A Hybrid Neural Network Model for ENSO Prediction in Combination with Principal Oscillation Pattern Analyses

Abstract

El Niño-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 Niño-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.

摘 要

厄尔尼诺-南方涛动(ENSO)是地球气候系统中最强的年际变率信号。当前基于数理方程的动力模式可以提前至少6个月来对ENSO进行较好的预测,但在实时预测中仍存在较大的误差和不确定性。近几十年来,各种统计方法的提出和基于大数据的神经网络发展极大地提高了我们对ENSO的认识和预测能力,包括基于主振荡型(POP)分析的线性统计模式和基于神经网络的非线性统计模式等。本文中,我们把POP方法与神经网络卷积-长短时记忆网络(CNN-LSTM)相结合构建了一个混合型神经网络模式(简称为POP-Net),用于"Ni" "n" ̃"o" 3.4区海表温度(SST)异常的预测,并比较了单独POP模式、单独CNN-LSTM模型和其两者相结合的POP-Net模型对ENSO的预测能力。结果表明,将POP方法作为多尺度信号提取技术融合到神经网络模型中有助于增强ENSO相关的年际信号及对ENSO预测的影响,从而使得POP-Net模型比单独POP模型和神经网络模型都具有更好的预测性能。在1994-2020年测试期内,POP-Net模型可以提前17个月提供有效的ENSO预测(相关系数>0.5),并大幅改善了春季预报障碍(SPB)问题。可见,这种结合物理过程和大数据的混合建模方法可通过强化有效信息而削减无关噪音能有效提升ENSO的预测水平。