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利用中尺度气象模式WRF的双参数显示云物理方案,开展冬季冷性层状云降水过程的数值模拟和人工增雨催化数值试验.模拟个例为2013年3月19日北京地区的一次典型降水过程,在分析模拟得到的云中水成物和上升速度分布的基础上设计不同催化试验,研究不同催化时刻(云体发展期、 云体成熟期)和三种催化剂量对地面降水、云中水成物浓度、动力场和热力场以及微物理转化过程的影响.模拟试验结果表明:模拟的自然降水分布和实测结果较为一致;不同的催化试验都可以使地面雨量增加,在云体发展期以107个?kg-1剂量进行催化的效果最佳;引入人工冰晶后催化区域水汽和过冷云水含量明显减少、冰晶和雪的含量有所增加、催化区域上升气流明显增强,温度提高;催化后40 min时雪的增长主要依靠其凝华增长、冰晶向雪的自动转化、雪和云滴之间的碰冻以及冰晶和雪之间的碰并;催化后200 min,催化云中各种微物理过程对雪的贡献高于自然云,催化前期消耗了过冷云水,此时云中雪和云滴之间的碰冻对雪的贡献非常微弱,雪的增长主要依靠凝华增长以及雪和冰晶的相互作用.“,”In recent years, the drought situation in Beijing area is very serious, especially in the winter. How to effectively utilize the water resources in the air and carry out rain enhancement operation is important. Rain en-hancement operation in winter is also likely to be used to remove fog and haze, and furthermore to improve air quality and visibility. Cloud models have been used in weather modification to formulate cloud-seeding hypothe-ses, assessments of the cloud-seeding potential or “seed ability”. In this study, the numerical simulations of a snowfall case was carried out by using the two-moment explicit cloud scheme ( nssl) of WRF model. The nssl scheme can predict the particle number concentration and mixing ratio of cloud water, rain, ice, snow, graupel and hail. For the snowfall case in Beijing on 19 March 2013, several seeding tests were designed to study the effects of different seeding time and seeding rate on cloud processes and precipitation amounts. The experimental results showed that all seeding tests could increase rainfall amount. When seeding with an amount of 1×107 kg-1 in cloud developing period, the rain enhancement effect was the best. The water vapor and supercooled cloud wa-ter mixing ratio in seeding area decreased after seeding, meanwhile the mixing ratios of ice and snow increased. The updraft and temperature of this area also increased. In 40 min after seeding, snow mixing ratios mainly grew through microphysical processes of deposition, the automatic conversion of ice to snow, snow accretion of cloud droplets, the collision between ice and snow. In 200 min after seeding, microphysical conversion processes of snow sources in the catalytic clouds were all higher than the natural clouds. Because of the consumption of super-cooled cloud water in seeding clouds, the accretion process between cloud droplets and snow was very small. The snow mixing ratio mainly increased through terms of snow deposition and the interaction between snow and ice. The research results are helpful to understand the seeding effects on the macro and micro-processes of winter stratiform clouds, which will provide the basis for the rainfall enhancement field operations.