构建了一种基于毫米波云雷达多普勒谱的过冷水滴、冰晶、雪花的识别算法, 通过对全局谱的谱峰识别, 分离出了不同类型粒子的局部谱, 得出了不同类型粒子的反射率因子、多普勒速度、谱宽等谱矩参数及含水量.通过对一次降雪过程Ka波段测云雷达多普勒谱的分析, 结果表明: (1)混合相云中, 由于雪花对毫米波雷达总回波强度贡献较大, 基于总雷达反射率因子直接反演液态水含量会忽略过冷水滴的贡献, 造成云中含水量的低估; (2)多普勒谱反演得到过冷水的液态水路径(LWP)与微波辐射计反演结果一致性较好, 说明毫米波雷达能够有效估量云中液态水路径; (3)冰雪晶粒子在过冷水层(SWL)中下落速度随反射率因子的变化梯度(dV/dZ)比在冰雪层(ISL)中大,这主要是因为冰雪晶粒子在SWL中通过凇附增长比在ISL中通过碰并增长要增长得更快.

The identification of supercooled water droplets in the cloud is of great significance for the physical process of cloud-precipitation and warning aircraft icing. In this paper, the spectral peak recognition algorithm is established by the U.S. ARM1-AMF2 spectral data of the 35 GHz cloud radar in Finland. The spectral separation of the total spectrum is obtained, and then the supercooled water droplets are identified. Next the supercooled water droplets are identified. Next, the reflectivity, doppler velocity and spectral width of different types of particles are calculated by spectral moment. Finally, the liquid water content in the cloud is retrieved from the empirical relationship and compared with the detection results of the microwave radiometer. The results are as follows (1)The radar reflectivity factor of mixed-phase clouds mainly depends on snow. Therefore, it is considered that the effective volume of radar is snow. The cloud liquid water content will be underestimated according to the total reflectivity.(2) The gradient of Doppler velocity(V) of ice and snow particles in the supercooled water layer(SWL) larger than that in the ice and snow layer(ISL). (3) The liquid path(LWP) obtained by the Doppler spectrum is good agreement with the microwave radiometer. It shows that the millimeter wave radar can effectively estimate the liquid water path in the cloud.