Effective representation of model uncertainty is crucial for improving the forecast skill of convection-permitting ensemble prediction system. The Stochastic Perturbed Parameterization Tendencies (SPPT) scheme is one of the primary approaches used to represent model uncertainty, and its effect is controlled by three parameters: Perturbation magnitude, temporal correlation scale, and horizontal perturbation scale. There have been few studies on the optimization of these three parameters for the operational 3 km CMA-REPS (Regional Ensemble Prediction System of China Meteorological Administration) v4.0. Based on CMA-REPS, this study selects 13 heavy rainfall cases in North China in 2024 to conduct SPPT parameter sensitivity experiments. The forecast skill for upper-air and surface variables, precipitation, and perturbation energy growth are analyzed. First, using a smaller magnitude (with a standard deviation of 0.35) and dropping attenuation of the perturbations within the boundary layer most effectively enhances the forecast skill of the variables. Second, a 3 h temporal scale is conducive to improving the forecast skill within the initial 12 h, whereas a 6 h time scale performs better after 24 h of integration. A horizontal scale of 500 km yields the best overall performance. Compared with a 1000 km scale, it improves the spread and consistency for most variables. Further reducing the scale to 200 km can improve light and moderate rain forecasts within the initial 12 h but leads to a decline in forecast skill after 18 h. Third, spatiotemporal scales significantly influence the perturbation energy growth. The 3 h temporal scale promotes the perturbation energy growth across scales within the initial 12 h, while the 6 h scale is more favorable for perturbation growth after 18 h. The 500 km horizontal scale is most beneficial for the development of difference kinetic energy and difference latent energy. Although the 200 km horizontal scale can initially enhance low-level perturbation energy and promote smaller-scale perturbation growth during convectively active periods, it results in the minimal development of larger- and meso-scale components, as well as perturbation in the middle and late periods of integration. In conclusion, a 0.35 standard deviation with unattenuated boundary layer perturbations, a 6 h temporal scale, and a 500 km horizontal scale are recommended.

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.