Macro And Micro Meteorological Data Forecast And Research Based On Deep Learnin

Meteorological changes are closely related to people’s lives,and the accuracy of weather forecasting has received increasing attention from people.Meteorological data is the basis for developing meteorological business and conducting meteorological scientific research,and it is also an important basic information resource for the country.With the continuous improvement of comprehensive meteorological observation technology,the acquisition channels of meteorological data have become more diversified,and the amount of data has greatly increased compared to before.However,with the large increase in meteorological data,traditional meteorological data forecasting methods have problems such as low efficiency and low prediction accuracy.Against this background,this article uses an improved deep learning model to implement a meteorological forecasting method,focusing on the El Ni(?)o index of macro meteorological data and the radar echo value of micro meteorological data for meteorological forecasting research.In the research of El Ni(?)o index prediction based on macro sea surface temperature data,data processing was performed on the dataset using methods such as filling missing values,normalization,and sliding windows.The El Ni(?)o index prediction task can actually be decomposed into two parts: sea surface temperature prediction and calculation of the El Ni(?)o index from the sea surface temperature.Therefore,for the sea surface temperature prediction part,the MIM differential recurrent neural network was used.By analyzing the MIM model,a differential recurrent neural network enhanced with attention mechanism was proposed.After comparing the feasibility of various attention mechanism enhancements,the NA attention module with better performance was finally selected.Based on this,the NA-MIM model was proposed.Through empirical analysis,it was confirmed that this model is feasible and accurate for predicting the El Ni(?)o index.In the research of near-term weather forecasting based on micro-radar reflectivity data,for high-resolution radar echo data sets,normalization and removal of low-value artifacts are used to reduce the impact of noise data on model fitting performance.Since radar echo extrapolation tasks are also spatiotemporal sequence prediction tasks,the model is based on the NA-MIM model.The prediction of nearby frames of radar echoes is decomposed into initial frames and inter-frame differences.By analyzing the correlation between the two data items and future frames,the U-shaped structure of the U-Net model is used to improve the NA-MIM model,which is used to reconstruct future frames and save computing resources.According to the experimental results,compared with other radar echo extrapolation models,this model has better high-resolution forecasting performance.In summary,we have achieved good results in both El Ni(?)o index forecasting and near-term weather forecasting through data processing and model optimization.For El Ni(?)o index forecasting,we proposed a differential recurrent neural network model enhanced with attention mechanisms.For near-term weather forecasting,we improved the model using the U-Net architecture to reconstruct radar echo images of future frames.The experiments confirmed the feasibility and accuracy of these models in their respective meteorological forecasting tasks.These research achievements are of great significance in improving the efficiency and accuracy of meteorological forecasting.