The roles of chaos seeding and multiple perturbations, including model perturbations and topographic perturbations, in convection-permitting ensemble forecasting, are assessed. Six comparison experiments were conducted for 14 heavy rainfall events over southern China. Chaos seeding was run as a benchmark experiment to compare their effects to the intended perturbations. The results first reveal the chaos seeding phenomenon. That is, the tiny and local perturbations of the skin soil moisture propagate into the whole analysis domain within an hour and expand to every prognostic variable, and the perturbations derived from chaos seeding develop when moist convection is active. Second, the chaos seeding has a statistically significant difference from our intended perturbations for the ensemble spread magnitudes of precipitation and the spread–skill relationships and probabilistic forecast skills of dynamical variables. Additionally, for the probabilistic forecasts of precipitation, initial and lateral boundary perturbations and model perturbations can yield statistically larger FSS and AROC scores than chaos seeding; topographic perturbations can only improve FSS and AROC scores a little. The different performances may be related to the different degrees of the real dynamical influence of our intended perturbations. Finally, model perturbations can increase the ensemble spreads of precipitation, and improve FSS and AROC scores of precipitation and the consistency of mid- and low-level dynamical variables. However, the effects of topographic perturbations are small.

To compare the roles of two kinds of initial perturbations in a convection-permitting ensemble prediction system (CPEPS) and reveal the effects of the differences in large-scale/small-scale perturbation components on the CPEPS, three initial perturbation schemes are introduced, including a dynamical downscaling (DOWN) scheme originating from a coarse-resolution model, a multiscale ensemble transform Kalman filter (ETKF) scheme, and a filtered ETKF (ETKF_LARGE) scheme. First, the comparisons between the DOWN and ETKF schemes reveal that they behave differently in many ways. Specifically, the ensemble spread and forecast error for precipitation in the DOWN scheme are larger than those in the ETKF; the probabilistic forecasting skill for precipitation in the DOWN scheme is better than that in the ETKF at small neighborhood radii, whereas the advantages of the ETKF begin to appear as the neighborhood radius increases; DOWN possesses better spread–skill relationships than ETKF and has comparable probabilistic forecasting skills for nonprecipitation. Second, the comparisons between DOWN and ETKF_LARGE indicate that the differences in the large-scale initial perturbation components are key to the differences between DOWN and ETKF. Third, the comparisons between ETKF and ETKF_LARGE demonstrate that the small-scale initial perturbations are important since they can increase the precipitation spread in the early times and decrease the forecast errors while simultaneously improving the probabilistic forecasting skill for precipitation. Given the advantages of the DOWN and ETKF schemes and the importance of both large-scale and small-scale initial perturbations, multiscale initial perturbations should be constructed in future research.

In July 2023, Beijing and southern Hebei experienced exceptionally heavy rainfall due to the remnants of Typhoon “Dussuri.” This study aims to investigate the factors influencing the forecast performance of numerical models for this event and the sources of forecast uncertainty. Initially, the operational convection-allowing ensemble forecast system (CAEFS) of North China was evaluated for its forecast accuracy. Based on ensemble forecast results, ensemble sensitivity analysis and ensemble group difference analysis were conducted to understand the variability among ensemble members. The findings highlight several influencing factors, notably the position of the high pressure system and the role of low-level jet streams, and there is significant uncertainty surrounding the large-scale circulation near the inverted trough. Ensemble member disparities reveal that good members accurately pinpoint the positions of the inverted trough and jet streams, facilitating substantial moisture transport, whereas less effective members struggle with understanding large-scale circulation dynamics and moisture factors. Given the pivotal role of large-scale circulation in this event, a scheme was devised to reconstruct background and lateral boundary conditions. This approach utilizes high-quality global deterministic forecasts to enhance the large-scale ensemble driving fields for CAEFS. Results from this new reconstruction method demonstrate marked improvements in both the location and intensity forecasts of this event, underscoring its promising application potential.