风云二号气象卫星红外观测在云团降水监测中的应用

提升灾害性对流天气的监测预警能力是短临天气预报的首要目标，但对流性降水在时间、空间上分布高度不均，观测难度大。卫星遥感监测降水的传统红外、水汽亮温判识方法，报警云团数量多，空报率高，指示意义不稳定，需要结合背景因素寻找方法提炼卫星辐射观测中更多的内在隐含信息，建立云顶亮温与此类灾害天气间的联系。此文尝试使用FY-2气象卫星红外云图数据和逐时加密地面降水观测资料，通过追踪云团移动进而分类、提取参数，然后用模糊支持向量机（FSVM）方法建立地面观测雨强与云团特征动态演变间的机器学习数学关系，标识出有监测预警意义的云团和强降水中心，对检验地域和时间的卫星强降水云团检测识别率达80％左右。

Algorithm design of convective precipitation monitoring and early warning service using FY-2 infrared data

Improving the monitoring and early warning capability of catastrophic convective weather is the primary target of short-term weather forecasting and nowcasting. Convective precipitation/wind is highly uneven in time and space, and realtime monitoring is difficult. As for the traditional identification methods using infrared and water vapor brightness temperature for satellite remote sensing monitoring of precipitation, the number of alarm clouds is large, the false alarm rate is high, and the indicative significance is poor. It is necessary to find ways to extract more hidden information in satellite radiation observation combined with background factors and establish connections between the cloud top brightness temperature and the location of such disaster weather. This study attempts to use data of FY-2 infrared cloud imagery and intensified ground observation of hourly precipitation to classify and extract parameters by tracking cloud movement, and then use fuzzy support vector machine (FSVM) method to establish mathematical relations for machine learning between ground observation of rainfall intensity and dynamic evolution of cloud cluster. Based on the mathematical relationship, the cloud cluster and the heavy precipitation center can be identified with the significance of monitoring and early warning. This work also illustrates the possibility of developing satellite products for heavy precipitation cloud detection suitable for disaster monitoring and early warning in specific seasons.