Research On Multi-mode Temperature Forecast Algorithm Based On Ensemble Learning And U-ConvLSTM Networ

Meteorology is closely related to human life,affecting various aspects of human life.The accuracy of weather forecasting has important implications for military,civilian,and economic fields.However,traditional numerical models suffer from systematic errors due to parameterization and initial field problems,which affect the accuracy of numerical forecasting.Therefore,error correction based on numerical model data is an important research direction in current weather forecasting.To address the above issues,this paper proposes a temperature forecast correction algorithm based on the U-Conv LSTM network,which aims to improve the accuracy of numerical model forecasts.Specifically,the Conv LSTM network is introduced and the Context Embedding block is designed to extract the temporal features of the data and capture longerterm contextual correlations.Secondly,the Conv LSTM and U-net are combined through skip connections to extract deep spatial features and shallow spatial features of the data.At the same time,an attention mechanism is introduced to enhance the model’s ability to extract detailed features in both the spatial and channel dimensions.Experimental results show that compared to other correction algorithms,the temperature forecast correction algorithm based on UConv LSTM network has better forecast correction performance and good spatial and temporal feature extraction capabilities.To further improve the accuracy of error correction and address the issue of differences between different numerical models,this paper proposes a multi-model ensemble forecast error correction model based on U-Conv LSTM and gradient boosting decision tree algorithm.The ensemble learning approach is adopted to integrate the advantages of each individual model using the gradient boosting decision tree algorithm to achieve better forecast correction performance.Experimental results show that compared with single-model models,the ensemble model can further improve the accuracy of forecast correction.