基于改进残差网络的气温预报技术在湖南的应用OA北大核心

This study introduces a residual spatio-temporal stacking(Res-STS)model designed to improve tem-perature forecasting in Hunan Province,China-a region characterized by complex terrain,with mountainous areas to the east,west,and south,and the Dongting Lake Plain to the north.This diverse topography,influenced by elevation gradients,vegetation cover,cold air pooling,and lake effects,results in spatially heterogeneous and tem-poral dynamic temperature patterns. Although deep learning models such as ResNets have demonstrated success in precipitation forecasting and severe weather recognition,their application to temperature forecasting remains limited-particularly regarding the integration of multi-scale physical variables from numerical models.To address this gap,the Res-STS model in-tegrates both surface and upper-air variables from the European Centre for Medium-Range Weather Forecasts In-tegrated Forecasting System(ECMWF-IFS),along with observational data,thereby enhancing the model's ability to capture spatiotemporal dependencies. The Res-STS architecture adapts the ResNet with residual connections to mitigate gradient vanishing and ex-plosion,preserving shallow-layer features.Unlike conventional sequential temporal models,Res-STS employs a spatiotemporal stacking approach to jointly learn background environmental fields and temporal evolution patterns.Spatially,a"field-to-point"framework is adopted:a 250 km × 250 km region centered on each forecast grid point-corresponding approximately to meso-beta-scale systems-is used as input.This design balances computa-tional efficiency and the retention of large-scale atmospheric information,avoiding the limitations of point-to-point oversimplification and field-to-field data scarcity.Temporally,consecutive 3-hourly ECMWF-IFS forecast fields are stacked to predict hourly temperatures in sequential time windows(e.g.,forecasts at T0 and T1 are used to predict hours 1-3;T1 and T2 for hours 4-6,and so on),enabling the generation of continuous 24-hour fore-casts. Evaluation results show that the Res-STS model outperforms benchmark models across all tested metrics.Compared with ECMWF-IFS and guidance from the National Meteorological Centre,Res-STS achieves mean ab-solute errors(MAEs)of 1.21 ℃ for hourly forecasts,1.38℃ for daily maximum temperatures,and 1.07 ℃ for daily minimum temperatures-representing reductions of 23.8％and 15.2％in MAEs for maximum and minimum temperatures,respectively.In high-altitude areas above 800 meters,the model's median error(1.12℃)is 31％lower than that of ECMWF-IFS.During extreme events,such as cold waves and heatwaves,Res-STS also outperforms manual corrections and objective forecasts,achieving 2 ℃ accuracy rates of 85.81％for mini-mum temperatures and 97.88％for maximum temperatures. Nonetheless,the model's reliance on ECMWF-IFS input fields constrains its performance under systematic biases-such as errors in cloud cover estimation-which can increase MAEs by 0.8-2.3 ℃ during persistent synoptic anomalies.Additionally,the 0.05° resolution of the China Meteorological Administration Land Data As-similation System(CLDAS)dataset may smooth terrain transitions in narrow valleys,contributing to residual af-ternoon temperature discrepancies of up to 1.8 ℃.Current computational limitations restrict the model's opera-tional use to Hunan Province.Future research is needed to reduce reliance on numerical model inputs,incorporate higher-resolution terrain data,and optimize computational performance for broader deployment and improved forecast accuracy during extreme cooling events. This study advances the integration of deep learning with numerical weather prediction and offers a novel post-processing framework for temperature forecasting in regions with complex topography.