Cloud properties were investigated based on aircraft and cloud radar co-observation conducted at Yitong, Jilin, Northeast China. The aircraft provided in situ measurements of cloud droplet size distribution, while the...Cloud properties were investigated based on aircraft and cloud radar co-observation conducted at Yitong, Jilin, Northeast China. The aircraft provided in situ measurements of cloud droplet size distribution, while the millimeter-wavelength cloud radar vertically scanned the same cloud that the aircraft penetrated. The reflectivity factor calculated from aircraft measurements was compared in detail with sinmltaneous radar observations. The results showed that the two reflectivities were comparable in warm clouds, but in ice cloud there were more differences, which were probably associated with the occurrence of liquid water. The acceptable agreement between reflectivities obtained in water cloud confirmed that it is feasible to derive cloud properties by using aircraft data, and hence for cloud radar to remotely sense cloud properties. Based on the dataset collected in warm clouds, the threshold of reflectivity to diagnose drizzle and cloud particles was studied by analyses of the probability distribution function of reflectivity from cloud particles and drizzle drops. The relationship between refiectivity factor (Z) and cloud liquid water content (LWC) was also derived from data on both cloud particles and drizzle. In comparison with cloud droplets, the relationship for drizzle was blurred by many scatter points and thus was less evident. However, these scatters could be partly removed by filtering out the drop size distribution with a large ratio of reflectivity and large extinction coefficient but small effective radius. Empirical relationships of Z-LWC for both cloud particles and drizzle could then be derived.展开更多
液态水含量(Liquid Water Content,LWC)是重要的云参数,对了解云微物理过程以及在人工影响天气效果检验等方面有重要的指导意义。针对已有研究的反射率因子(Z)与LWC经验公式适用范围有限的问题,利用2018—2020年飞机观测资料,在验证中...液态水含量(Liquid Water Content,LWC)是重要的云参数,对了解云微物理过程以及在人工影响天气效果检验等方面有重要的指导意义。针对已有研究的反射率因子(Z)与LWC经验公式适用范围有限的问题,利用2018—2020年飞机观测资料,在验证中国首部机载云雷达(Ka-band Precipitation Radar,KPR)探测能力和数据可靠性的基础上,采用分档平均方案,建立了适用于降水性积层混合云的Z-LWC经验公式(Z=2454.71×LWC^(1.614)),决定系数达0.995,均方根误差(RMSE)为0.2 g/m^(3)。验证表明,该经验公式反演的液态水含量与飞机实测的LWC吻合较好,且在大多数情况下都优于已有经验公式反演的结果。展开更多
基金supported by the National Key Program for Developing Basic Sciences under Grant 2012CB417202the National Natural Science Foundation of China under Grant Nos. 40975014, 41030962 and 41175038sponsored by the Program for Postgraduates Research Innovation of Jiangsu Higher Education Institutions (Grant No. CXZZ11-0615)
文摘Cloud properties were investigated based on aircraft and cloud radar co-observation conducted at Yitong, Jilin, Northeast China. The aircraft provided in situ measurements of cloud droplet size distribution, while the millimeter-wavelength cloud radar vertically scanned the same cloud that the aircraft penetrated. The reflectivity factor calculated from aircraft measurements was compared in detail with sinmltaneous radar observations. The results showed that the two reflectivities were comparable in warm clouds, but in ice cloud there were more differences, which were probably associated with the occurrence of liquid water. The acceptable agreement between reflectivities obtained in water cloud confirmed that it is feasible to derive cloud properties by using aircraft data, and hence for cloud radar to remotely sense cloud properties. Based on the dataset collected in warm clouds, the threshold of reflectivity to diagnose drizzle and cloud particles was studied by analyses of the probability distribution function of reflectivity from cloud particles and drizzle drops. The relationship between refiectivity factor (Z) and cloud liquid water content (LWC) was also derived from data on both cloud particles and drizzle. In comparison with cloud droplets, the relationship for drizzle was blurred by many scatter points and thus was less evident. However, these scatters could be partly removed by filtering out the drop size distribution with a large ratio of reflectivity and large extinction coefficient but small effective radius. Empirical relationships of Z-LWC for both cloud particles and drizzle could then be derived.