福建师范大学学报（自然科学版）2026，Vol.42Issue(2)：11-21,11.DOI:10.12046/j.issn.1000-5277.2025030045
基于EMA-UNet模型的次季节温度预报校正
Subseasonal Temperature Forecast Bias Correction Based on the EMA-UNet Model
摘要
Abstract
To address the prediction bias in subseasonal temperature forecasting resulting from the limited capacity of traditional models and existing deep learning methods to fully capture multiva-riate spatiotemporal dependencies,an ensemble neural network model based on an improved U-Net architecture EMA-UNet)is proposed.By incorporating a multi-level residual structure,a temporal embedding module,and a cross-channel spatiotemporal attention mechanism,the model overcomes the limitations of traditional architectures and achieves an accurate characterization of dynamic tem-perature evolution.Experimental results show that the EMA-UNet consistently outperforms the tradi-tional physical model EC)and mainstream deep learning models,significantly reducing prediction errors at the subseasonal scale and demonstrating enhanced robustness during high-temperature peri-ods in summer.Ablation studies further validate the effectiveness of the core modules in optimizing multivariate modeling.关键词
次季节气候预测/气温预测/数值天气预报后处理/深度U-NetKey words
subseasonal climate forecasting/temperature prediction/numerical weather pre-diction post-processing/deep U-Net分类
信息技术与安全科学引用本文复制引用
张鉴,张煜杰,翁彬,饶嘉蔚,叶晓炜,李爽..基于EMA-UNet模型的次季节温度预报校正[J].福建师范大学学报（自然科学版）,2026,42(2):11-21,11.基金项目
福建省科技厅引导性项目(2022Y0008、2021Y0057) （2022Y0008、2021Y0057）
