Given the chaotic nature of the atmosphere and inevitable initial condition errors, constructing effective initial perturbations (IPs) is crucial for the performance of a convection-allowing ensemble prediction system (CAEPS). The IP growth in the CAEPS is scale- and magnitude-dependent, necessitating the investigation of the impacts of IP scales and magnitudes on CAEPS. Five comparative experiments were conducted by using the China Meteorological Administration Mesoscale Numerical Weather Prediction System (CMA-MESO) 3-km model for 13 heavy rainfall events over eastern China: smaller-scale IPs with doubled magnitudes, larger-, meso-, and smaller-scale IPs; and a chaos seeding experiment as a baseline. First, the constructed IPs outperform unphysical chaos seeding in perturbation growth and ensemble performance. Second, the daily variation of smaller-scale perturbations is more sensitive to convective activity because smaller-scale perturbations during forecasts reach saturation faster than meso- and larger-scale perturbations. Additionally, rapid downscaling cascade that saturates the smallest-scale perturbation within 6 h for larger- and meso-scale IPs is stronger in the lower troposphere and near-surface. After 9–12 h, the disturbance development of large-scale IPs is the largest in each layer on various scales. Moreover, thermodynamic perturbations, concentrated in the lower troposphere and near-surface with meso- and smaller-scale components being dominant, are smaller and more responsive to convective activity than kinematic perturbations, which are concentrated on the middle–upper troposphere and predominantly consist of larger- and meso-scale components. Furthermore, the increasing magnitude of smaller-scale IPs enables only their smaller-scale perturbations in the first 9 h to exceed those of larger- and meso-scale IPs. Third, for forecast of upper-air and surface variables, larger-scale IPs warrant a more reliable and skillful CAEPS. Finally, for precipitation, larger-scale IPs perform best for light rain at all forecast times, whereas meso-scale IPs are optimal for moderate and heavy rains at 6-h forecast time. Increasing magnitude of smaller-scale IPs improves the probability forecast skills for heavy rains during the first 3–6 h.

How to construct appropriate perturbations for convection-permitting ensemble prediction systems (CPEPSs) is a critical issue awaiting urgent solutions. As two common perturbations, initial perturbations (IPs) and lateral boundary perturbations (BPs) interact with each other, affecting the model error growth, especially in mesoscale models. Using the China Meteorological Administration (CMA)-CPEPS, this study tries to elucidate how BPs interact with matched and mismatched IPs under varied large-scale weather conditions/forcings. Seven groups of experiments were conducted for strong-forcing and weak-forcing weather regimes over southern China: three with single IPs, one with single BPs, and three with combined perturbations. It is found that the perturbation magnitudes were dominated by meso-α-scale components, and IPs under weak forcing exhibited more pronounced effects than under strong forcing; whereas BPs exerted more pronounced effects under strong forcing than weak forcing regimes. Furthermore, it lasts longer for high-level variables when the perturbation energy from BPs is higher than that from IPs, compared to low-level variables. Moreover, for precipitation and dynamic variables, IPs and BPs can mutually reinforce. The source of these perturbations, and their specific vertical levels, do not alter the extent of their interactions. Nevertheless, the weather regime and the scales of the perturbations influence the strength of their mutual reinforcement. In particular, the weak-forcing regimes exhibit a more pronounced reinforcing effect, and meso-α-scale perturbations are more conducive to fostering interactions compared to meso-β-scale ones. Ultimately, it is the perturbation magnitude inherent in the initial perturbation itself that determines the interactions between IPs and BPs.