Using convolutional neural network to improve the effect of short-term weather temperature prediction
In order to improve the accuracy of fine temperature forecast in Tianjin,based on ECMWF-IFS,CMA-GFS model data of China Meteorological Administration and hourly temperature data from 259 regional automatic stations in Tianjin,a 3D convolutional neural network based on U-Net code was proposed to model hourly temperature.Many hyper-parameters were adjusted by dichotomous search method,and the optimal model was obtained by 148 groups of experimental training,and the test set error was 1.226 ℃.Results show that the prediction error of the model is lower than that of the original numerical model,especially for the central and southern Tianjin(including the central urban area)and the eastern coastal area.The prediction characteristics of diurnal temperature variation are closer to the actual temperature,which can effectively improve the prediction error of the original numerical model,and the model shows stronger error stability.
fine temperature forecastconvolutional neural networkprediction characteristics of diurnal variationcorrection effect
万方数据
维普
