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干旱半干旱地区层状云人工增雨雪作业催化指标研究

Research on Seeding Criteria for Artificial Rain and Snow Enhancement in Stratiform Clouds over Arid and Semi-arid Regions

  • 摘要: 本研究基于WRF中尺度数值模式构建的三维冷云催化模型,研究了中国干旱半干旱地区层状云降水的人工催化潜力与增雨(雪)效果。研究建立了分等级催化潜力指标体系,将催化潜力划分为三个等级:一级(弱)、二级(一般)和三级(强)。通过多组数值模拟试验验证,确定三级潜力区的最优催化条件为:小时降水量0.1~20 mm h−1、垂直速度>0.01 m s−1、温度−12°C~−5°C、云水含量>0.2 g kg−1。研究发现,过冷水主要分布在−12°C~0°C温度区间,其中−5°C~−12°C为最佳催化温度窗口。该温度范围内层状云系具有较高的稳定性,适度的过冷水含量与微弱垂直扰动为冰晶凝华增长提供了理想环境。数值模拟结果表明,夏季层状云增雨催化会改变云中各类水凝物的空间分布,并引起催化区域温度场和动力场的响应;而冬季层状云增雪催化对云微物理过程和动力场的影响相对较弱,但在自然降水背景较弱的条件下仍能形成清晰可辨的催化线特征。在实施增雨作业前,可通过模式预报提前获取目标云系的宏微观特征,基于本研究的催化指标预判并制定包含最佳作业区域与时机的最优方案,为科学开展人工增雨雪作业提供重要指导。

     

    Abstract: This study employs a three-dimensional cold cloud seeding model integrated with the Weather Research and Forecasting mesoscale numerical model to investigate the potential and effects of artificial precipitation enhancement in stratiform cloud systems across China’s arid and semi-arid regions. New seeding criteria were developed to categorize seeding potential into three levels: level 1 (weak), level 2 (medium), and level 3 (strong). Through extensive numerical simulation experiments, the optimal seeding conditions for areas with level 3 potential were quantitatively determined, characterized by precipitation intensity ranging from 0.1 to 20 mm h−1, vertical velocity exceeding 0.01 m s−1, temperature between −12°C and −5°C, and cloud water content greater than 0.2 g kg−1. The research reveals that supercooled water is predominantly distributed within the −12°C to 0°C temperature range, with the −12°C to −5°C interval identified as the optimal seeding temperature window due to the high stability of stratiform cloud systems in this regime. The moderate supercooled water content coupled with weak vertical disturbances creates an ideal environment for ice crystal growth through desublimation. Numerical simulations demonstrate that summer stratiform cloud seeding alters the spatial distribution of hydrometeors while inducing responses in both the thermal and dynamic fields over the seeded region. In contrast, winter stratiform cloud seeding for snowfall exerts weaker effects on cloud microphysical processes and the dynamic field. However, even under weak natural precipitation conditions, distinct linear seeding signatures can still be identified. Prior to precipitation enhancement operations, key macro- and microphysical characteristics of the target cloud systems can be obtained through model forecasting. By applying the seeding criteria established in this study, the optimal seeding locations and timing can be determined, enabling the formulation of effective operational strategies and providing valuable scientific guidance for precipitation enhancement operations.

     

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