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.

The North Atlantic winter storm Eunice brought severe disasters to central and western Europe in February 2022. Based on satellite data, the environmental fields, the dry intrusion characteristics on satellite water vapor images, and the impact of potential vorticity forcing in the upper and middle troposphere on explosive development are studied in this paper. The results show that during the Eunice activities, western/eastern part of the northern hemisphere high latitudes was abnormally cold/warm. The polar vortex shifted towards the northern part of the North American continent, and the average air temperature was lower than the climatological value near the North American polar region. The generation of Eunice was related to the cold air-mass splitting from polar vortex in the North American polar region and its eastward propagation along the westerlies. During the explosive development, the sea level pressure decreased by about 40 hPa/(24 h), which far exceeded the indicator of explosive development (24 hPa/(24 h)) and occurred over the ocean with positive sea surface temperature anomalies. The polar cold air-mass from the cyclone in the south of Greenland formed strong northwesterly to westerly winds over the North Atlantic Ocean surface, which was particularly important for the explosive development. Corresponding to the strong northwesterly to westerly winds, a large range of well-arranged cellular cumulus clouds extended to the vicinity of the Eunice center on satellite true color images, and rapidly enhanced dry intrusion features accompanied by strong positive potential vorticity anomalies displayed on satellite water vapor images. When Eunice reached its strongest stage, the strong positive potential vorticity extended downwards, with the maximum center at 400 hPa located directly above the storm center. Below 500 hPa, the strong positive potential vorticity tilted to the southeast, accompanied by strong descending in the middle and lower troposphere that provided energy for the enhancement of low- level wind to a certain extent. The intrusion of positive potential vorticity was also in favorable for the cyclone development below it.

The Northeast China Cold Vortex (NECV) is a significant atmospheric circulation system that triggers severe weather in mid-to-high latitudes of Asia. Fengyun-4B (FY-4B) satellite provides 15-min atmospheric motion vector (AMV) and 2-h three-dimensional temperature profiles, enabling unprecedented high spatiotemporal resolution for real-time vortex tracking. This study evaluates the effectiveness of FY-4B AMV and temperature products in tracking 24 NECVs in 2023, among which two strong NECVs in winter and summer 2023 were carefully examined. We first assessed the accuracy of wind speed and direction of the AMVs in the NECV monitoring region by comparing them with radiosonde observations, revealing reasonable correlation coefficients (CC), mean absolute errors (MAE), and root mean square errors (RMSE). NECVs and their centers were identified by using AMV data from four channels (CH09, CH10, CH11, and CH13) within the 200–500-hPa layer, employing the “8-point method” that sets specific criteria for the wind directions at 8 surrounding points to ensure a consistent cyclonic pattern around the central point. The NECV centers identified from AMVs are found to be close (mean distance of 181.9 km) to those determined by ERA5 geopotential height. The retrieved FY-4B temperature data are also evaluated against radiosonde observations, showing a high CC of 0.996 and RMSE of 1.87 K, indicating reliable temperature retrievals for NECV tracking. Based on the FY-4B/Geostationary Interferometric Infrared Sounder (GIIRS) 500-hPa temperature, the NECV cold centers are obtained and cross-validated against ERA5 reanalysis temperature at 500 hPa, revealing a mean distance deviation of 140.6 km. The real-time operational NECV monitoring based on the FY-4B AMV and temperature products on high spatiotemporal resolutions in this study provides valuable information for disaster prevention and mitigation.