To investigate the mechanism of wire icing growth and simulate the icing growth rate,this study analyzed two cases of wire icing observed in Lushan(Jiangxi Province,South China)during winter 2016 under conditions of c...To investigate the mechanism of wire icing growth and simulate the icing growth rate,this study analyzed two cases of wire icing observed in Lushan(Jiangxi Province,South China)during winter 2016 under conditions of coexisting freezing rain and supercooled fog.By combining meteorological elements with the microphysical parameters of coexisting freezing rain and supercooled fog,the correlation between the icing growth rate and each of these factors was examined.To simulate the icing growth process,this study adopted four widely used machine learning models:random forest(RF),support vector machine(SVM),convolutional neural network(CNN),and extreme learning machine(ELM)models.For both studied cases,the results indicated that the rain rate,temperature,wind speed(≥1 m s^(-1)),and wind direction exhibited statistically significant positive correlation with the icing growth rate.In the stronger wind scenario(Case 1),the wire icing growth rate was negatively correlated with the number concentration,liquid water content,mean diameter,and mean volumetric diameter of the precipitation particles,with wind speed being the most important factor in the RF model.The icing growth was driven primarily by the increased rain rate,which subsequently led to higher liquid water content.In the weaker wind scenario(Case 2),the wire icing growth rate exhibited positive correlation with the number concentration,liquid water content,and mean diameter of the precipitation particles,with the mean volumetric diameter of supercooled fog droplets being the most important factor in the RF model.The icing growth was attributable primarily to increase in the number of water-phase particles and overall increase in particle size,leading to increase in liquid water content.All four machine learning models successfully simulated the icing growth process,yielding results that outperformed those derived from traditional empirical formulas and numerical simulations,with the RF,SVM,and CNN models demonstrating particularly strong performance.展开更多
基金Supported by the National Natural Science Foundation of China(42075063 and 42075066)Jiangsu Graduate Scientific Research Innovation Project(KYCX23_1316)+2 种基金China Scholarship Council(CSC)(202309040027)Meteorological Observation Centre of the China Meteorological Administration Field Experimental Project in 2024(GCSYJH24-30)Project of China Meteorological Administration Training Centre(2025CMATCPY08)。
文摘To investigate the mechanism of wire icing growth and simulate the icing growth rate,this study analyzed two cases of wire icing observed in Lushan(Jiangxi Province,South China)during winter 2016 under conditions of coexisting freezing rain and supercooled fog.By combining meteorological elements with the microphysical parameters of coexisting freezing rain and supercooled fog,the correlation between the icing growth rate and each of these factors was examined.To simulate the icing growth process,this study adopted four widely used machine learning models:random forest(RF),support vector machine(SVM),convolutional neural network(CNN),and extreme learning machine(ELM)models.For both studied cases,the results indicated that the rain rate,temperature,wind speed(≥1 m s^(-1)),and wind direction exhibited statistically significant positive correlation with the icing growth rate.In the stronger wind scenario(Case 1),the wire icing growth rate was negatively correlated with the number concentration,liquid water content,mean diameter,and mean volumetric diameter of the precipitation particles,with wind speed being the most important factor in the RF model.The icing growth was driven primarily by the increased rain rate,which subsequently led to higher liquid water content.In the weaker wind scenario(Case 2),the wire icing growth rate exhibited positive correlation with the number concentration,liquid water content,and mean diameter of the precipitation particles,with the mean volumetric diameter of supercooled fog droplets being the most important factor in the RF model.The icing growth was attributable primarily to increase in the number of water-phase particles and overall increase in particle size,leading to increase in liquid water content.All four machine learning models successfully simulated the icing growth process,yielding results that outperformed those derived from traditional empirical formulas and numerical simulations,with the RF,SVM,and CNN models demonstrating particularly strong performance.
