In summertime, eastern China experiences severe ozone pollution. Stratosphere-to-troposphere transport (STT), as the primary natural source of tropospheric ozone, may have a non-negligible contribution to ground-level ozone. Rossby wave breaking (RWB) is a leading mechanism that triggers STT, which can be categorized as anticyclonic wave breakings (AWBs) and cyclonic wave breakings (CWBs). This study uses an objective method to diagnose AWBs and CWBs and to investigate their influence on the surface ozone in eastern China using ground-based ozone observations, satellite ozone data from AIRS, a stratospheric ozone tracer simulated by CAM-chem, and meteorological fields from MERRA-2. The results indicate that AWBs occur mainly and frequently over northeast China, while CWBs occur mostly over the northern Sea of Japan. STTs triggered by AWBs mainly have sinking areas over the North China Plain, increasing the ground-level ozone concentrations by 5–10 ppbv in eastern China. The downwelling zones in the CWBs extend from Mongolia to the East China Sea, potentially causing an elevation of 5–10 ppbv of ozone in both central and eastern China. This study gives an overview of the impacts of AWBs and CWBs on surface ozone in eastern China and helps to improve our understanding of summertime ozone pollution in eastern China.

The complex terrain of the Qinghai–Tibetan Plateau (QTP) makes low-level wind shear (LLWS) detection challenging. Using May–September 2023 high-resolution Doppler Wind Lidar (DWL) observations, this study analyzed the spatiotemporal characteristics of LLWS and proposed an optimized detection algorithm. A key novelty of this work lies in the development of a hybrid physical–statistical detection scheme that combines horizontal divergence with logistic regression to dynamically modulate the shear field. This approach effectively reduces noise-induced false alarms in complex plateau terrain. The results show that LLWS occurred mainly near the surface at night in June, while in September it appeared more frequently during daytime throughout the boundary layer. Horizontally, the dominant directions of LLWS shifted seasonally from northwest and west in June to south and east in September. The proposed optimization method effectively suppressed false alarms, reducing moderate and strong LLWS frequencies by 30–40%. In June, optimization significantly reduced spurious detections of LLWS in the northeast and southwest. The frequency of LLWS in the northeast direction was reduced by up to 0.03. In September, scattered shear was removed and strong shear became more organized in the southeast, while southwest shear frequency decreased by up to 0.04, confirming LLWS patterns and method effectiveness.

Accurate precipitation forecasting on the Qinghai-Tibet Plateau (QTP) remains a significant challenge due to complex terrain and nonlinear atmospheric dynamics. This study evaluates an XGBoost-SHAP framework for 24 h precipitation forecasting at Maqu Station, leveraging multi-source observations from 2020 to 2022. Vertical profile analyses via microwave radiometer (MWR) indicate that moisture is predominantly confined to altitudes below 4 km (AGL), with Integrated Water Vapor (IWV) and Liquid Water Path (LWP) typically varying between 0–15 mm and 0–2.5 mm, respectively. The optimized XGBoost model achieves an annual R² of 0.872 and a Root Mean Square Error (RMSE) of 1.609 mm, showing improved statistical consistency compared with standard Random Forest baselines. While the framework maintains robust performance for winter stratiform precipitation (RMSE = 0.32 mm), predictive variance increases during summer convective periods (RMSE = 3.26 mm). SHAP diagnostic analysis identifies Dew Point Temperature (DPT) as a consistent year-round predictor. Feature sensitivity analysis further reveals shifting seasonal driving mechanisms: spring precipitation appears sensitive to mid-tropospheric geopotential height, whereas summer forecasts are more strongly modulated by 500 hPa specific humidity and lower-level water vapor density. Overall, the XGBoost-SHAP framework serves as a transparent and physically plausible diagnostic tool for examining seasonal moisture–dynamic coupling. While these site-specific results are encouraging, they represent a localized empirical baseline; further cross-site validation is required to assess regional generalizability.

Low-level wind shear (LLWS) poses a critical threat to aviation safety, especially at high-altitude airports located in valley environments. However, the formation mechanisms of LLWS with low-level jets (LLJs) remain poorly understood under high-altitude valley conditions. In this study, high-resolution Doppler lidar observations collected during March–April 2024 were used to investigate the characteristics of low-level wind fields, LLJs, and LLWS, as well as their relationship in an airport environment. The results reveal pronounced diurnal variations in the low-level circulation, with easterly winds dominating from early morning to late morning, westerly winds prevailing in the afternoon, and southerly winds occurring at night. LLJ events exhibit a strong diurnal cycle, with the highest frequency occurring during 02:00–10:00 LT, and jet cores are mainly distributed between 200 and 300 m. The observed LLJs are predominantly easterly, with typical maximum wind speeds of 8–10 m s⁻¹. The development of LLJs is related to enhanced LLWS, although the temporal correspondence between LLJ intensity and LLWS evolution varies among individual events. The vertical shear structure is closely related to LLJ characteristics, including jet core height and wind speed distribution. Case analyses further demonstrate that the spatial evolution of LLWS follows the directional migration of LLJs, with the high wind-speed region shifting from easterly sectors in the morning to westerly sectors in the afternoon and southerly sectors at night. These findings provide new observational insights into LLJ-related LLWS processes under high-altitude valley environments and contribute to improved understanding of low-level wind hazards in complex airport environments.