基于Ka波段毫米波云雷达多普勒谱的降雪微物理过程的分析

云中过冷水滴的识别对于预警飞机积冰及云-降水物理研究具有重要意义。本文利用美国ARM-AMF2在芬兰的35GHz测云雷达多普勒谱数据，建立了谱峰识别算法，对全局谱进行了谱分离，识别出了过冷水滴然后，经过谱矩计算得到不同类型粒子的反射率因子、多普勒速度、谱宽，最后根据经验关系反演云中液态水含量，并与微波辐射计探测结果进行对比。结果表明：(1)混合云中，雪花主导了毫米波雷达总回波强度，因此根据总雷达反射率因子反演液态水含量会造成低估；(2)冰雪晶粒子在过冷水层(SWL)中多普勒速度随反射率因子的变化梯度比在冰雪层(ISL)中大；(3)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。

Analysis of snow microphysical process from Doppler spectra of the Ka-band millimeter-wave cloud radar

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. ARM-AMF2 spectral data of the 35GHz 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.