【摘要】 构建了一个基于WRF-ARW （Advanced Re search Weather Research and Forecasting）模式的中尺度集合预报系统,直接耦合碘化银（AgI）冷云催化模块,以探讨北方层状云的催化增水潜力及其不确定性。选取2022年2月12～13日华北地区的一次典型降雪过程,通过扰动初始条件（EN_IC）和微物理参数化方案（EN MP）集合预报催化目标区的作业条件,并通过耦合AgI催化集合（AgI_IC）进行催化效果评估。结果表明,目标区域内云系具备较高的催化潜力,过冷水含量>0.2 gkg^-1概率达85%、最大冰晶数浓度<20 L^-1的概率超过75%。催化试验结果显示,AgI提高了冰晶生成速率,增强了冰水转化效率。集合成员均预报出增雪效果,集合预报增水量2.76%～101.09%,平均增水量约36.87%。本研究创新性地采用非参数核密度估计（KDE）方法量化催化效果的不确定性,结果表明,催化显著改变了降水概率分布,催化后增水概率>0.01 mm的区域覆盖率达90%,增水概率>0.1 mm的区域超过70%。研究表明,集合概率预报能够量化人工催化中的不确定性,在催化条件预报和效果预估阶段提供科学参考,对提升人工影响天气作业的科学性和精准性有一定应用价值。更多还原

【Abstract】 A mesoscale ensemble forecasting system based on the WRF-ARW(Advanced Research Weather Research and Forecasting) model was developed and an AgI cold-cloud seeding module was directly coupled to evaluate the precipitation-enhancement potential of stratiform clouds and quantify associated uncertainties in northern China. A typical snowfall event over North China from 12 February to 13 February 2022, is selected for investigation. This study employs a multimicrophysics ensemble and a multi-initial-condition ensemble to assess the seeding suitability of the target area.Moreover, an AgI-coupled ensemble is utilized to evaluate the seeding effects. The results indicate that the target cloud system possesses high seeding potential, with ensemble probabilistic forecasts showing a supercooled water content(>0.2 g/kg) probability exceeding 85% and an ice crystal concentration(<20 L-1) probability surpassing 75%. Seeding experiments demonstrate that AgI particles enhance ice crystal formation and accelerate ice–water conversion, leading to increased precipitation. The precipitation enhancement ranges from 2.76% to 101.09%, with an ensemble mean of about36.87%. This study innovatively applies nonparametric kernel density estimation to quantify uncertainty in seeding effects. The results reveal that the seeding process considerably alters the precipitation probability distribution. After seeding, the probability of precipitation enhancement exceeding 0.01 mm covers more than 90% of the target area,whereas that of precipitation enhancement exceeding 0.1 mm surpasses 70% of the target area. This study demonstrates that ensemble probabilistic forecasting can effectively quantify uncertainties in artificial cloud seeding, providing scientific support for seeding-condition prediction and seeding-effect estimation, thereby helping improve the scientific nature and precision of weather-modification operations.更多还原