大气科学学报2025，Vol.48Issue(3)：438-448,11.DOI:10.13878/j.cnki.dqkxxb.20241231002
深度学习在湖南次季节气温预测业务中的应用
Application of deep learning for subseasonal air temperature prediction in Hunan Province
摘要
Abstract
Subseasonal temperature anomaly prediction is a critical component of short-term climate forecasting in China.Accurate forecasts are essential for effective response to meteorological hazards such as heatwaves and cold surges.In recent years,artificial intelligence(AI)has been increasingly applied in climate prediction,enab-ling the detection of predictable signals related to extreme climate events and the development of statistical predic-tion models.However,many existing approaches underutilize valuable information from dynamical models.To address this limitation,this study proposes a deep learning framework that integrates dynamical model outputs to improve subseasonal temperature anomaly prediction.Using daily atmospheric circulation data from the NCEP/NCAR reanalysis,outgoing longwave radiation(OLR)data from NOAA,and daily temperature records from 97 national weather stations in Hunan Province for the period 1981-2023,we develop a pre-trained convolutional neural network(CNN)model to forecast 30-day temperature anomalies with 1-to 10-day lead times.The model is further fine-tuned using outputs from two sub-seasonal to seasonal(S2S)dynamic models-NCEP-CFSv2 and CMA-CPSv3-incorporating atmospheric circulation and OLR predictors.The results demonstrate that:1)The CNN model achieves spatial anomaly correlation coefficient(ACC)exceeding 0.35 for 30-day temperature a-nomaly forecasts at lead times of 1 to 10 days,substantially outperforming both S2S models,which exhibit ACCs below 0.2.In addition to ACC,the CNN model consistently outperforms the dynamical models in terms of temporal correlation coefficient(TCC),anomaly sign consistency(AS),and root mean square error(RMSE).2)Seasonal evaluation shows that the CNN model maintains superior ACC skill across all months.The AS skill is particularly higher during autumn and winter,while predictive performance declines during the winter-spring transition,early summer,and the Meiyu season.Spatially,the CNN model underperforms NCEP-CFSv2 in east-ern and northwestern Hunan but performs better in other regions.At a 10-day lead time,the CNN model achieves higher TCC scores than CMA-CPSv3 at all stations across Hunan.3)Interpretability analysis indicates that the CNN model places strong emphasis on predictors from the tropical Indian Ocean,suggesting that this region may serve as a key source of predictability.Conversely,mid-and high-latitude atmospheric circulation-which is known to influence subseasonal temperature variation in Hunan-is underutilized by the CNN model.This may partially explain reduced model performance in spring and summer.Future work should focus on enhancing model performance and robustness by incorporating additional predictors,utilizing higher-resolution reanalysis and model data,and employing more advanced deep learning architectures.关键词
气温预测/深度学习/迁移学习Key words
temperature prediction/deep learning/transfer learning引用本文复制引用
黄超,李巧萍,谭楚岩..深度学习在湖南次季节气温预测业务中的应用[J].大气科学学报,2025,48(3):438-448,11.基金项目
湖南省自然科学基金部门(行业)联合基金项目(2025JJ80280) （行业）
广东省基础与应用基础研究重大项目(2020B0301030004) （2020B0301030004）
湖南省气象局创新发展专项项目(CXFZ2024-ZDZX04) （CXFZ2024-ZDZX04）
国家自然科学基金项目(42341209) （42341209）
