ensemble members can not only output ensemble mean forecast result of the Kuroshio path, but also estimate probability density functions for the path types. The KurNet illustrates a high predictive ability for the KI with the mean classification accuracy of 71.9% and root mean square error of 0.913 on the testing set, which is superior to the single control prediction by ~1.0–2.9%, although the control prediction model is selected as one of the ensemble members with the best predictive ability on the validation set. Furthermore, the predictability analysis of 10 KI events indicates that when the lead time is 3 days, the most important areas are in the east of Luzon Island due to the upstream Kuroshio transport. As the lead time increases, the most important area is in the Luzon Strait due to the eddy activity. Observing system simulation experiments reveal that the KI forecast skill can be enhanced by ~12–18%, when uncertainties of the input data in these important regions are removed.
关键词
Kuroshio intrusion, Deep learning, Predictability, South China Sea, Ensemble forecast
报告人
Junkai Qian
PhD Hohai University稿件作者
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重要日期
-
会议日期
01月14日
2025
至01月17日
2025
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09月27日 2024
初稿截稿日期
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12月14日 2024
注册截止日期
主办单位
State Key Laboratory of Marine Environmental Science, Xiamen University
联系方式
- JKejing GU
- xm******@xmu.edu.cn
- +86*********
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