沙漠与绿洲气象2026，Vol.20Issue(1)：91-99,9.DOI:10.12057/j.issn.2097-6801.2410.17004
黑龙江省分类强对流环境条件分析及客观预报方法
Analysis of Environmental Characteristics and Objective Forecasting Methods for Classified Severe Convective Weather in Heilongjiang Province
摘要
Abstract
Based on the observation data,European center reanalysis data(ERA5),mesoscale numerical model data(CMA-MESO),random forest algorithm was utilized to construct the classified severe convective forecasting model.The study analyzed the differences of physical characteristic such as thermal,vapor,and dynamic factor between short-term heavy rainfall and thunderstorm gale in Heilongjiang Province.The physical characteristics was selected as the negative index by using the factor contribution ranking of the model output.The forecast products were verified from June to September 2023.The results indicate that the thermal factor is more important and has good indicative significance for distinguishing the potential of severe convections.Short-term heavy rainfall is more likely to occur in environments with good vapor conditions,warm and humid entire layers,high unstable energy,and uplift effects in the middle and lower layers.Thunderstorm gale is more likely to occur in environments with relatively dry,high unstable energy,large temperature decline rate,and strong vertical wind shear.The classified forecasting products based on model data have a certain forecasting ability to severe convections types and large-scale falling areas in Heilongjiang Province.Short-term heavy rainfall forecast product is more effective than thunderstorm gale forecast product.The rate of false alarms and miss cases is high in certain areas.The accurate forecasts are provided for 4 hours in advance.关键词
短时强降水/雷暴大风/对流参数/随机森林Key words
short-term heavy rainfall/thunderstorm gale/convective index/random forest分类
天文与地球科学引用本文复制引用
高梦竹,刘松涛,王芳,李吉,卜文惠,王承伟,陈雪..黑龙江省分类强对流环境条件分析及客观预报方法[J].沙漠与绿洲气象,2026,20(1):91-99,9.基金项目
中国气象局创新发展专项(CXFZ2023J011) （CXFZ2023J011）
中国气象局复盘专项(FPZJ2024-038) （FPZJ2024-038）
中国气象局沈阳大气环境研究所和东北冷涡重点开放实验室联合开放基金(2023SYIAEKFMS11) （2023SYIAEKFMS11）
