This study introduces a tornado perturbation model utilizing the cyclostrophic wind model, implemented through a shallow-water equation framework. Four numerical experiments were conducted: a single cyclonic wind perturbation (EXP1), a single low-geopotential height perturbation (EXP2), a cyclonic wind perturbation with a 0 Coriolis parameter (EXP3), and a single anticyclonic wind perturbation (EXP4). The outputs showed that in a static atmosphere setting, a small-scale cyclonic wind perturbation generated a tornado-like pressure structure. The centrifugal force in the central area exceeded the pressure gradient force, causing air particles to flow outward, leading to a pressure drop and strong pressure gradient. The effect of the Coriolis force is negligible for meso-γ-scale and smaller systems, while for meso-β-scale and larger systems, it begins to have a significant impact. The results indicate that a robust cyclonic and an anticyclonic wind field can potentially generate a pair of cyclonic and anticyclonic tornadoes when the horizontal vortex tubes in an atmosphere with strong vertical wind shear tilt, forming a pair of positive and negative vorticities. These tornadoes are similar but have different rotation directions.

This study investigates an extreme squall line event that occurred in northern Jiangxi Province, China on 30–31 March 2024. Based on the WRF model, convection-permitting ensemble forecast experiments were conducted using two distinct initial perturbation approaches, namely, the Local Breeding of Growing Modes (LBGM) and the Ensemble Transform Kalman Filter (ETKF), to compare their perturbation structures, spatiotemporal evolution, and precipitation forecasting capabilities. The experiments demonstrated the following: (1) The LBGM method significantly improved the root mean square error (RMSE) of mid-upper tropospheric variables, particularly demonstrating superior performance in low-level temperature field forecasts, but the overall ensemble spread of the system was consistently smaller than that of ETKF. (2) The evolution of dynamical spread within the squall line system confirmed that ETKF generated greater spread growth in low-level wind fields, while LBGM exhibited better spatiotemporal alignment between mid-upper tropospheric wind field spread and the synoptic system evolution. (3) Vertical profiles of total moist energy revealed that ETKF initially exhibited higher total moist energy than LBGM. Both methods showed increasing total moist energy with forecast lead time, displaying a bimodal structure dominated by kinetic energy in upper layers (300–100 hPa) and balanced kinetic energy and moist physics terms in lower layers (1000–700 hPa), with ETKF demonstrating larger growth rates. (4) Kinetic energy spectrum analysis indicated that ETKF exhibited significantly higher perturbation energy than LBGM in the 100–1000 km mesoscale range and superior small- to medium-scale perturbation characterization at the 6–60 km scales initially. Precipitation and radar echo verification showed that ETKF effectively corrected positional biases in precipitation forecasts, while LBGM more accurately reproduced the bow-shaped echo structure near Nanchang due to its precise simulation of leading-edge vertical updrafts and rear-sector low pseudo-equivalent potential temperature regions.

In the context of global warming, the prediction of extreme precipitation events faces great challenges, especially the ensemble forecast of convective-scale heavy precipitation. Taking the heavy rainstorm in Zhengzhou on 20 July 2021 as an example, this paper aims to explore the performance of the convective-scale ensemble forecasting system based on the local breeding model cultivation method (LBGM) in extreme precipitation forecasting, and reveal the key physical mechanisms affecting the quality of forecasting. The traditional scoring (TS, Bias), neighborhood FSS and Contiguous Rain Area (CRA) methods were used to systematically evaluate the precipitation forecast, and the superior and inferior forecast members were diagnosed and analyzed by combining physical quantities such as isentropy vortex, relative vorticity, and water vapor flux divergence. The results show that: (1) the LBGM-EPS system can better capture the spatial distribution and intensity of heavy precipitation, which is better than the single deterministic forecast; (2) The CRA method is better than the traditional score in describing the spatial structure and intensity of precipitation, and can effectively identify the good and bad members of the forecast. (3) The reason why the dominant forecast members perform better is that the simulation of the dynamic-thermal structure of the mesoscale convective vortex is more reasonable, especially the coupling mechanism of the downward transmission of the high-level vortex and the convergence of water vapor at the lower level is better. The preliminary application of convective-scale ensemble forecasting based on the LBGM in this study has reference value for improving the prediction ability of extreme precipitation.