典型文献
A Hybrid Neural Network Model for ENSO Prediction in Combination with Principal Oscillation Pattern Analyses
文献摘要：
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.
文献关键词：
中图分类号：
作者姓名：
Lu ZHOU;Rong-Hua ZHANG
作者机构：
CAS Key Laboratory of Ocean Circulation and Waves,Institute of Oceanology,and Center for Ocean Mega-Science,Chinese Academy of Sciences,Qingdao 266071,China;University of Chinese Academy of Sciences,Beijing 100029,China;Laboratory for Ocean and Climate Dynamics,Pilot National Laboratory for Marine Science and Technology,Qingdao 266237,China;Center for Excellence in Quaternary Science and Global Change,Chinese Academy of Sciences,Xi'an 710061,China
文献出处：
引用格式：
[1]Lu ZHOU;Rong-Hua ZHANG-.A Hybrid Neural Network Model for ENSO Prediction in Combination with Principal Oscillation Pattern Analyses)[J].大气科学进展（英文版）,2022(06):889-902
A类：
B类：
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AB值：
0.588196
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