Daily Maximum and Minimum Temperature Forecasts Correction Based on Deep Learning Model Ensemble

LU Shu; GUO Kemeng; ZHOU Yue; FU Chenghao; XU Lin; GU Xue

doi:10.16032/j.issn.1004-4965.2024.090

Abstract：Using temperature data from the China Meteorological Administration(CMA)Land Data Assimilation System and high-resolution forecast products from the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System(ECMWF-IFS)during 2018-2023,we developed two deep learning frameworks:a residual spatiotemporal stacking network(Res-STS)and a self-attention long short-term memory network(Attention-LSTM).An ensemble model was subsequently developed from these two models to produce 0.05 °×0.05 ° gridded temperature forecasts specific to the Hunan region.Validation results for the year of 2023 indicate that the deep learning models effectively improved the accuracy of ECMWF-IFS forecasts.For the 0～24 h forecasts,the mean absolute error(MAE)of daily maximum temperature was reduced by 25.76%～40.40%compared to the ECMWF-IFS products and by 15.03%～31.79%compared to the products from the System of Central Meteorological Observatory for Correction(SCMOC),CMA.The MAE of daily minimum temperature was reduced by 10.53%～31.58%compared to the ECMWF-IFS products and by 5.31%～19.47%compared to products from the SCMOC,with the ensemble model performing the best.Furthermore,the ensemble model effectively mitigated the limitation of the ECMWF-IFS in forecasting within complex terrain.The proportion of areas achieving an F2 score of 85%for daily maximum temperature was 17.31%,mainly in the Dongting Lake plain area,whereas it was below 6%in other models.For daily minimum temperature,the area with an F2 score of 90%reached 68.63%,which was 21.08%～63.09%higher than those of other models.Overall,the ensemble model exhibited superior forecasting performance in most months.The integration of multiple models and deep learning can significantly enhance the reliability and accuracy of temperature forecasts.