Design Of Multi-mode Integrated Weather Forecast Syste

In recent years,global climate change has accelerated,leading to frequent meteorological disasters and causing significant losses to human lives and property.Accurate meteorological forecasting has become an essential means of disaster prevention and mitigation.Traditional single numerical model forecasts have low accuracy and limited applicability,making it difficult to provide reliable and effective data support for monitoring and early warning of meteorological disasters.Therefore,this paper proposes a multi-model ensemble forecasting method based on improved random forest.By integrating the advantages of multiple single models,a comprehensive forecast result is formed,and a multi-model ensemble meteorological forecasting system is designed and realized based on the ensemble forecast data.The main research achievements are as follows:(1)Aiming at the problem of low forecasting accuracy due to systematic deviation and uncertainty of a single numerical model,this paper proposes an improved random forest multimodel ensemble forecasting method based on Bayesian combined with grid search,using the TIGGE data set multiple models and the forecast data of the WRF automatic forecast platform are used for ensemble forecasting of 2m air temperature,10 m zonal wind and 10 m meridional wind.The improved random forest(BGS-RF)was compared with other ensemble forecasting methods and the optimal single model in the experiment to compare the forecasting results of different elements under different forecasting timeliness.The experimental results prove that the forecasting effect of the algorithm proposed in this paper is obviously better than other methods and single mode.(2)Aiming at the problems of small scope of application and limited application scenarios of traditional numerical model forecast data,this paper designs a multi-mode ensemble meteorological forecast system based on ensemble forecast data,which realizes user login,WRF automatic forecast,multi-mode ensemble forecast,data visualization,Functions such as parameter setting,downscaling,and data transfer.Designed a distributed data storage solution based on Elasticsearch and Minio,and introduced Nginx reverse proxy and load balancing technology to further optimize the system’s software architecture and improve the system’s data processing capabilities and response speed.(3)In order to verify whether the system meets the functional and non-functional requirements,the system is deployed and tested,covering all functional modules and business processes of the system.The test results show that the multi-model ensemble meteorological forecast system designed in this paper has achieved the expected effect,making the application of meteorological forecast data more multi-dimensional and diversified,and can meet various application requirements.