Aerosol radiative forcing is known to significantly disturb the thermodynamic and dynamic structure of the Planetary Boundary Layer (PBL), particularly in heavily polluted urban regions. In this study, the effects of aerosol–PBL interactions were examined over a megacity in China’s Central Plains by comparing ERA5 reanalysis data with multi-source ground-based observations. Key meteorological variables—including wind speed, wind direction, temperature, and relative humidity—were analyzed across pressure levels from 1000 to 800 hPa. Good agreement in wind direction was observed between ERA5 and observations (R² > 0.84), while wind speed showed a moderate correlation (R² = 0.54–0.73) with an RMSE of 1.85 m/s near 975 hPa. Temperature discrepancies were found to decrease with altitude, with RMSE values reducing from 3.02 K to 1.84 K, indicating a modulation of thermal stratification by aerosol radiative effects. A stable structure was revealed by humidity analysis near the surface but increased variability aloft, with absolute differences reaching ±30% at 850–800 hPa. Diurnal variations were characterized by night-time warming of up to +5 °C in the lower PBL and concurrent cooling above 800 hPa. The Heating and surface Dimming (HD) Index was found to correlate positively with PM_2.5 concentration (R = 0.60), reflecting increased thermal stability and vertical inhomogeneity under aerosol loading. These findings underscore the need for an improved understanding and mitigation of aerosol–PBL interactions to support sustainable urban air quality management strategies.

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.

Understanding and estimating the impact of atmospheric turbulence on the growth of precipitation particles in freezing weather can help improve the prediction of freezing rain precipitation. In this study, the turbulent kinetic energy dissipation rate (ε) during a freezing weather event was retrieved using the power spectral analysis method, based on observational data from a Ka-band millimeter-wave cloud radar, radiosondes, and ground automatic weather stations, and the influence of atmospheric turbulence on the growth of freezing rain precipitation particles was investigated using nonlinear causal methods such as transfer entropy (TE), convergent cross-mapping (CCM), the phase-randomized surrogate test, and partial CCM. Causality tests confirmed a significant bidirectional causal relationship between ε and Z_e, with the information transfer from ε to Z_e systematically dominating throughout the entire height range of precipitation particles. The bidirectional original ρ was far higher than the 99% quantile of the surrogate distribution, and the coupling was enhanced after removing the influence of temperature and humidity, indicating that it was independent of the thermodynamic covariant background. The causal intensity in the ice crystal growth zone above 6 km was approximately 3 times the average in the liquid zone, indicating that atmospheric turbulence is one of the key factors driving the growth of precipitation particles in freezing weather and that the effect is concentrated in the dominant layer of ice crystals. This finding provides a practical basis for improving the parameterization scheme and numerical prediction of the precipitation particle microphysical processes in freezing weather.