吉林大学学报（理学版）2026，Vol.64Issue(3)：617-626,10.DOI:10.13413/j.cnki.jdxblxb.2024530
基于领域先验知识的时空神经网络模型在MJO预报中的应用
Application of Domain Prior Knowledge-Based Spatio-temporal Neural Network Model in MJO Forecasting
摘要
Abstract
Aiming at the problem that current artificial neural network methods could not accurately forecast the Madden-Julian oscillation(MJO),a seasonal climate phenomenon,we proposed a domain prior knowledge-based spatio-temporal neural network model.Firstly,we integrated domain prior knowledge into data preprocessing according to the characteristics of climate circulation data.Secondly,a pretraining-finetuning architecture was adopted,model data from the subseasonal-to-seasonal scale were used for pretraining model,and we completed finetuning by using the ERA5 reanalysis data.Finally,through spatio-temporal modeling,a framework combining convolutional neural networks and long short-term memory networks was selected to embed the prior knowledge into the pretraining process and optimize the forecasting.Experimental results show that the proposed model can achieve accurate MJO forecasting for 23 d,and its performance is superior to other artificial neural network methods and domestic numerical forecasting methods.关键词
领域先验知识/时空神经网络/Madden-Julian振荡预报/预训练模型Key words
domain prior knowledge/spatio-temporal neural network/Madden-Julian oscillation forecasting/pretraining model分类
信息技术与安全科学引用本文复制引用
张弄,徐哲文,刘长征..基于领域先验知识的时空神经网络模型在MJO预报中的应用[J].吉林大学学报（理学版）,2026,64(3):617-626,10.基金项目
吉林省自然科学基金面上项目(批准号:20230101062JC)和国家自然科学基金(批准号:42175052). （批准号:20230101062JC）
