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丹江口水库汇水区秋季积层混合云催化增雨的数值模拟研究

Numerical Simulation of Precipitation Enhancement by Seeding Stratiform Clouds with Embedded Convection in the Danjiangkou Reservoir Catchment During Autumn

  • 摘要: 积层混合云是人工增雨的重要对象,丹江口水库作为“南水北调”的重要水源地,针对该地区积层混合云降水云系的人工增雨研究显得愈发重要。在中尺度WRF(Weather Research and Forecast)模式双参数云物理方案基础上,采用显式方法加入碘化银粒子的成核机制,引入碘化银比含水量和数浓度预报方程,建立起一个中尺度WRF人工催化模式。使用建立的WRF人工催化模式,对2023年9月25日秋季一次积层混合云开展了面状播撒(EXP1试验)、线状播撒(EXP2试验)和单点播撒(EXP3试验)3组催化模拟试验。结果表明,播撒后碘化银在气流的作用下向下风方输送,碘化银的扩散范围和数量EXP1试验最大,EXP2试验次之,EXP3试验最小。碘化银核化后通过消耗过冷水使云中冰晶大量增加,使得冰晶—雪、雪—霰的转换过程增强,雪、霰粒子含量增加,雪和霰的融化作用导致地面降水增加。EXP1试验主要通过雪的融化增加地面降水,EXP2试验主要通过霰的融化增加地面降水,EXP3试验雪或霰的增加范围最小。催化后在播撒区下风方40~90 km出现降水增加,EXP1试验的地面增雨量、增雨率和增雨范围最大。区域平均增雨雨强最大出现在播撒结束后1.5 h,数值为EXP1试验的0.047 mm h−1。EXP1试验的增雨效果最好,EXP2试验的增雨效果次之,EXP3的增雨效果最弱,表明催化作业方式对作业效果有较大影响。研究结果可为人工影响天气外场作业提供科学指导。

     

    Abstract: Stratiform clouds with embedded convection are important targets for artificial precipitation enhancement. Danjiangkou Reservoir serves as a key water source for the “South-to-North Water Diversion” project, research on artificial enhancement of precipitation cloud systems in this region has gained increasing importance. Based on the bulk two-moment microphysical scheme of the mesoscale Weather Research and Forecasting (WRF) model, an explicit method was employed to incorporate the nucleation mechanism of silver iodide particles, introducing prognostic equations for the silver iodide mixing ratio and number concentration, thereby establishing a mesoscale WRF artificial seeding model. Using this WRF artificial seeding model, three seeding simulation experiments were conducted on a stratiform cloud with embedded convection in autumn on September 25, 2023: area-wide seeding (EXP1), line seeding (EXP2), and single-point seeding (EXP3). The results showed that, after seeding, silver iodide was transported downwind by airflow, with the widest diffusion range and largest amount in EXP1, followed by EXP2, and the smallest in EXP3. After nucleation, silver iodide significantly increased ice crystals in the cloud by consuming supercooled water, enhancing the ice–snow and snow–graupel conversion processes, and increasing the contents of snow and graupel particles, which in turn raised surface precipitation. The EXP1 experiment primarily increased surface precipitation through snow melting, the EXP2 experiment through graupel melting, while the EXP3 experiment showed the smallest increase in snow or graupel. Following seeding, increased precipitation occurred 40–90 km downwind of the seeding area. The EXP1 experiment produced the greatest enhancement in surface precipitation in terms of amount, percentage, and range. The maximum regional average precipitation enhancement rate occurred 1.5 h after the end of seeding, reaching 0.047 mm h−1 in EXP1. The precipitation enhancement effect was strongest in EXP1, followed by EXP2, and weakest in EXP3, indicating that the seeding operation method significantly influences the seeding outcome. The results of this study provide scientific guidance for the operational management of weather modification.

     

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