Infrared brightness temperature–based indicators for identifying thunderstorm clouds: Insights from FY-4A satellite observations

Accurate monitoring and timely identification of early indicators of thunderstorms are of paramount importance in preventing and mitigating the potential disasters associated with such meteorological events. This study utilizes the TOBAC (Tracking and Object-Based Analysis of Clouds) automated tracking algorithm, integrated with FY-4A geostationary satellite infrared data, to investigate the spatiotemporal characteristics and evolutionary patterns of cloud-top brightness temperature (BT), areal extent, and phase transitions of thunderstorm and non-thunderstorm clouds across the North China region. The analysis reveals that thunderstorm clouds demonstrate significantly enhanced convective intensity compared to non-thunderstorm clouds, with the following distinctive features: (1) a substantially faster drop rate in 10.8 μm BT, reaching values nearly 2.3 times greater; (2) positive BT differences between the 10.8 and 8.5 μm channels, exceeding 0 K; and (3) a dramatically increased areal expansion rate, approximately 4.5-fold higher than that observed in non-thunderstorm clouds. These findings provide significant scientific insights and practical implications for advancing thunderstorm identification techniques and optimizing early warning systems, which are critical to improving disaster prevention and mitigation capacity across the North China region. 准确监测与及时识别雷暴早期指标对防灾减灾至关重要.本文基于TOBAC (Tracking and Object-Based Analysis of Clouds) 自动追踪算法, 结合FY-4A卫星红外资料, 分析了华北地区雷暴云与非雷暴云在云顶亮温,面积及相态演变等方面的时空特征与发展规律差异.结果表明: (1) 10.8 μm通道亮温下降速率为非雷暴云的约2.3倍; (2) 10.8与8.5 μm通道亮温差为正, 超过0 K; (3) 云顶面积扩展速率为非雷暴云的约4.5倍.本研究有助于增强雷暴识别与预警能力, 提升华北地区气象灾害防御水平.