In this study, the effects of different nesting methods on simulating a flash‐flood‐producing severe convective event over Cheongju, South Korea on July 16, 2017 was examined. This event developed as part of a mesoscale convective system (MCS) accompanied by frontal forcing. Numerical experiments were conducted using the Weather Research and Forecasting model (WRF) employing one‐way concurrent (OWC), one‐way sequential (OWS), and two‐way (TW) nesting approaches with advanced physics options from Korean Integrated Model (KIM). Analysis of model simulations against Tropical Rainfall Measuring Mission (TRMM) and Automatic Weather Station (AWS) observations suggests that the TW nesting method performs better than both OW nesting methods in simulating rainfall. Large‐scale features, moisture, instability in the boundary layer, and the vertical distribution of meteorological parameters favorable for convection are better represented by TW nesting. Probability distribution Function (PDF) analysis from AWS/WRF reveals that the local‐scale distribution of surface meteorological parameters which affect storm intensity were well captured using TW. Further assessment of Equitable Threat score (ETS) also showed better precipitation forecast skill over different thresholds in TW. Vertical velocity in the innermost domain simulated using TW nesting is more consistent with ERA5 reanalysis. An additional MCS (11–13 July 2006) simulated using a similar numerical setup also benefited from TW nesting, increasing confidence in the initial findings. Along with frequent lateral boundary conditions, TW nesting allows multi‐scale interactions between parent and nested domains and improves the representation of both synoptic and local‐scale features, enhanced cloud hydrometeor, vertical velocity distributions, and subsequent rainfall.

中文翻译：

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使用天气研究和预报模型，不同嵌套方法对韩国一次严重对流事件模拟的影响

在这项研究中，研究了不同嵌套方法对2017年7月16日在韩国清州市上空产生洪涝的严重对流事件进行模拟的影响。该事件是中尺度对流系统（MCS）的一部分，并伴有锋面强迫。使用天气研究和预报模型（WRF）进行了数值实验，该模型采用单向并发（OWC），单向顺序（OWS）和双向（TW）嵌套方法以及来自韩国综合模型（KIM）的高级物理选项）。针对热带雨量测量任务（TRMM）和自动气象站（AWS）观测值进行的模型仿真分析表明，TW嵌套方法在模拟降雨方面要比OW嵌套方法更好。大规模特征，水分，边界层的不稳定性，TW嵌套可以更好地表示有利于对流的气象参数的垂直分布。AWS / WRF的概率分布函数（PDF）分析表明，使用TW可以很好地捕获影响风暴强度的地表气象参数的局部尺度分布。对公平威胁评分（ETS）的进一步评估还显示，在TW的不同阈值上，降水预报技能更高。使用TW嵌套模拟的最内部区域中的垂直速度与ERA5重新分析更加一致。使用类似的数值设置模拟的另一个MCS（2006年7月11日至13日）也受益于TW嵌套，从而增加了对初始结果的信心。伴随着频繁的横向边界条件，