Water vapor boundaries (WVBs) are recognized indicators of clear-air turbulence (CAT), yet their relationship remains insufficiently explored due to the lack of automated detection methods. In this study, an improved multi-scale “2-out-of-8” neighborhood criterion recognition algorithm is adopted to automatically identify mid- to upper-tropospheric WVBs based on water vapor observations from the Advanced Geostationary Radiation Imager onboard the Fengyun-4A satellite. By integrating high-altitude CAT reports, identified WVBs, and the ERA5 reanalysis data, the spatiotemporal relationship between CAT and WVBs, along with their associated environmental characteristics, is systematically analyzed over China and its surroundings in 2019. A total of 341 WVBs and 752 CAT events were identified, with 45% of the CAT events exhibiting a pronounced association with WVBs. It is found that the risk of encountering CAT increased markedly for flights operating above 5000 m and located along the leading edge or within 100 km downstream of dry surge or jet-parallel WVBs, particularly when the flight path intersected the WVB at angles greater than 30º. Environmental field analysis reveals that WVB regions exhibited enhanced potential vorticity and potential vorticity gradients, higher wind speed, and pronounced wind shear, all of which favor the enhancement of Kelvin–Helmholtz instability and the triggering of CAT events. These findings highlight the indicative role of WVBs in CAT occurrence and provide valuable references for CAT detection and forecasting.

The subtropical high (hereinafter “STH”) is a fundamental large-scale weather system that governs tropical and subtropical regions. Its structural variations are closely related to regional heavy rainfall, summer heatwaves, and other extreme weather events. Accurately characterizing both the intensity and spatial features of the subtropical high is crucial for weather forecasting and short-term climate prediction. Currently, traditional weather observations and analyses are inadequate for the real-time, refined identification of STH structures. In this study, we introduce an STH identification model based on the Fengyun-4 (FY-4) satellites (FY_STH), utilizing a machine learning model that synthesizes brightness temperature data from the Advanced Geosynchronous Radiance Imager (AGRI) onboard FY-4 with satellite-retrieved outgoing longwave radiation (OLR) measurements. The model utilizes a piecewise eXtreme Gradient Boosting (XGBoost) classification decision tree ensemble to identify the subtropical high, and Bayesian optimization to refine its structure and parameters for optimal performance. The ERA5 reanalysis data serve as the benchmark for defining the subtropical high, and systematic evaluations of the FY_STH model are conducted, with sounding data used for validation. The results demonstrate that FY_STH consistently delineates the STH influence area in different seasons, achieving an average accuracy of 0.8, a false alarm rate of 0.3, a critical success index (CSI) of approximately 0.6, a dice coefficient of about 0.7, and an area under the curve (AUC) of the ROC (Receiver Operating Characteristic) around 0.9. In comparison with the CMA-GFS forecasts, FY_STH exhibits superior overall performance, with all evaluation metrics showing that its results are accurate and reliable. FY_STH is then employed to capture the relationship between Typhoon Gaemi and the STH over the western Pacific during 19–28 July 2024. It is revealed that variations in the trajectory of Typhoon Gaemi are strongly associated with shifts in the STH position, underscoring that real-time satellite monitoring facilitates a timely and precise understanding of typhoon trajectory variations and enhances the forecast accuracy based on the interaction between the STH and the typhoon.