气象2026，Vol.52Issue(5)：538-551,14.DOI:10.7519/j.issn.1000-0526.2026.030903
盘古预报产品融合物理约束深度学习温度预报方法
Temperature Forecasting Method of Physics-Constrained Deep Learning Integrated with Pangu-Weather Model Forecast Products
摘要
Abstract
Aiming at the fine-scale forecasting challenge of 2 m temperature(T2m)in complex terrain areas,this paper selects the Guangxi Region,a typical area with complex terrain,as the research object and pro-poses a physics-constrained deep learning forecasting model named PSD-Net,which integrates the forecast products of the Pangu-Weather Model.The forecast products of Pangu-Weather(PANGU)are used as feature variables input.The generator based on the super-resolution generative adversarial network is em-ployed to extract multi-scale features.Power-spectral-density and Kullback-Leibler divergence are explicit-ly injected into the loss function as constraint terms so as to improve the consistency of forecast products with observations in spectral fidelity and probability distribution.Compared to the T2m forecast perform-ance of the ECMWF,SCMOC and PANGU products in the Guangxi Region in 2024,both the grided fore-casts and station-based forecasts of PSD-Net outperform the compared forecast products.In particular,the mean absolute error(MAE)of the gridded forecasts is reduced by 37.6％relative to that of PANGU and the accuracy is improved by 17 percentage points.The growths of both MAE and root mean square error(RMSE)for the 1-72 h T2m forecast products from PSD-Net are less than those of the compared forecast products,and there is a gentle error growth at lead time 25-72 h.In a word,this study has verified the effectiveness of the physics-constrained deep learning framework in fine-scale T2m forecasting which could provide a new approach for the combination of meteorological and AI models.关键词
2 m气温/AI/物理约束/预报模型/检验评估Key words
2 m temperature/AI/physics-constrained/forecast model/performance evaluation分类
天文与地球科学引用本文复制引用
蒋健,李明志,李超,黄开刚,龙柯吉..盘古预报产品融合物理约束深度学习温度预报方法[J].气象,2026,52(5):538-551,14.基金项目
广西壮族自治区气象局气象科研计划项目(桂气科2024M20)、湖北省自然科学基金项目(2023AFD101)、高原与盆地暴雨旱涝灾害四川省重点实验室科技发展基金项目研究型业务重点专项(SCQXKJYJXZD202402)、中国气象局航空气象重点开放实验室青年课题(HKQXQ-2025007)共同资助 （桂气科2024M20）
