摘要
ENSO(El Nino/Southern Oscillation)是发生在赤道东太平洋海域重要的海气耦合现象,在全球气候变化中起着重要作用。当热带太平洋东部的海表温度(Sea Surface Temperature,SST)出现连续5个月以上的异常升温/降温现象,就会产生厄尔尼诺现象/拉尼娜现象。因此,研究和预测这一区域的SST动态具有重要的科学意义。本文采用输入层附加Attention机制的长短时记忆(Long Short-Term Memory,LSTM)神经网络(Attention-LSTM)模型,对多时段、多站位的热带太平洋观测浮标站位获取的厄尔尼诺年和拉尼娜年的SST数据进行一年的预测。研究发现:实验站位SST预测中,LSTM算法的均方误差在0.5℃左右,而Attention-LSTM算法的均方误差均不超过0.31℃,证明了Attention-LSTM算法的预测精度高于传统的LSTM模型;在发生ENSO现象年份的东太平洋海域不同站,Attention-LSTM算法对SST的春季预报障碍(Spring Predictability Barrier,SPB)现象也有一定的精度改善作用。
ENSO(El Nino/Southern Oscillation)is an important air-sea coupling phenomenon that occurs in the equatorial centraleastern Pacific Ocean,and plays an important role in the global climate system.El Niño/La Niña occurs when the SST(Sea Surface Temperature)in the Eastern Tropical Pacific is warm/cool abnormally for more than 5 consecutive months.Therefore,it is of great scientific value to study and predict the SST dynamics in this region.In this paper,we use the Attention-LSTM model that introduces the Attention mechanism into the input layer of LSTM(Long Short-Term Memory)neural network to make one-year predictions of SST data obtained from multi-time and multiple Tropical Pacific observation buoy stations in El Niño and La Niña years based on exploring the effect of training sample length on the prediction results.In the SST prediction of the experimental site,the mean square error of the LSTM algorithm is about 0.5℃,while the mean square error of the Attention-LSTM algorithm is less than 0.31℃,which proves that the prediction accuracy of the Attention-LSTM algorithm is higher than that of the traditional LSTM model;At different stations in the Eastern Pacific Ocean in the year of ENSO,the Attention-LSTM algorithm also has a certain accuracy improvement on the Spring Predictability Barrier(SPB)phenomenon of SST.
作者
邱钰
丁军航
徐腾飞
官晟
QIU Yu;DING Junhang;XU Tengfei;GUAN Sheng(School of Automation,Qingdao University,Qingdao 266071,China;First Institute of Oceanography,MNR,Qingdao 266061,China;Shandong Key Laboratory of Industrial Control Technology,Qingdao 266071,China;Key Laboratory of Marine Science and Numerical Modeling,MNR,Qingdao 266061,China;Shandong Key Laboratory of Marine Science and Numerical Modeling,Qingdao 266061,China;Laboratory for Regional Oceanography and Numerical Modeling,Pilot National Laboratory for Marine Science and Technology(Qingdao),Qingdao 266237,China)
出处
《海洋科学进展》
CAS
CSCD
北大核心
2023年第2期207-219,共13页
Advances in Marine Science
基金
国家重点研发计划项目(2021YFC3101105)。
