Gridded Temperature Forecast Model in Hunan Based on Improved Long Short-Term Memory Networks
Based on forecast products of the European Centre for Medium-Range Weather Forecasts-Integrated Forecasting System(ECMWF-IFS)and hourly temperature observation data from the CMA Land Data Assimilation System(CLDAS),an enhanced model named ED-LSTM-FCNN is constructed,with an embedding layer module incorporated to handle high-dimensional spatial and temporal features.A fully connected neural network is utilized to integrate various feature types,achieve regression prediction of temperature,and generate gridded hourly temperature forecast products with a resolution of 0.05°× 0.05°.Verification for the forecast products in Hunan Province in 2022 shows that this model exhibits a notable capacity to mitigate forecast errors inherent in the numerical model,and can enhance the overall fore-cast stability.The root mean square errors(RMSEs)of forecasts with lead time ranging from 1 to 24 hours exhibit a reduction of 25.4％-37.7％when compared to ECMWF-IFS and a decrease of 15.8％-40.0％relative to the National Meteorological Centre forecast(SCMOC).The model can significantly im-prove the forecast performance of ECMWF-IFS forecast,in spatial scale,particularly in regions character-ized by intricate terrain.The RMSEs across most areas vary within the range of 1.2-1.6℃.The forecast accuracy of the model,with an error margin of±2℃,surpasses 83.0％across various seasons,demon-strating a significant improvement compared to both ECMWF-IFS and SCMOC.The forecasting perform-ance is notably superior,particularly in stable extreme high-temperature weather conditions compared to alternative products.In conclusion,this model has proved to be effective in the high-resolution tempera-ture forecasting operations.
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