A Hybrid Neural Network Model for ENSO Prediction in Combination with Principal Oscillation Pattern Analyses
文献类型：CNKI期刊论文
| 作者 | Lu ZHOU ; Rong-Hua ZHANG
|
| 发表日期 | 2022-04-04 |
| 出处 | Advances in Atmospheric Sciences
 |
| 关键词 | ENSO prediction the principal oscillation pattern(POP) analyses neural network a hybrid approach |
| 英文摘要 | El Ni?o-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 Ni?o-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. |
| 文献子类 | CNKI期刊论文 |
| 资助机构 | supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA19060102) ; the National Natural Science Foundation of China [NSFC ; Grant Nos. 41690122(41690120), and 42030410] |
| 卷 | v.39期:06页:73-86 |
| 语种 | 英文; |
| 分类号 | P732.4 |
| ISSN号 | 0256-1530 |
| 源URL | [http://ir.qdio.ac.cn/handle/337002/187461]  |
| 专题 | 中国科学院海洋研究所 |
| 作者单位 | 1.CASKeyLaboratoryofOceanCirculationandWaves,InstituteofOceanology,andCenterforOceanMega-Science,ChineseAcademyofSciences 2.LaboratoryforOceanandClimateDynamics,PilotNationalLaboratoryforMarineScienceandTechnology 3.CenterforExcellenceinQuaternaryScienceandGlobalChange,ChineseAcademyofSciences 4.UniversityofChineseAcademyofSciences |
| 推荐引用方式 GB/T 7714 | Lu ZHOU,Rong-Hua ZHANG. A Hybrid Neural Network Model for ENSO Prediction in Combination with Principal Oscillation Pattern Analyses. 2022. |
入库方式： OAI收割
来源：海洋研究所
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