Impacts of the Initial Perturbation Scale and Magnitude on Convection-Allowing Ensemble Forecasts over Eastern China

Given the chaotic nature of the atmosphere and inevitable initial condition errors, constructing effective initial perturbations (IPs) is crucial for convection-allowing ensemble prediction system (CAEPS). The IP growth in 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 CMA-MESO (China Meteorological Administration Mesoscale Numerical Weather Prediction System) 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, larger-scale IPs provide more comprehensive perturbation scales throughout the troposphere. 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 in 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 hours to exceed those of larger- and meso-scale IPs. Third, for upper-air and surface weather variables, larger-scale IPs provide 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 rain at 6-h forecast time. Increasing magnitude of smaller-scale IPs improves the probability forecast skills for heavy rain during the first 3–6 h.