Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2021GL094407 |
Forecasting the Indian Ocean Dipole with Deep Learning Techniques | |
Jun Liu; Youmin Tang; Yanling Wu; Tang Li; Qiang Wang; Dake Chen | |
2021-10-11 | |
发表期刊 | Geophysical Research Letters
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出版年 | 2021 |
英文摘要 | In the present research, IOD prediction was explored using statistical methods based on deep learning techniques. First, convolutional neural network (CNN) models were trained using sea-surface-temperature anomaly (SSTA) maps of the Indian Ocean from 1854 to 1989, and the properly trained CNN models were then validated with the period from 1991 to 2019. The results indicate that the deep learning approach is capable of forecasting the IOD at lead times up to seven months. The forecast skills of CNN are superior to those of the dynamic models in the North American Multi-Model Ensemble (NMME). The CNN outperforms the NMME with lower sensitivity to predictability barriers and fewer systematic errors. Moreover, the gradient heat map analysis demonstrates that the triggering precursors selected by CNN models for IOD events are novel and physically sensible. These results suggest the CNN to be a new and effective tool for both IOD prediction and comprehension. |
领域 | 气候变化 |
URL | 查看原文 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/340098 |
专题 | 气候变化 |
推荐引用方式 GB/T 7714 | Jun Liu,Youmin Tang,Yanling Wu,et al. Forecasting the Indian Ocean Dipole with Deep Learning Techniques[J]. Geophysical Research Letters,2021. |
APA | Jun Liu,Youmin Tang,Yanling Wu,Tang Li,Qiang Wang,&Dake Chen.(2021).Forecasting the Indian Ocean Dipole with Deep Learning Techniques.Geophysical Research Letters. |
MLA | Jun Liu,et al."Forecasting the Indian Ocean Dipole with Deep Learning Techniques".Geophysical Research Letters (2021). |
条目包含的文件 | 条目无相关文件。 |
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