Flow dependence of forecast uncertainty in a large convection‐permitting ensemble

Abstract The flow dependence of forecast distributions is studied using a 120‐member ICON‐D2 ensemble for two representative case studies of weak and strong synoptic forcing of convection. The bootstrapping technique is used to investigate the convergence of sampling error for a range of surface and midtroposphere variables. Convergence is generally observed for the mean and the standard deviation, but not for the 95th percentile, especially in strong forcing. Additionally, maps of uncertainty are introduced, which allow for a more detailed analysis of the spatial pattern of uncertainty and facilitate the interpretation of the sources and evolution of forecast uncertainty in different synoptic forcing conditions. It is found that convection significantly increases uncertainty in model variables and shapes the spatial uncertainty pattern, with weak forcing leading to patchy uncertainty and strong forcing resulting in a more coherent pattern due to large convective systems. Overall, the main result of this work is the strong connection between uncertainty of forecast variables and convection, with synoptic forcing being crucial in determining the spatial distribution and uncertainty evolution within 24 h.