Considering a whole summer season in central Europe, we find that the operational, convection‐permitting ICON‐D2 ensemble prediction system is spatially underdispersive in convective precipitation forecasts. The spatial spread of hourly precipitation is insufficient to capture the inherent error adequately across all scales (up to 300 km) and forecast times (up to 24 h). This lack of spread becomes more pronounced in the weak convective forcing regime. Using physically based stochastic perturbations in the planetary boundary layer is beneficial and leads to a reduction in spatial error at scales larger than 20 km and increases the spread at scales less than 50 km during weak forcing of convection, whereas the effect is almost neutral during strong forcing. Complementing the stochastic perturbations by perturbed parameters in the microphysics scheme shows an additive effect on spatial error and spread for a characteristic case study. Assessing the practical predictability of convective precipitation in a flow‐dependent manner is crucial, and our approach of combining multiple sources of uncertainty proves beneficial in mitigating the spatial underdispersion across scales, particularly during weak convective forcing.

中文翻译：

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结合不同模型不确定性表示的对流降水的流量和尺度相关的空间可预测性

考虑到中欧的整个夏季，我们发现可操作的、允许对流的 ICON-D2 集合预报系统在对流降水预报中在空间上分散不足。每小时降水量的空间分布不足以充分捕捉所有尺度（最多 300 公里）和预报时间（最多 24 小时）的固有误差。这种扩散的缺乏在弱对流强迫状态下变得更加明显。在行星边界层中使用基于物理的随机扰动是有益的，在对流弱强迫期间，可以减少大于 20 km 尺度的空间误差，并增加小于 50 km 尺度的扩散，而在强对流强迫期间，效果几乎是中性的。强迫。通过微物理方案中的扰动参数来补充随机扰动，显示出对特征案例研究的空间误差和扩散的附加效应。以依赖流量的方式评估对流降水的实际可预测性至关重要，我们结合多种不确定性来源的方法被证明有助于减轻跨尺度的空间欠分散，特别是在弱对流强迫期间。