Analysis of the Current Status of Meteorological Data Quality Control Methods and Reflections
In recent years,global warming has led to frequent occurrence of extreme weather,which has a great impact on various fields.The accuracy and consistency of meteorological data have become the focus of attention,and inaccurate data may affect public decision-making and production activities.Traditional quality control strategies include climatological boundary value check,temporal continuity test,internal consistency test and spatial continuity test.With the advent of the big data era,advanced QC strategies based on data mining have been widely studied,such as machine learning,clustering algorithms,regression models and convolutional neural networks.Currently,techniques such as deep neural networks and convolutional neural networks have been successfully applied by the European Center for Medium-Range Weather Forecasting and the U.S.Weather Company,among others.However,user-oriented QC programs are still deficient and need to further improve user experience and service strategies.Therefore,in-depth research on user feedback and improving the quality of weather services are key to enhancing branded weather services.This research framework provides useful guidance for deepening the field of weather data QC.
国家科技期刊平台
NSTL
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