A Hybrid Neural Network Model for ENSO Prediction in Combination with Principal Oscillation Pattern Analyses
文献类型：期刊论文
| 作者 | Zhou, Lu1,4,5; Zhang, Rong-Hua1,2,3,4,5
|
| 刊名 | ADVANCES IN ATMOSPHERIC SCIENCES
 |
| 出版日期 | 2022-03-08 |
| 页码 | 14 |
| 关键词 | ENSO prediction the principal oscillation pattern (POP) analyses neural network a hybrid approach |
| ISSN号 | 0256-1530 |
| DOI | 10.1007/s00376-021-1368-4 |
| 通讯作者 | Zhang, Rong-Hua(rzhang@qdio.ac.cn) |
| 英文摘要 | El Nino-Southern Oscillation (ENSO) can be currently predicted reasonably well six months and longer, but large biases and uncertainties remain in its real-time prediction. Various approaches have been taken to improve understanding of ENSO processes, and different models for ENSO predictions have been developed, including linear statistical models based on principal oscillation pattern (POP) analyses, convolutional neural networks (CNNs), and so on. Here, we develop a novel hybrid model, named as POP-Net, by combining the POP analysis procedure with CNN-long short-term memory (LSTM) algorithm to predict the Nino-3.4 sea surface temperature (SST) index. ENSO predictions are compared with each other from the corresponding three models: POP model, CNN-LSTM model, and POP-Net, respectively. The POP-based pre-processing acts to enhance ENSO-related signals of interest while filtering unrelated noise. Consequently, an improved prediction is achieved in the POP-Net relative to others. The POP-Net shows a high-correlation skill for 17-month lead time prediction (correlation coefficients exceeding 0.5) during the 1994-2020 validation period. The POP-Net also alleviates the spring predictability barrier (SPB). It is concluded that value-added artificial neural networks for improved ENSO predictions are possible by including the process-oriented analyses to enhance signal representations. |
| 资助项目 | Strategic Priority Research Program of the Chinese Academy of Sciences[XDA19060102] ; National Natural Science Foundation of China [NSFC][41690122(41690120)] ; National Natural Science Foundation of China [NSFC][42030410] |
| WOS研究方向 | Meteorology & Atmospheric Sciences |
| 语种 | 英语 |
| WOS记录号 | WOS:000766037100001 |
| 出版者 | SCIENCE PRESS |
| 源URL | [http://ir.qdio.ac.cn/handle/337002/178200]  |
| 专题 | 中国科学院海洋研究所 |
| 通讯作者 | Zhang, Rong-Hua |
| 作者单位 | 1.Chinese Acad Sci, Inst Oceanol, Qingdao 266071, Peoples R China 2.Chinese Acad Sci, Ctr Excellence Quaternary Sci & Global Change, Xian 710061, Peoples R China 3.Pilot Natl Lab Marine Sci & Technol, Lab Ocean & Climate Dynam, Qingdao 266237, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100029, Peoples R China 5.Chinese Acad Sci, Ctr Ocean MegaSci, Qingdao 266071, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhou, Lu,Zhang, Rong-Hua. A Hybrid Neural Network Model for ENSO Prediction in Combination with Principal Oscillation Pattern Analyses[J]. ADVANCES IN ATMOSPHERIC SCIENCES,2022:14. |
| APA | Zhou, Lu,&Zhang, Rong-Hua.(2022).A Hybrid Neural Network Model for ENSO Prediction in Combination with Principal Oscillation Pattern Analyses.ADVANCES IN ATMOSPHERIC SCIENCES,14. |
| MLA | Zhou, Lu,et al."A Hybrid Neural Network Model for ENSO Prediction in Combination with Principal Oscillation Pattern Analyses".ADVANCES IN ATMOSPHERIC SCIENCES (2022):14. |
入库方式： OAI收割
来源：海洋研究所
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