Cloud and precipitation systems are simulated with a multi‐scale modeling framework (MMF) and compared over the Tropics and Subtropics against the Tropical Rainfall Measuring Mission (TRMM) Radar‐defined Precipitation Features (RPFs) product. A methodology, in close analogy to the TRMM RPFs, is developed to produce simulated precipitation features (PFs) from the output of the embedded two‐dimensional (2D) cloud‐resolving models (CRMs) within an MMF. Despite the limitations of 2D CRMs, the simulated population distribution, horizontal and vertical structure of PFs, and the geographical location and local rainfall contribution of mesoscale convective systems (MCSs) are in good agreement with the TRMM observations. However, some model discrepancies are found and can be identified and quantified within the PF distributions. Using model biases in relative population and rainfall contributions, PFs can be characterized into four size categories: small, medium to large, very large, and extremely large. Four different major mechanisms might account for the model biases in each different category: (1) the two‐dimensionality of the CRMs, (2) a positive convection‐wind‐evaporation feedback loop, (3) an artificial dynamic constraint in a bounded CRM domain with cyclic boundaries, and (4) the limited CRM domain size. The second and fourth mechanisms tend to contribute to the excessive tropical precipitation biases commonly found in most MMFs, whereas the other mechanisms reduce rainfall contributions from small and very large PFs. MMF sensitivity experiments with various CRM domain sizes and grid spacings showed that larger domains (higher resolutions) tend to shift PF populations toward larger (smaller) sizes.

中文翻译：

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在多尺度建模框架中评估降水特征和降雨特征

利用多尺度建模框架（MMF）对云和降水系统进行了模拟，并与热带和亚热带地区的热带雨量测量任务（TRMM）雷达定义的降水特征（RPF）产品进行了比较。开发了一种类似于TRMM RPF的方法，以便从MMF中嵌入的二维（2D）云解析模型（CRM）的输出中生成模拟的降水特征（PF）。尽管二维CRM的局限性，但中尺度对流系统（MCS）的模拟人口分布，PF的水平和垂直结构以及地理位置和局部降雨贡献与TRMM观测结果非常吻合。但是，发现了一些模型差异，并且可以在PF分布内对其进行识别和量化。利用相对人口和降雨贡献的模型偏差，可将PFs分为四个大小类别：小，中到大，非常大和非常大。四种不同的主要机制可能会解释每种不同类别中的模型偏差：（1）CRM的二维性；（2）对流-风-蒸发正向反馈回路；（3）有界CRM中的人为动态约束具有循环边界的域，以及（4）受限制的CRM域大小。第二和第四种机制倾向于导致大多数MMF中普遍存在的过度热带降水偏差，而其他机制则减少了小型和大型PF的降雨贡献。