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A Novel Fusion Forecast Model for hail Weather in Plateau Areas Based on Machine Learning

Journal of Meteorological Research 2021年第5期35卷 896-910页

作者： Yan ZHANG Zhong JI Bing XUE Ping WANG School of Electrical and Information Engineering Tianjin UniversityTianjin 300072 Public Meteorological Service Center China Meteorological AdministrationBeijing 100081

In order to improve the accuracy of hail forecasting for mountainous and plateau areas in China,this study presents a novel fusion forecast model based on machine learning ***,known mechanisms of hail formation and two newly proposed elevation features calculated from radar data,sounding data,automatic station data,and terrain data,are firstly combined,from which a hail/short-duration heavy rainfall(SDHR)classification model based on the random forest(RF)algorithm is built ***,we construct a hail/SDHR probability identification(PI)model based on the Bayesian minimum error decision and principal component analysis ***,an"and"fusion strategy for coupling the RF and PI models is *** addition to the mechanism features,the new elevation features improve the models’performance *** results show that the fusion strategy is particularly notable for reducing the number of false alarms on the premise of ensuring the hit rate.A comparison with two classical hail indexes shows that our proposed algorithm has a higher forecasting accuracy for hail in mountainous and plateau *** 19 hail cases used for testing could be identified,and our algorithm is able to provide an early warning for 89.5%(17 cases)of hail cases,among which 52.6%(10 cases)receive an early warning of more than 42 minutes in *** PI model sheds new light on using Bayesian classification approaches for highdimensional solutions.

关键词： hail identification hail warning random forest Bayesian minimum error decision fusion strategy

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